Give the agent a defined responsibility
Useful agentic AI starts with a bounded job: finding knowledge, preparing a response, coordinating a workflow, or deciding the next permitted step. I design AI agents around those responsibilities, with explicit tool access and clear human ownership.
What I build
- Task-focused agents connected to your APIs and business tools.
- LangGraph and LangChain workflows where their capabilities fit the architecture.
- Multi-agent systems when distinct responsibilities justify the added complexity.
- Approval gates for consequential actions.
- Evaluation examples, execution traces, and escalation paths.
From prototype to useful system
We identify the agent's inputs, permitted actions, and completion criteria. Then we test representative tasks and failure cases before expanding its autonomy. An agent that can explain its evidence and stop safely is more useful than one that always produces an answer.
My healthcare engineering work on Naya and knowledge-system work on Narravo inform this approach. Those projects have different requirements; their case studies distinguish implemented capabilities from intended uses.
Common questions
Should we use multiple agents?
Only when separating responsibilities improves the system. A single workflow is easier to evaluate and maintain.
Can an agent connect to our existing software?
Usually through supported APIs, tools, and integrations. We establish what is accessible during discovery.
How do we know it works?
We agree task-level evaluation criteria, including tool selection, answer quality, approval handling, and failure recovery. Start with when to choose an agent.