Glossary

Got lost in the jargon? Browse our glossary and get familiar with key industry terms.

An open protocol, introduced by Google in 2025, that lets AI agents built on different frameworks discover each other’s capabilities and exchange tasks directly. It solves a different problem than the Model Context Protocol: MCP connects an agent to tools and data, while A2A connects one agent to another.

Adaptive Project Framework (APF), or Adaptive Project Management (APM), is a project management and software development methodology rooted in adaptive planning, iterative development, and ongoing enhancement. Unlike more traditional methods, the distinctive feature of adaptive is a fixed timeline established during the scoping and planning cycles – if a deadline for a deliverable isn’t met, it is put aside and re-prioritized later.

The lifecycle framework applied when the system being built is itself agentic: where agents reason, decide, and act as part of the product’s core behavior, rather than assisting with how the product gets built. Distinct from SDLC, which governs delivery process rather than agentic product behavior.

An Agent delivers complex results doing multiple rounds in the observe-think-act loop while leveraging LLMs to plan, execute actions, observe results and adapt its approach and Tool calling to get more data into the loop.

The practice of surrounding an agent with automated checks like type checkers, test suites, linters and pre-commit hooks that allow AI agents to detect and correct their own mistakes without human intervention, sometimes implemented using tools like LangGraph.

A methodology for measuring agent reliability by running the agent repeatedly against a fixed set of tasks with known correct outcomes, tracking pass rates across runs rather than judging single outputs — necessary because non-determinism means a single successful run proves nothing; the primary tool for catching regressions when upgrading models, changing prompts, or modifying tools.

A code library for building agentic systems (LangGraph, CrewAI, Microsoft AutoGen, OpenAI Agents SDK) that provides the building blocks of a harness: agent loops, tool wiring, state graphs, hand-offs between sub-agents and human-in-the-loop checkpoints.

The orchestration layer around an agent that constructs context, assembles the system prompt, reads the AGENTS.md, executes tool calls, enforces guardrails and decides when to loop — sort of an operating system for the agent.

A liveness detection mechanism where an agent periodically updates a timestamp in a shared database, allowing a monitoring system to detect crashed agents and automatically reassign their uncompleted work.

Collecting traces, token costs, latencies and outcomes from production agents into dashboards and alerts (LangSmith, Langfuse, Braintrust), giving agentic systems the operational visibility.

The managed infrastructure layer that runs agents in production, combining orchestration, persistent memory, session state, observability, identity and monitoring in one system what the harness is to a single agent, the platform is to a fleet of them. Notch built Sovera AI for this

Instruction sets that an agent lazy-loads into its context window only when it determines they are relevant to the current task — the opposite of MCP.

The complete recorded sequence of an agent’s run, including every prompt, reasoning step, tool call and result, used to debug failures, audit behaviour and build eval datasets from production incidents.

Labeling an ordinary chatbot, script, or workflow tool “agentic” without giving it the ability to plan, decide, or act on its own. The term has spread as more vendors compete for AI budget by relabeling existing products. Buyers can test for it by asking what decision the system actually makes without a human writing the rule first.

See also: Agentic AI, Agent

Amazon Web Services, a cloud computing platform, provides a comprehensive suite of services for seamless application development and deployment.

Microsoft’s comprehensive cloud computing platform offers a suite of services, from virtual computing to analytics. It provides scalable solutions for data storage, analytics, and virtual computing, empowering businesses to innovate and scale efficiently.

The digital powerhouse behind websites and applications, handling databases, server logic, and data processing, ensuring functionality and data management. While users interact with the front-end, the back-end works tirelessly, managing the entire web experience.

A product-centric list of features, improvements, technical debt tasks, issues and other product-related work items to be planned, refined, and for many ultimately implemented. Plans and priorities are reflected in it through Product Backlog Item (PBI) ordering. Anyone can add PBIs to it, but only the Product Owner, or The Customer can do removal and ordering. Items from the top of that list are supposed to be quite well refined and ready to be implemented, but as you go down the detail level and readiness for implementation drops.

The algorithm that efficiently computes the gradients needed for gradient descent by propagating the loss signal backwards through every layer of the network using the chain rule of calculus, making it practical to train models with billions of parameters.

One of the three pillars of NotchForge™. The discovery and scoping stage that maps how a business’s goals, data, and required interactions fit together before any architecture decisions are made.

A standardised test suite with known answers, such as MMLU for knowledge, SWE-bench for coding and GPQA for science, used to compare models on equal footing; useful for headlines, but no substitute for evals on your own tasks.

BPM integration links Business Process Management systems with different tools, promoting communication and streamlined workflows. It simplifies processes by connecting various applications, ensuring efficient flow of information across the organisation.

Business Process Model and Notation is a visual language simplifying the representation of business processes. Employing standardised symbols facilitates clear communication and analysis of workflows. Used widely, BPMN streamlines process mapping, fostering efficiency and collaboration in organisations by providing a universal framework for process visualisation.

A professional who collaborates with SMEs, guiding the team and strategically involving SMEs in new software presentations. Business analysts interpret data for efficient decision-making, ensuring alignment between business objectives and technological solutions.

An innovative open-source platform for workflow automation, orchestrating business processes with agility. Utilising BPMN (Business Process Model and Notation) standards, Camunda empowers organisations to streamline operations, enhance efficiency, and adapt to dynamic business landscapes through customizable and scalable workflow solutions.

A data integration technique tracking and capturing changes in a database. CDC ensures real-time synchronisation between data sources, facilitating efficient updates and analysis. This method is pivotal for maintaining accuracy and currency in data-driven environments.

A saved snapshot of a model’s weights and optimiser state at a specific point during training, used to resume interrupted training runs, compare intermediate model quality, or roll back to an earlier version if a later stage degrades performance.

Chunking in software development is the practice of breaking down large datasets, blocks of information, or computational tasks into smaller, more manageable pieces called “chunks”. Each chunk can then be processed, transferred, or stored independently, and reassembled later as needed. It is used for memory management, parallel processing, scalability, error isolation, resource optimization or optimized data transfer. Uses include data storage/transmission, big data and AI, text, image and audio processing, and memory management in programming.

Chunking in artificial intelligence is the technique of breaking down large pieces of data—such as text, images, or audio—into smaller, more manageable units called chunks. This is a crucial step in various AI applications, particularly those involving natural language processing (NLP) and large language models (LLMs), as well as RAG systems. The goal is to improve processing efficiency, enhance the accuracy of information retrieval, and optimize model performance.

NLTK, spaCy, Hugging Face Transformers, and LangChain offer flexible options for splitting text into meaningful segments for NLP tasks. It is important to select chunk sizes based on the model’s context window and the specific task. Each chunk should be able to stand alone semantically to ensure clarity. For better relevance, content-aware chunking is preferred over fixed-size splitting, and using overlapping chunks helps retain context when continuity is important.

Anthropic’s official CLI coding agent that runs in the terminal with direct filesystem, shell and git access, guided by CLAUDE.md for project-specific instructions and extensible via hooks and MCP servers.

Extensions that connect Claude to third-party services and tools — such as web search, databases or internal APIs — expanding what the model can access and act on beyond its base capabilities, analogous to how MCP servers extend agentic workflows.

