AI Agent Workflows

Design, build, and operate autonomous AI agents that execute real business tasks end-to-end.

AI agents are powerful when they are scoped tightly, instrumented thoroughly, and observed continuously. In practice, most agent projects do not fail because of the underlying model — they fail because of poor task definition, unreliable tool interfaces, and no way to detect when things go wrong before the damage is done. I help mid-size businesses design, develop, and integrate AI agent workflows that automate real business tasks reliably, with the safety boundaries and observability that production systems require. The first step in any agent engagement is task selection. Not every workflow is suited for autonomous execution. I evaluate candidate processes against three criteria: the task must have a clear success condition that can be verified programmatically, the tools the agent needs to use must be well-defined and reliable, and the cost of an undetected failure must be bounded. Processes that meet these criteria become excellent candidates for AI agent development. Typical AI agent workflows I build include: research agents that gather and synthesize information from multiple sources and produce structured reports, data processing agents that transform unstructured inputs into database-ready records, monitoring agents that watch for defined conditions and trigger actions or alerts, and multi-step workflow agents that coordinate sequences of tasks involving external APIs, databases, and communication systems. Tool design is one of the most critical and most underestimated aspects of building reliable AI agents. The tools an agent can call must have unambiguous descriptions, predictable input and output schemas, and sensible error handling. I spend significant time designing tool interfaces that the model can use correctly and consistently — not just in test cases, but across the full distribution of real-world inputs. Evaluation is built into every agent system I deliver. Before deployment, I build an evaluation harness with representative test cases, adversarial inputs, and quality metrics relevant to the specific task. This harness runs on every model update and every prompt change, catching regressions before they reach production. Every agent system I build includes full observability: structured logging of every decision step, tool call, model output, and downstream action. For agentic workflows, this trace is essential — it is what allows you to understand why the agent did what it did, correct misconfigurations, and demonstrate to stakeholders that the system behaves as intended.