Agentic Workflows
AI pipelines that do real work, not just demos.
- End-to-end agent pipeline architecture and implementation
- Tool and API integration for autonomous agent actions
- Error recovery, guardrails, and human-in-the-loop fallbacks
- Evaluation frameworks to measure agent reliability over time
What this is
Agentic workflows are AI systems that go beyond single-prompt interactions. They chain together reasoning steps, tool calls, and decision points into pipelines that handle complex tasks from start to finish. Autonomous workers that can plan, execute, verify, and adapt.
I build these systems for production, not for demos. That means proper error handling, retry logic, cost controls, and observability baked in from day one. Every pipeline I ship can run unsupervised and degrade gracefully when something unexpected happens.
The difference between a cool prototype and a production agent is about a thousand edge cases. The multi-agent pipeline that ships features to this site is public, failure modes included, so you can see how I handle them.
How it works
I start by mapping the workflow to automate. Not the idealized version; the real one, with all its exceptions and edge cases. That conversation usually surfaces which steps actually need AI reasoning and which are better handled by deterministic code. The answer is almost always a mix.
From there, I build the core path first. Something running in the real environment, against real data, early. Then I harden it: error recovery, retry logic, cost guardrails, monitoring, and evaluation suites. Each iteration delivers a working pipeline that's more reliable than the last.
There's no black-box handoff because there's no black box. Design decisions get documented as they're made, and known failure modes get written down instead of sanded off.