A Claude-specific variant of AGENTS.md which is automatically added to context and includes persistent, project-specific instructions, giving the agent immediate context about the codebase’s architecture, build commands, testing procedures and coding conventions.

Cloud computing represents a comprehensive suite of computing services delivered over the internet, encompassing storage, processing power, and applications. Unlike traditional models relying on local servers or personal devices for computing tasks, cloud computing enables users to access and leverage these resources from remote servers, often administered by third-party providers. Cloud computing offers scalability, flexibility, and cost efficiency as users pay for the resources they consume, allowing organisations to leverage computing capabilities without the need for significant upfront investments in infrastructure.

A “garbage collection” strategy that removes older messages from an agent’s conversation to free space in the context window, usually dangerous for hallucinations and mitigated by pinning critical allocations (system prompt, specs, .md files), spawning sub-agents for fresh contexts, or Ralph loops with persistence in between.

An agent capability where the model operates a real graphical interface by reading screenshots, moving the mouse and typing instead of calling APIs, enabling automation of software that has no programmatic interface at the cost of speed and reliability.

Units of software that bundle applications and their dependencies. Containers enhance scalability, efficiency, and ease of management, providing a flexible solution for seamless deployment and execution of software in various computing environments.

The context window is analogous to RAM and we design, manage and optimise the information in it to optimise the reliability and quality of model output — with allocations, deallocations, caring for fixed allocations, memory isolation via sub-agents and context budgeting.

The degradation of model attention and output quality as the context window fills up, where instructions from early in a long conversation get diluted or ignored; the reason compaction, sub-agents and disciplined context engineering exist.

A fixed-size buffer of tokens that an LLM can “see” at once, including everything the model reads (the system prompt, conversation history, tool definitions, and tool results) plus everything it writes (the response text and any tool calls).

AI’s memory span during a task, or the amount of text an AI can remember at once. If the input is too long, older parts may be forgotten.

A software development approach ensuring rapid and reliable application releases. CD automates the entire process, from code integration to deployment, fostering a continuous and incremental delivery model.

A dynamic approach fostering ongoing enhancements in processes, products, or services. CI empowers teams to systematically identify, assess, and implement refinements, fostering a culture of innovation and adaptability to drive sustained progress and excellence.

One of the three pillars of NotchForge™. The governed structure (main agent, subagents, tools, and reference material) within which coding agents operate on an engagement, scoped narrowly enough to keep behavior predictable and auditable.

An AI-native IDE forked from VS Code that embeds model-powered code completion, multi-file editing and chat directly in the editor, allowing the agent to read the entire codebase as context and apply diffs across multiple files in a single operation.

Custom software development is the process of building tailored web and mobile software that meets detailed customer requirements. In contrast to commercially available off-the-shelf (COTS) software, custom solutions are typically designed to address specific customer challenges. They are primarily employed in-house by customers and are not intended for resale.

Customer Identity and Access Management (CIAM) encompasses the tech infrastructure facilitating digital interactions between businesses and their users. This includes user processes such as sign-up, sign-in, application access, web portals, and digital services provided by the organisation. CIAM functions as the digital identity layer integrated into customer-facing applications, empowering users to oversee and manage their preferences and privacy settings.

A cutting-edge end-to-end testing framework for web apps, offering developer-friendly automation with real-time debugging. Emphasising reliability, speed, and simplicity, Cypress empowers teams to ensure robust web application performance.

In Scrum, “D.E.E.P.” is an acronym that describes the qualities of a Product Backlog:

Detailed Appropriately: Items are described in enough detail to provide clarity, especially for higher priority tasks.
Estimated: Each item has an estimated effort required for its completion.
Emergent: The backlog is dynamic and evolves over time based on new information, feedback, and changing requirements.
Prioritized: Items are ranked by importance and urgency, ensuring that the most valuable and urgent tasks are completed first.

Dark code is AI-generated code that works and passes tests, but that no engineer on the team fully understands or can explain. Accumulates when agent output isn’t reviewed at the same depth as human-written code, and becomes a liability the first time it needs to be modified.

Data drift represents change in the statistical properties or distribution of data over time, especially in the context of machine learning and data-driven applications. When the data used in production becomes different from the data a model was originally trained on, the model’s performance and accuracy can decline.

Key Points:

Happens when real-world data changes – for example, due to evolving user behavior, seasonal trends, or modifications in data collection methods.
Can be gradual, sudden, recurring, or unpredictable.
Often impacts machine learning models by making their predictions less reliable if not properly monitored and managed.
Monitoring and detecting data drift is vital to keep models accurate and relevant.

Data science is a multidisciplinary field developed in the late 20th century to help people and organisations get actionable insights and information from structured and unstructured data. It converges statistics, data analysis, machine learning, and other methods to help understand data.

An organization’s ability to control where its data lives, who can access it, and which laws govern it, regardless of which cloud or model vendor is involved. It differs from Sovereign AI, which is a nation’s capacity to build and run AI infrastructure. For enterprise buyers in regulated industries and the EU, data sovereignty decides whether a given model or hosting setup is usable at all.

In software development, dedicated teams consist of developers dedicated exclusively to a particular software project or a defined set of tasks. Organisations typically engage such teams when their emphasis is on the exclusive advancement, maintenance, and enhancement of software applications or systems.

Design patterns in software development are proven solutions to recurring design challenges, enhancing code organisation and scalability. They embody best practices, empowering developers to create efficient and maintainable software architectures.

A collaborative and innovative workshop that brings together cross-functional teams to tackle complex problems and create solutions. This structured process, often lasting a week, emphasises rapid prototyping and user testing, fostering efficient problem-solving and informed decision-making in product development.

A technical approach that integrates software development and IT operations. This methodology aims to streamline the entire software development lifecycle, fostering continuous integration, delivery, and feedback loops for accelerated and reliable software deployment.

Digital product development is the end-to-end process of designing, building, and maintaining software solutions using agile methodologies for flexibility and alignment with customer and user needs.

Digital transformation solutions involve integrating digital technology into all business areas to simplify and streamline processes. Such solutions entail automating business processes and result in reduced manual work and fewer human errors. They include software development, cloud computing, data analytics, AI, cybersecurity, and more.

An innovative, collaborative session where cross-functional teams come together to explore and define project goals and solutions.

A training technique in which a smaller “student” model is trained to mimic the output distribution of a larger “teacher” foundation model, producing a compact model that retains much of the teacher’s capability at a fraction of the inference cost.

A storage solution for messages that can’t be processed in a messaging system. It serves as a holding area, enabling developers to investigate and address issues, ensuring message integrity in distributed systems.

An agile framework that emphasises collaboration, adaptability, and iterative development. It ensures flexibility by involving users and responding to changing requirements for timely and effective delivery.

How AI measures meaning: AI turns words or images into numbers to compare them, figure out context, and group related content based on meaning.

Enterprise app modernization refers to updating legacy software to better suit the current computing infrastructure, frameworks, platforms, computing languages, and even new employees. It helps enterprises avoid the accumulating costs of maintaining old software while also improving existing processes and avoiding risking business continuity.

Enterprise architecture (EA) is an extensive model that defines an organisation down to very granular details. It encompasses the organisation’s processes, technology infrastructure, information systems, and employees. Enterprise architecture is often used to help organisations achieve various large-scale objectives, such as successful digital transformation.

