Agentic systems push your transformation from building for automation to training for autonomy, at every stage.
We design, build, and deploy multi-agent systems for enterprise workflows using the Agent Development Lifecycle (ADLC), a methodology built for agentic AI.
Senior engineers own the architecture. Agents execute within it. Every action is traceable and human control is defined upfront.
“Great engineering rests on three pillars: the discipline to build software that lasts, the intuition to catch what AI gets wrong, and the common sense to know when simpler is smarter.”
Identify where agentic AI creates the most value in the existing system.
Define memory, human-in-the-loop needs, data scope, and autonomy boundaries per task.
Select agents, tools, and libraries. Define how each works in the harness.
Connect agents to existing systems within the governed orchestration layer.
Observability, traceability, and reasoning quality tested in production.
Define the model: private or public, local or cloud, with escalation paths.
Evaluating and recommending which AI agents are best suited for each specific business task, combining structured technical assessments with strategic business context.
Designs, builds, and integrates agent workflows, ensuring they interact correctly with your systems and handle low-risk tasks and decisions consistently and reliably.
We follow the agent development lifecycle (ADLC), a six-phase lifecycle built for agentic systems. Each phase produces a defined output your team approves before the next begins, accounting for non-deterministic behavior and post-deployment evaluation loops.
Agentic AI systems go further than traditional automation. They make context-aware decisions, take actions, and interact with systems and people based on what is actually happening; not a fixed ruleset written for static conditions. When inputs are dynamic and unpredictable, agentic systems adapt. At Notch, every agentic system is built with human-in-the-loop controls defined before a single agent reaches production. Which actions run automatically. Which require approval. Which always need a human decision. That’s not a safety net. It’s an architectural standard.
A recruitment platform that receives a job brief, searches candidate databases, scores CVs against requirements, drafts outreach messages, and schedules interviews without a human coordinating each step. Each task is handled by a specialized agent. The agents delegate, share context, and recover from failure automatically. A human reviews and approves at defined points. The workflow runs end to end. That is agentic AI in practice. Notch built a system of this kind for Candid, cutting CV processing time by 75%.
Most initial implementations can be prototyped in 2–4 weeks, with full deployments typically completed within 6–8 weeks, depending on complexity and integration depth.
We implement a risk-based framework where only low-risk, well-defined decisions are handled by agents. Mid- and high-risk scenarios are escalated to humans, with clear logic and audit trails to maintain accountability.