Enterprise-grade custom software is software designed to address the specific business requirements of an organisation. What sets it apart from non custom software is that it needs to meet the specific criteria set by the organisation so it can easily be integrated into the company’s infrastructure and used by its employees.

Two ways to keep a person in control of an AI agent. Approval models require sign-off on every action, which turns speed back into a bottleneck. Escalation models let agents carry routine volume and route only exceptions to a person. Research across 51 enterprise deployments found escalation models delivered a 71% median productivity gain, against 30% for approval models (Stanford Digital Economy Lab, 2026).

The European Union’s risk-based AI regulation, phasing in from 2025 to 2027, that classifies systems by risk level and imposes transparency, documentation and human-oversight duties on providers and deployers of high-risk and general-purpose AI; the regulatory backbone of AI governance in Europe.

A mode in which the model reasons through a problem in a scratchpad before producing its final response, trading higher token cost and latency for substantially improved accuracy on complex multi-step tasks; exposed as a controllable API parameter in Claude and OpenAI o-series models.

Extreme Programming (XP) stands as an agile software development methodology highlighting collaboration, flexibility, and customer satisfaction. It places a strong emphasis on frequent releases, continuous feedback, and adaptive planning, with core practices like pair programming and continuous integration ensuring a responsive and iterative approach. This methodology fosters efficient development and the delivery of high-quality software.

Feature-driven development (FDD) is an agile methodology framework designed to help developers working on large-scale projects. FDD entails teams breaking down a large project into smaller and more manageable fragments to deliver software with features that meet the client’s requirements.

Feature Creep refers to the gradual addition of additional features beyond a project’s original scope that can dilute the original objectives and functionalities of a project. This often results in increased complexity, delays, and sometimes even project failure. Feature creep typically occurs when stakeholders or team members propose enhancements or modifications that were not part of the initial requirements.

Common reasons for feature creep include user feedback requesting new functionalities, input from stakeholders leading to demands for additional features, or pressure to keep up with competitors.

Feature creep can result in increased development time, budget overruns, and compromised quality.

Steps to manage feature creep include establishing clearly-defined project goals, implementing prioritization frameworks, and taking a structured approach towards approving changes to the project scope.

A prompt engineering technique where examples of the desired input-output format are included directly in the prompt, allowing the model to infer the task pattern from those demonstrations and apply it to new inputs without any fine-tuning.

Continued training of a pre-trained model on a smaller, curated dataset to adapt its behaviour to a specific task, domain or style, updating the weights with a much lower learning rate to avoid erasing the general knowledge acquired during pre-training.

A way to enforce sequences of operations in an agent loop by modelling which transitions are legal and preventing the system from entering invalid states — like executing a tool while already executing one, or producing a response before processing a tool result, primarily implemented as software code to maintain determinism.

Flutter, developed by Google, is a freely available UI software development kit. It enables the compilation of applications for mobile, web, and desktop platforms from a unified codebase. Utilizing the Dart programming language, Flutter provides a wide range of pre-designed widgets for crafting visually appealing and responsive user interfaces.

Unlike steering messages, these are user messages queued and delivered after the agent completes all of its tool calls and produces a response, then triggering a new turn.

A Forward Deployed Engineer (FDE) is a customer-facing software engineer who works closely with a client to design, build, integrate, and deploy technical solutions in their real operating environment. FDEs combine hands-on engineering with customer collaboration, bridging the gap between a product’s standard capabilities and the client’s specific business, data, and infrastructure needs.

A large model trained on broad data through pre-training that serves as a general-purpose model able to do image generation, text summary or coding; GPT-4, Claude and Gemini are all foundation models.

Frontend refers to the user interface and experience of a software application or website. It encompasses the design, layout, and interactive elements that users interact with directly. Frontend development involves creating visually appealing and intuitive interfaces using technologies like HTML, CSS, and JavaScript.

The most capable AI models at the current edge of what is technically achievable — a moving bar used in policy and safety discourse to identify which systems warrant the highest scrutiny; today’s frontier models include GPT-4o, Claude Opus and Gemini Ultra.

Full-stack development involves working on all aspects of applications, most commonly, front-end and back-end components of software applications. A full-stack developer is proficient in different technologies and can handle tasks ranging from developing UI, implementing business logic, managing databases, or handling deployments. Usually, full-stack developers are T-shaped software engineers.

A class of AI systems trained to produce new content — text, images, audio, video or code — by learning the statistical patterns of their training data, as opposed to discriminative models that only classify or predict; large language models are the most prominent example.

A family of decoder-only language models built on the Transformer architecture, trained on large text corpora using a simple objective: predict the next token in a sequence; the same architecture used by Claude, Gemini and most major language models today.

GitHub is a web-based platform that uses the Git version control system. It is a collaborative environment for software development, allowing developers to host and manage code repositories of their own or of organizations. GitHub has many features like issue tracking, pull requests, project management tools, and CI/CD tooling.

The Global Data Synchronization Network (GDSN) is an internet-based, interconnected network of interoperable data pools governed by GS1 standards that enables companies around the globe to exchange standardized product master data with their trading partners in real-time. GDSN serves as the world’s largest product data network, facilitating seamless sharing of high-quality product information across global supply chains.

How GDSN Works

The system operates using a publish-subscribe model between trading partners, each accessing the network through certified data pools connected by the GS1 Global Registry. The process follows five key steps: suppliers upload product information to source data pools, data pools register information with the global registry, recipients subscribe to receive specific product data, source pools publish requested information to recipient pools, and recipients send confirmations back through the network.

Go, formerly known as Golang, is a statically typed programming language developed by Google. It is straightforward, efficient, and has very good and process-wise cheap concurrency support. Go is primarily a first-pick language in case you are developing anything in system programming, cloud services, or scalable web applications.

A cutting-edge cloud computing service by Google, offering a comprehensive suite of scalable and secure solutions for storage, computing, and data analytics. From infrastructure to machine learning, GCP empowers businesses with flexible tools to innovate and thrive in the digital landscape.

OpenAI’s cloud-based agentic coding system that runs tasks in a sandboxed environment — executing terminal commands, reading and writing files and submitting pull requests — positioned as a direct cloud-native competitor to Claude Code.

The iterative optimisation algorithm used to train neural networks by computing the gradient of the loss function with respect to every weight — via backpropagation — and nudging each weight in the direction that reduces the loss by a step size set by the learning rate.

The practice of anchoring a model’s outputs to verifiable, external sources of truth — via RAG, tool calling, citations or structured data — to reduce hallucination and make responses auditable.

Input and output validation layers placed around a language model that detect and block unsafe, off-topic or policy-violating content before it reaches the model or the user, acting as a complement to RLHF alignment and defending against prompt injection attacks.

Making things up, or AI’s occasional answering in a manner that only seems right, but is actually false, due to missing or faulty data.

HashiCorp is a firm specializing in the creation of infrastructure automation software. Among their primary tools are Terraform, used for defining and provisioning infrastructure through code; Consul, a solution for service networking that facilitates secure connections between various services; and Vault, a tool designed for managing secrets and safeguarding sensitive data.

Shell commands configured in an agent’s settings to execute automatically before or after specific events — such as a tool call or file edit — used for validation, linting, test running and other forms of backpressure without modifying the agent’s own code.

A design pattern where the agent pauses at defined checkpoints (destructive actions, plan approval, external communications) and waits for human sign-off before proceeding, trading autonomy for safety on consequential operations; the runtime complement to guardrails and plan mode.

One of the three pillars of NotchForge™. The Human-in-the-Loop (HITL) accountability model in which engineers remain responsible for architecture, domain logic, and every commit, regardless of how much of the implementation an agent produced.

Any configuration value that governs the training process itself — such as learning rate, batch size, number of layers, or context length — rather than a value learned from data; tuning hyperparameters is a major part of the model development workflow.

The process of running a trained model on new inputs to produce predictions, without updating weights — the model is stateless between requests and uses only the context window as memory; quantization is a common technique to make inference faster and cheaper.

Infrastructure deployment refers to setting up or upgrading existing hardware, software, networks, and other necessary technology components that are needed to run an IT system or an application successfully. It is a vital step in ensuring IT solutions work and deliver as intended.

The Internet of Things (IoT) is a common name for a network of interconnected devices that communicate and share data over the Internet. These devices, which can range from sensors and actuators to everyday objects such as household appliances, enable data collection, automation, and creation of smart systems.

Java is a high-level, object-oriented programming language. Java is also platform-independent, allowing applications to run on any device with the JVM (Java Virtual Machine). It has been widely used for almost three decades for the development of enterprise-level applications, mobile apps, or web applications.

JavaScript is a flexible scripting language widely employed in web development. It empowers developers to craft dynamic, interactive user interfaces on websites. When utilized as the frontend language, JavaScript operates on the client side, facilitating the manipulation of webpage content and user interactions.

Kanban, as an agile methodology, visually tracks the progression of work through a system using boards and cards to represent tasks and their respective statuses. Emphasizing continuous delivery and optimizing workflow enables teams to adapt efficiently to changes.

Kotlin, characterized by static typing, prioritizes conciseness, expressiveness, and interoperability with existing Java code. This language incorporates functional programming elements, extension functions, and a variety of contemporary language features. Notably popular in Android development, Kotlin is recognized for its modern syntax, minimized boilerplate code, and improved null safety features.

Kubernetes, an open-source container orchestration platform, streamlines the deployment, scaling, and administration of containerized applications. It offers a framework for deploying and managing applications in a scalable and robust fashion, simplifying the management of extensive containerized workloads.

The key-value cache storing intermediate attention computations during inference, reused across tokens so the model doesn’t reprocess the full context on every generation step; the underlying mechanism that makes prompt caching economical.

A Large Language Model (LLM) is a type of artificial intelligence algorithm that uses deep learning techniques and large datasets to understand, summarize, generate, and predict new content. These sophisticated AI systems are trained on vast amounts of text data using self-supervised machine learning, designed specifically for natural language processing tasks, especially language generation.

Common Use Cases

Automated code generation and development assistance: Generate complete code snippets, functions, and programs from natural language descriptions, while providing intelligent code completion and automated documentation creation that dramatically reduces development time.
Enterprise AI chatbots and customer support systems: Deploy 24/7 intelligent customer service solutions that handle complex inquiries, provide personalized assistance, and seamlessly integrate with existing business systems to improve customer satisfaction.
Business intelligence and data analysis automation: Transform raw business data into actionable insights through automated report generation, predictive analytics, and intelligent document processing that supports strategic decision-making.
Content creation and marketing automation: Generate high-quality marketing materials, product descriptions, technical documentation, and personalized communications at scale while maintaining brand consistency and quality standards
Legacy system modernization and technical debt management: Analyze and understand complex legacy codebases, provide migration strategies, and assist in refactoring outdated systems to modern architectures with reduced risk and faster implementation

Key Benefits

Accelerated time-to-market and development efficiency: Reduce development cycles through automated code generation and intelligent assistance, enabling faster project delivery and competitive advantage in rapidly evolving markets.
Measurable ROI and cost optimization: Deliver higher returns on investment through process automation, reduced manual labor costs, and improved operational efficiency while providing transparent, predictable pricing models based on actual usage.
Enhanced quality and risk mitigation: Implement automated testing, code review, and quality assurance processes that identify issues early, reducing post-deployment bugs, maintenance costs, and project risks.
Scalable innovation and competitive differentiation: Build AI-powered solutions that grow with business needs without proportional resource increases, positioning organizations as technology leaders and creating new revenue opportunities.
Strategic resource optimization: Enable skilled development teams to focus on high-value strategic work by automating repetitive tasks, while providing flexible implementation approaches that start with proof-of-concepts to minimize financial risk.

Lean is a production and management philosophy derived from Toyota’s manufacturing practices. It emphasizes minimizing waste, optimizing efficiency, and continuously improving processes. In software development, Lean principles are applied to streamline workflows and deliver value to customers more efficiently.

Collaborative, learn-by-doing mode where Claude will not only share “Insights” while coding, but also ask you to contribute small, strategic pieces of code yourself, by adding TODO(human) markers in your code for you to implement.

A hyperparameter controlling how large a step gradient descent takes when updating weights; too high and training diverges or overshoots minimal, too low and training stalls or converges to a poor solution.

Legacy app modernization refers to updating obsolete and dated systems to better serve an organization in the future.

The goal of app modernization is to ensure that applications are up-to-date and compatible with modern objectives, enabling scalability, higher performance, and improved functionality. By modernizing, organizations can eliminate some of the risks associated with outdated technology.

Organizations with outdated applications should consider app modernization in order to:

Improve compatibility with new technologies
Enhance scalability and performance
Align with emerging trends
Take advantage of cloud-based software and infrastructure.

Modernization involves several stages, beginning with assessing full documentation to evaluate how the code base, architecture, APIs and other elements are cooperating. This is followed by checking error logs for any application stability issues. Technical evaluation concludes with assessing how applications communicate with other systems and workflows.

The benefits of app modernization include improved efficiency and performance, better UX, reduced cost, increased scalability, and alignment with current technologies and trends.

To understand more, read our complete guide – Understanding Legacy Application Modernization.

Less (Large-Scale Scrum) is an agile framework for scaling Scrum to larger organizations. It optimizes collaboration and simplifies project management by breaking down large-scale developments into smaller, manageable units. With a focus on flexibility and adaptability, Less promotes efficient and transparent practices in complex environments.

A parameter-efficient fine-tuning technique that freezes the original model weights and trains only small low-rank adapter matrices inserted into each layer, drastically reducing the memory and compute needed to specialise a large model for a new task.

A mathematical function that measures how wrong a model’s predictions are compared to the correct answers during training; the optimisation process uses gradient descent to adjust the weights in the direction that minimises this value across the training dataset.

The lifecycle framework applied to building and maintaining custom AI models, covering the build-vs-buy decision, data preparation, model selection or fine-tuning, and ongoing accuracy monitoring after deployment. Fine-tuning is one stage within it, not the whole lifecycle.

Persistent storage external to the context window — files, databases, vector stores — that agents read and write across turns and sessions to maintain state beyond a single context window; distinct from RAG in that it is agent-owned and actively written rather than retrieved from a static knowledge base.

A Minimal Viable Product (MVP) refers to a version of a new product containing the most basic features required to test and validate assumptions, as well as to gather user feedback. MVP comes from the lean startup methodology and is meant to optimize the time and resources spent on developing a product, while gaining actual feedback about real user needs.

Commonly used in software development, an MVP is not a final product, but a starting point that allows teams to validate product ideas based on real user feedback.

The cheapest model that can do a specific job well. Chosen agent by agent, instead of routing every task to the most powerful, most expensive option. Pairing a Minimum Viable Model with model routing keeps AI running costs predictable as usage scales.

A Transformer variant where each layer contains many parallel “expert” sub-networks and a router activates only a few per token, letting models grow to enormous total parameter counts while keeping inference cost close to that of a much smaller model; used by Mixtral, DeepSeek and most frontier models.

MLOps (Machine Learning Operations) is a set of practices that helps organizations build, deploy, and manage machine learning models in real-world production environments. It combines principles from software development (DevOps) with the unique needs of machine learning projects, creating a framework that bridges the gap between data science teams and IT operations. MLOps addresses the entire machine learning lifecycle, from collecting and preparing data to training models, deploying them in production, and continuously monitoring their performance.

The main goal of MLOps is to automate and streamline the complex process of turning experimental machine learning models into reliable business solutions. This includes automating tasks like model training, testing, deployment, and monitoring to ensure models work consistently and accurately over time. MLOps also enables better collaboration between data scientists, engineers, and IT teams by providing standardized tools and workflows that everyone can use. Key features include:

Version control for models and data
Continuous integration and deployment (CI/CD) pipelines
Automated testing
Real-time performance monitoring

Organizations implement MLOps to solve common challenges like models losing accuracy over time (called “data drift”), difficulty scaling models across different environments, and the struggle to move models from experimental labs to production systems. The benefits include faster time-to-market for AI solutions, improved model reliability, reduced operational costs, and the ability to manage hundreds or thousands of models simultaneously. By adopting MLOps practices, companies can ensure their machine learning investments deliver consistent business value and adapt to changing data and business requirements.

Mobile development involves creating software designed specifically for devices such as smartphones and tablets. It spans planning, design, coding, testing, and deployment, usually both for iOS and Android operating systems.

Model Context Protocol (MCP) is a specification and framework for standardizing how AI models interact with context windows and external data sources. MCP provides a structured approach to:

1. Context management: Organizing and prioritizing information within limited context windows.

2. Memory systems: Standardizing how agents store and retrieve information across interactions.

3. Tool integration: Defining how models can invoke external tools and systems.

4. Data exchange: Establishing formats for passing information between components.

MCP operates through a client-server architecture where AI applications (hosts) connect to external services (servers) via standardized JSON-RPC 2.0 messaging. This eliminates the need for custom integrations between every AI model and data source, instead providing a universal interface that works across different AI systems and external tools.

Developed by Anthropic and rapidly adopted by major AI companies including OpenAI, Google DeepMind, and Replit in 2025, MCP has become the de facto standard for AI integrations. The protocol emphasizes security through explicit user consent for all data access and tool usage, while enabling AI models to work with live, real-time information rather than static training data.

Sending each request to the model best suited to handle it, instead of one model for every task. Routing simple requests to smaller, cheaper models and reserving frontier models for hard cases lets AI scale sublinearly with usage. Robinhood used model routing to grow from 500 million to 5 billion tokens a day while cutting AI costs by 80% (AWS, 2025).

Adjusting a general AI model using specialized data so it performs better on certain tasks, like industry-specific applications.

The risk of becoming dependent on a single model provider’s API, pricing and capabilities, mitigated by provider registries, abstraction layers and maintaining eval suites that can benchmark multiple models.

A single version-controlled repository containing multiple related projects or packages; in agentic development, monorepos give the agent unified cross-package context but require careful CLAUDE.md scoping and sub-agent isolation to prevent one task’s changes from cascading into unrelated parts of the codebase.

An architecture in which an orchestrator agent decomposes work and routes it to specialist sub-agents (a researcher, a coder, a reviewer), each with its own context window and tools, coordinating their results into a single outcome; the pattern behind swarm analysis and parallelization.

A model trained on multiple data modalities — such as text, images, audio or video — enabling it to understand and generate across those modalities within a single unified Transformer-based architecture, with each modality typically converted into tokens via a modality-specific encoder.

Natural Language Processing (NLP) represents a sophisticated artificial intelligence technology that empowers computers to understand, interpret, and generate human language in a meaningful and contextually aware manner. By combining computational linguistics with advanced machine learning techniques, NLP bridges the gap between natural human communication and structured digital data, enabling seamless interaction between people and machines.

NLP systems excel at parsing complex language structures, recognizing intent and sentiment, and extracting valuable insights from unstructured text and speech data. Modern NLP leverages cutting-edge deep learning architectures, including transformer models like BERT and GPT, which utilize self-attention mechanisms to capture intricate relationships between words and understand context across entire documents.

Organizations leverage NLP to automate critical business processes such as customer support through intelligent chatbots, document analysis for compliance and knowledge management, sentiment analysis of customer feedback, and real-time language translation for global operations. By transforming vast volumes of unstructured textual data into actionable business intelligence, NLP solutions drive operational efficiency, enhance customer experiences, and unlock valuable insights that inform strategic decision-making across industries ranging from healthcare and finance to legal services and e-commerce.

In software development, nearshore outsourcing is a strategic approach to software development and certain business operations, where development is outsourced to a 3rd party company located in a nearby geographical location. This geographical and cultural proximity, (typically within the same region or time zone) improves communication and collaboration between the business and the third-party developer, enabling successful execution of more intricate projects that demand heightened collaboration and faster delivery times.

A subset of machine learning inspired by the human brain’s structure, consisting of interconnected layers of artificial neurons (nodes) that process information, identify patterns, and learn from data by adjusting numerical weights through training; LLMs are neural networks of the Transformer architecture.

NgRx is a state management library designed for Angular applications, drawing inspiration from Redux principles. It aids in predicting the application state through critical concepts such as store, actions, reducers, and effects. Its integration with Angular seamlessly leverages reactive programming features using RxJS.

Node.js serves as a runtime environment enabling the execution of JavaScript code beyond web browsers. Primarily utilized for server-side development, it facilitates the use of JavaScript on both client and server ends. Node.js stands out for its event-driven, non-blocking I/O model, making it well-suited for constructing scalable and efficient network applications.

A digital identity issued to an AI agent, script, or service account rather than a person, so systems can tell which actions were taken by which non-human actor. As agents run continuously and touch more systems, NHIs are becoming as common as user accounts, and just as important to govern, log, and revoke.

See also: AI Governance, Guardrails

Notch’s greenfield delivery methodology for new products: Scope (2–3 weeks), Ship (8–12 weeks), Strengthen (ongoing). Designed to take an idea to a working MVP in around 12 weeks.

Notch’s engineering and delivery framework, built on three pillars: BDI™ (Business-Data-Interactions), Curated Harness, and Humans in the Co-op, that governs how software, agentic systems, and custom models are built regardless of which lifecycle applies. Guarantees predictability, transparency, compliance, and consistency across engagements.

Operating in the cloud, Okta is a service for identity and access management (IAM). It specializes in providing secure authentication, authorization, and user management for web and mobile applications. Okta is instrumental in helping organizations control access, manage user identities, and bolster security through features like single sign-on (SSO) and multi-factor authentication.

A tool for downloading and running open source models locally on consumer hardware that exposes an OpenAI-compatible API, making it straightforward to swap cloud models for local ones in any agentic workflow without sending data to external providers.

Running model inference on your own hardware or private cloud rather than calling an external API, eliminating third-party providers cost and data sharing.

Foundation models with public weights specifically trained or fine-tuned for code generation and understanding — such as DeepSeek Coder, Qwen Coder, Kimi, MiniMax, Code Llama and StarCoder — that can be run locally via Ollama, customised with LoRA on proprietary codebases, and used in agentic coding tools without API costs or data leaving the organisation.

An open-source terminal-based AI coding assistant built by SST, designed as an alternative to Claude Code with a TUI interface and support for multiple model providers including local open source models.

Outsourcing in the IT industry means hiring external firms to handle specific tech tasks like software development, IT support, and network management. This approach helps companies reduce costs, access specialized skills, and focus on their core activities while benefiting from the expertise and technologies of outside providers.

When a model learns the training data so precisely that it memorises noise rather than underlying patterns, causing strong performance on training data but poor generalisation to new, unseen examples; mitigated during fine-tuning by using smaller datasets carefully and regularisation techniques.

Running multiple sub-agents concurrently on independent tasks and merging their results, the primary way agentic systems beat wall-clock time on large workloads; requires careful isolation to prevent agents from overwriting each other’s work.

A structured mode in agentic coding tools like Claude Code where the agent explores the codebase and drafts a detailed implementation plan into the context window for human review and approval before writing or modifying any files, reducing wasted effort on large or ambiguous tasks.

Platform engineering combines reusable tools and self-service features with automated infrastructure management to improve developer productivity, autonomy and experience. This leads to higher-quality delivery, faster time to market, and increased business value.

Platforms are integrated frameworks supporting diverse applications and services, fostering scalability, connectivity, and innovation in the digital ecosystem.

Playwright, crafted by Microsoft, is a versatile Node.js library tailored for streamlining browser automation and testing across diverse web platforms. A unified interface for popular browsers like Chromium and Firefox empowers developers to automate tasks, simulate user interactions, and execute end-to-end testing effortlessly. Offering comprehensive features such as debugging and performance monitoring, Playwright serves as an indispensable tool for elevating the quality and efficiency of web applications.

The first and computationally expensive phase of model development in which a neural network is trained on massive unlabelled datasets — typically large portions of the web — to learn general statistical patterns in language by minimising a loss function before any task-specific adaptation.

An API-level optimisation that stores the KV state for a repeated context prefix — such as a system prompt or large codebase snapshot — so subsequent requests reuse it rather than reprocessing, cutting both latency and cost on long-context agentic tasks.

The art of crafting clear and targeted instructions that guide AI to give more accurate, relevant, and creative responses

An adversarial attack in which a user crafts input that overrides or manipulates a language model’s system prompt instructions, causing it to behave in unintended ways — a key security concern when building agents that process untrusted external content.

Proof of Concept (PoC) refers to a prototype intended to validate the feasibility and potential effectiveness of a specific idea. PoC is commonly used in software development to test whether a concept can be turned into a viable product or solution.

The purpose of a PoC is to confirm whether a product concept or idea has practical applications that can be successfully implemented.

A PoC consists of a concept overview describing the idea or technology being tested, an implementation plan that outlines how the concept will be tested, and a demonstration of a working model or prototype that proves the concept’s core functionality.

Developing a PoC reduces risk exposure by validating ideas before committing resources to development. A PoC advances development by winning stakeholder buy-in by proving a concept’s feasibility, and providing insights that guide further development.

Essentially a config file to decouple from the LLM provider and to be able to change models — for example from Anthropic to Mistral, including mid-conversation — enabling multi-model agents.

Recognized for its readability and versatility, Python is a high-level, interpreted programming language extensively employed in web development, data analysis, artificial intelligence, scientific computing, and various other domains. Python’s syntax is both clear and concise, and its status as an interpreted language makes it particularly suitable for beginners.

Systematic process ensuring products or services meet predefined standards through rigorous testing and continuous improvement. It fosters reliability, efficiency, and customer satisfaction by maintaining adherence to quality benchmarks.

A post-training compression technique that reduces the numerical precision of a model’s weights (e.g., from 32-bit floats to 4-bit integers) to shrink its memory footprint and speed up inference with minimal loss in output quality.

Quarkus, a Java framework tailored for GraalVM and OpenJDK HotSpot, is specifically crafted for Kubernetes environments. It strives to enhance Java applications for cloud-native and microservices architectures. Quarkus stands out with rapid startup times, minimal memory usage, and effective performance, rendering it well-suited for the development of lightweight and scalable microservices.

A pattern for iteration over perfection, where work is split into small iterations and each iteration runs in a fresh context window instead of keeping one long chat alive until it degrades — while true; do — used with backpressure and sub-agents.

React, also known as React.js, serves as a JavaScript library designed for constructing user interfaces. Created by Facebook, it empowers developers to craft reusable UI components that dynamically update in response to data changes. React is commonly utilized in combination with other libraries and frameworks to construct contemporary, interactive web applications.

React Redux is a library that connects React applications with the Redux state management. It acts as a bridge, allowing React components to interact with the global state in the Redux store. By facilitating a predictable and centralized approach to state management, React Redux simplifies the development of scalable and maintainable React applications. It’s widely used for maintaining a single source of truth for the application state.

“Thinking out loud”: AI breaks down its reasoning step by step, especially useful in solving problems that require logical thinking.

A class of models trained with reinforcement learning to produce long internal chains of reasoning before answering (OpenAI’s o-series, DeepSeek R1, Claude with extended thinking), trading tokens and latency for large gains on math, code and planning.

Rebuilding is an application modernization strategy that involves completely reconstructing an application from scratch while maintaining its original scope and specifications. This approach creates entirely new applications using modern technologies, frameworks, and development practices.

Some examples of rebuilding applications include a financial institution might rebuild its legacy COBOL-based loan processing system using modern programming languages like Java or Python, incorporating contemporary security standards and cloud-native design patterns. Another example includes rebuilding a desktop-based customer relationship management system as a modern web application with mobile capabilities.

Rebuilding offers several benefits, including the highest level of modernization and optimization potential, enabling organizations to leverage the latest technologies and best practices. This approach eliminates technical debt accumulated over years and creates applications that are fully optimized for current and future business needs. While requiring the highest investment in terms of time and resources, rebuilding often delivers the best long-term results for applications that are central to business operations.

Recursive self-improvement (RSI) in AI is a process in which an AI system improves its own capabilities or the tools, code, and methods used to build future versions of itself. By creating a feedback loop in which each improvement can make the next one easier, RSI could accelerate AI progress, though meaningful human oversight and evaluation remain essential.

The act of restructuring existing code without altering its external behavior is known as refactoring. The primary aim is to elevate code quality, readability, and maintainability. Developers leverage refactoring to improve code structure, address technical debt, and simplify the codebase for enhanced comprehension and maintenance.

Rehosting, commonly known as “lift and shift,” involves migrating applications to a new infrastructure environment without making significant changes to the application code or architecture. This strategy focuses on moving existing workloads from on-premises infrastructure to cloud platforms with minimal modifications.

Examples: Moving a web application running on physical servers to Amazon Web Services EC2 instances using AWS Server Migration Service, maintaining the same operating system and application configuration. Another example includes migrating an on-premises database to a cloud-based virtual machine while preserving the existing database structure and applications.

The benefits of rehosting include the fastest and most cost-effective migration paths with minimal business disruption. Organizations typically achieve immediate cost reductions through rehosting alone, while gaining access to cloud benefits like improved reliability and basic scalability. Rehosting requires minimal technical expertise and poses the lowest risk among all modernization strategies, making it an ideal starting point for cloud migration initiatives

Replatforming, also referred to as “lift, tinker, and shift,” involves migrating applications to new runtime platforms while making minimal code changes that don’t affect the core architecture. This strategy optimizes applications to take advantage of cloud benefits without complete rewrites.

Examples of replatforming include migrating a database from Oracle to Amazon RDS while making minor configuration adjustments to leverage cloud database features like automated backups and scaling. Another example could include moving a Java application to a containerized environment using Docker while optimizing for cloud-native services.

The benefits of replatforming include achieving an optimal balance between speed and optimization, delivering immediate cloud benefits with moderate investment. This approach can improve application performance, reduce operational overhead, and enable access to modern platform services while minimizing development effort. Organizations often see improved reliability and simplified maintenance as additional benefits of replatforming initiatives

Retrieval Augmented Generation (RAG) is an advanced artificial intelligence technique that combines information retrieval with text generation, allowing AI models to retrieve relevant information from external knowledge sources and incorporate it into generated responses. RAG represents a significant advancement in AI technology by addressing the limitations of traditional large language models (LLMs) that rely solely on their pre-trained data.

RAG is a hybrid framework that integrates retrieval models and generative models to produce text that is not only contextually accurate but also information-rich. Unlike traditional LLMs that respond based on static training data, RAG systems dynamically pull relevant information from databases, documents, or web sources to inform their responses. This approach enables AI models to access domain-specific and up-to-date information that may not be available in their original training data.

Key Benefits

RAG offers several significant advantages over traditional language models:

Enhanced accuracy and relevance: RAG models can access up-to-date information through retrieval, ensuring generated content is current and relevant.
Reduced hallucinations: By grounding responses in factual external data, RAG helps minimize AI hallucinations where models generate plausible but incorrect information.
Cost efficiency: RAG eliminates the need to retrain entire models when new information becomes available, saving computational and financial resources.
Transparency: Users can verify cited sources since RAG systems can include references to the retrieved information in their responses.
Domain expertise: RAG enables AI systems to work with specialized knowledge bases and proprietary company data.

A training technique used to align a language model’s outputs with human preferences: human raters compare model responses, a reward model is trained on those preferences, and the language model’s weights are updated using reinforcement learning to maximise that reward signal — the primary method behind fine-tuning Claude and ChatGPT for safe, helpful behaviour.

Running an agent’s tool calls inside an isolated environment such as a container, VM or permission-restricted shell, so that a hallucinated rm -rf or leaked credential cannot damage the host system; the infrastructure counterpart to backpressure.

Scala is a flexible programming language that integrates elements of both object-oriented and functional programming. Known for its expressiveness and conciseness, Scala is specifically engineered for excellent scalability. It boasts robust features such as powerful pattern matching, immutability, and support for both procedural and functional programming styles. Scala operates on the Java Virtual Machine (JVM), making it compatible with Java.

The empirical finding that model capability improves predictably as compute, data and parameters increase together, which justified the enormous investments in frontier models and still shapes the industry’s roadmap.

Scope creep refers to the gradual increase in a project’s scope beyond its original objectives. This happens when new features, tasks, or requirements are added after a project has already commenced, and often results in project delays, budget overruns, and poorer quality outcomes.

Common factors contributing to scope creep can include a lack of clear project goals, frequent stakeholder requests for additional features, or new market conditions forcing teams to meet new requirements.

Scope creep can cause delays thanks to additional tasks extending established project timelines, driving costs due to additional resources, and negatively impact the quality and effectiveness of core deliverables.

Managing scope creep by setting clear project documentation, change control processes, and maintaining regular communication ensures projects stay on track, within budget, and achieve the original objectives.

Scrum is an agile project management framework focusing on collaboration, adaptability, and iterative progress through time-boxed sprints. Key roles include Product Owner, Scrum Master, and Development Team, ensuring efficient teamwork and delivering incremental value.

SDK, or Software Development Kit, is a compilation of software tools and libraries employed by developers to build applications for specific platforms, hardware, or software frameworks. An SDK usually consists of tools such as compilers, debuggers, documentation, and sample code, serving as a comprehensive developer resource. The main purpose of SDKs is to facilitate development, offering pre-written code and quickly integrable tools for building environment-specific software.

Software development lifecycle (SDLC) refers to a software development methodology. SDLC has clearly defined processes that focus on software development phases, specifically: requirement analysis, planning, design, development, testing and deployment. The purpose of the SDLC methodology of software development is to produce the highest quality software possible in the shortest time and at the lowest cost.

Employees using unapproved AI tools with company data, like pasting source code into free chatbots or running unsanctioned agents, creating data leakage and compliance risks that AI governance policies and approved-tool catalogues are meant to contain.

Reusable markdown instruction files for Claude Code, stored in .claude/commands/ and invoked via slash commands, that are lazy-loaded into the context window only when relevant — packaging team workflows like deploy procedures, PR templates or code review checklists as on-demand context rather than permanent system prompt bloat.

Small language models (SLMs) are compact artificial intelligence models designed to process, understand, and generate natural language, similar to large language models (LLMs), but with significantly fewer parameters – typically ranging from a few million up to about 10 billion.

Key Characteristics

Parameter Size: SLMs generally have between 1 million and 10 billion parameters, compared to LLMs, which may have hundreds of billions or more.
Efficiency: Because of their smaller size, SLMs require much less computational power and memory, making them suitable for use on resource-constrained devices like smartphones, embedded systems, or edge devices.
Specialization: SLMs are often fine-tuned for specific tasks or domains and thus excel at targeted use cases such as domain-specific chatbots, summarization, or information retrieval.
Architecture: Like LLMs, small language models are built using transformer neural network architectures, and may use techniques like knowledge distillation, pruning, and quantization to reduce size while retaining effective functionality.

Benefits and Use Cases

Lower costs: Smaller models are less expensive to train, operate, and maintain.
On-device AI: Their compact footprint allows deployment on devices without relying on cloud resources or powerful infrastructure.
Environmental impact: SLMs consume less power and compute, making them “greener” than LLMs for suitable use cases.
Customization: Easier and cheaper to fine-tune for specialized applications.

Software development is the systematic process of designing, coding, testing, and deploying computer programs or applications. From conceptualization to maintenance, it aims to create efficient, scalable, and functional solutions that meet specific user and business requirements.

Software engineering refers to the discipline of computer science involved in the design, development, testing, and maintenance of software applications. Software engineers develop software solutions by using knowledge of engineering principles and computer programming languages.

Software quality engineering involves thorough quality checks at every stage of product development, using analysis, development, management, and quality assurance protocols. In software development, it means applying quality engineering practices throughout the entire software development lifecycle. Quality engineers are embedded in production teams to detect issues early.

Software system design (SSD) is a practice that translates business requirements into a concrete technical blueprint, sitting between requirements analysis and implementation in the SDLC. Specifies system architecture, data flow, component interactions, and technology stack to ensure the resulting system is scalable and reliable.

Notch’s agentic orchestration platform, built internally before being offered to clients. Sovera AI™ provides the infrastructure layer for agentic deployments, including memory, observability, data sovereignty, and recovery logic, particularly for regulated industries that cannot route data through third-party hyperscaler endpoints.

A nation’s or organization’s capacity to develop, own and operate AI infrastructure independently of foreign or external technology providers, while retaining strategic autonomy.

Spec-driven development (SDD) is an approach where a written specification becomes the primary artifact an engineer or AI agent builds from. Gaining traction in AI-native development because agents perform more reliably against an explicit, structured spec than against a vague natural-language request.

A java-based open-source platform that streamlines enterprise application development. With a modular architecture and features like dependency injection, it enhances scalability and maintainability. Spring framework supports aspect-oriented programming, fostering robust, loosely coupled software solutions.

A standalone component is an independent and self-contained element within a system, capable of functioning independently without reliance on external dependencies. It performs specific tasks or provides distinct functionalities, contributing modularity and ease of integration within diverse software architectures.

A user message injected into an agent’s conversation mid-run that interrupts remaining tool executions and redirects the agent’s behavior, typically between tool calls and in contrast with follow-up messages.

A technique where a language model sends its response incrementally as it is generated — token by token — rather than waiting for the complete output, allowing for early cancellation if the model is on a wrong path.

Constraining a model’s response to a strict schema — JSON, XML — so downstream code can parse it reliably without brittle string extraction; now a first-class API feature in most providers and essential for any agent that passes data between steps.

A child agent spawned by a parent agent into a fresh context window, enabling memory isolation for: bounded units of work, bounded tasks, irrelevant intermediate states and other cases.

At planning time before executing a large epic, swarm analysis goes through the graph of work items and groups them into waves — sets of tasks that share the same depth level and can be done by sub-agents concurrently, waiting for each other before the next wave begins.

Training data generated by a model rather than collected from humans, used to fill gaps in real datasets, train student models via distillation and create eval cases, with the risk of amplifying the generating model’s own blind spots.

A fixed allocation message placed as the first entry in an LLM’s context window defining the model’s behavior, personality, capabilities and constraints — treated with higher trust by most models.

Tech debt refers to the implied cost of rework needed when choosing a quick or easy solution over a better approach that would take longer. It arises when development teams prioritize speed of delivery over optimal code quality, often due to tight deadlines or immediate business needs. This accrues “interest” in the form of increased cost, inefficiency, risk, and customer dissatisfaction if not addressed.

Technical debt isn’t inherently bad and can be useful for progressing a project forward, such as with Minimum Viable Products (MVPs). However, it can become problematic if poorly managed, leading to extreme maintenance costs, reduced competitiveness, and increased downtime.

Tech debt, also called code debt or design debt, usually happens when organizations opt for quicker delivery over clean, efficient code. This can result in increased costs, inefficiencies, elevated risk, and customer dissatisfaction.

This can be because of deliberate, like when opting for a quick solution. In other cases, tech debt can be unintentional owing to lack of knowledge or changing standards.

Depending on the cause, tech debt can be classified further as architecture debt, code debt, design debt, defect debt, documentation debt, and build debt.

Managing tech debt requires identifying, measuring, and managing. However, ignoring can strain an organization’s financial and human resources. Further, engineers spend a considerable amount of time dealing with tech debt.

In summary, tech debt represents the cost of maintaining or improving outdated systems versus investing in emerging technologies and timely upgrades.

A telecom order management system refers to order management systems used by telecommunication service providers to track order information from all points of the telecom order lifecycle. This includes managing and updating the data, customers, and processes specific to telecom order lifecycles.

Controls how creative or focused AI is, which is a setting that affects how varied or predictable AI’s responses are (Lower values = more focused; higher = more random or creative)

A cutting-edge infrastructure as code (IaC) solution revolutionising deployment by enabling users to define, provision, and manage cloud and on-premises infrastructure through declarative configurations. This innovative tool enhances scalability and efficiency, streamlining the orchestration of complex IT environments.

Test-driven decelopment (TDD) is a software development practice where a test is written before the corresponding implementation code. The typical cycle is: write a failing test, write the minimum code needed to pass it, then refactor. Keeps the codebase continuously verified and shapes implementation around clear, testable requirements from the start.

The point where AI-generated code without engineering discipline stops paying off. Early velocity feels unstoppable. By months four to nine, agents lose context inside a bloating codebase and progress slows. By months sixteen to eighteen, the cost of maintaining tangled logic passes the cost of a rewrite. Specification-first development is how Notch keeps teams off this path.

A token in LLMs is a small unit of text that the model reads and processes so instead of understanding full sentences or words directly, the model breaks text into smaller pieces called tokens (unbelievable – un, believ, able).

The discipline of understanding what actually drives AI spend (input vs. output token pricing, prompt caching discounts, model tier selection, context length), because an agent that re-reads a large codebase on every turn can cost 100× more than one engineered to cache and summarise.

AI splits text into manageable, smaller units called tokens to better understand and respond within its processing limits.

A mechanism that allows large language models to query a database, call a web API or run a calculation — and feed the result back to the model as a new message in the context window.

The full cost of running an AI system beyond API fees — including engineering time, infrastructure, fine-tuning, monitoring, compliance overhead – the number that makes on-prem vs. cloud trade-offs more meaningful.

A neural network setup that helps AI grasp how words relate to each other by using attention mechanisms, making it effective at understanding and generating complex text.

Extending JavaScript, TypeScript is a superset that introduces static typing and additional features to improve the development of large-scale applications. TypeScript supports the ECMAScript standard and is versatile for client- and server-side coding. It enhances the creation of sizable JavaScript applications through optional static typing, interfaces, and other features. TypeScript code is transpiled to standard JavaScript, ensuring compatibility with existing JavaScript environments and libraries.

The artistic process of shaping visually enticing and user-friendly digital interfaces involves strategically arranging elements, such as buttons and icons, to optimise the user’s interaction with a website or application.

In software development, a User Requirements Specification (URS) is a document that details the specific user needs, expectations, and requirements of a product or system. The URS is a fundamental tool that documents what users require for their system to function effectively.

A URS defines how the users expect the system to perform, and serves as a reference point for developers and stakeholders. The URS is not intended to include technical details or implementation methods. Instead, it focuses on user-centric requirements which can be tested during the validation process.

Typically, a URS document should include:

Introduction: The introduction outlines the project scope and objectives;
Functional requirements: This section defines the specific functions that the system needs to perform;
Non-functional requirements: This includes criteria such as performance, usability, and security level the system needs to meet;
Use cases: This section describes how users should interact with the system.

A User Requirement Specification serves to ensure that everyone involved in development understands the user needs. This helps establish the project requirements at the start of the development lifecycle, which reduces the probability of changes in the latter stages. Finally, a URS serves as a reference point against which to test whether the product meets user requirements.

The art and science of crafting seamless interactions between users and digital interfaces. It involves thoughtful design, intuitive navigation, and empathetic understanding to enhance user satisfaction.