I built my first AI agent for workflow orchestration about two years ago, and I'll be honest — I thought it would be easier. The pitch sounds seductive: give an LLM a goal, let it break down tasks, call APIs, and handle exceptions. No more hardcoded state machines. No brittle BPMN diagrams. Just tell the agent what you want and let it figure out the rest.

It worked great in the demo. The agent received a purchase request, checked inventory, queried the supplier API, cross-referenced budget data, and generated a PO — all in one smooth chain. I showed it to the VP of Operations and he practically high-fived me. We rolled it into production the next week.

Then the real world showed up. The agent couldn't handle a supplier API that returned a 500 error mid-workflow. It just kept retrying the same call, burning tokens and time. Another time, the budget service was down, and the agent decided to bypass the check entirely — it generated a PO for a product we couldn't afford. That one cost us a conversation with finance that I'd rather forget.

The problem wasn't the LLM. It was that I'd treated the agent like a black box that would magically handle edge cases. It doesn't. An agent is just code that makes decisions, and without proper guardrails, it will make bad ones. I learned that the hard way.

Now I build agents with explicit state machines underneath. The LLM handles the unstructured parts — parsing a vague email request, deciding which supplier to contact, or summarizing a conversation. But the orchestration itself has clear boundaries. If an API call fails three times, the agent escalates to a human. If a budget check is unavailable, the workflow pauses, not proceeds. Those rules aren't learned; they're hardcoded.

I also learned to log everything the agent does. Not just the final output, but every intermediate decision, every API call, every prompt sent and response received. When something goes wrong — and it will — you need to replay the agent's reasoning to understand why it made a choice. Without that traceability, debugging is just guessing.

The other thing that surprised me was how much context matters. An agent that works perfectly at 10 AM might fail at 2 PM because the database has a daily backup window. Or it might make different decisions on Monday versus Friday because the pricing data gets stale. I started injecting time-of-day and day-of-week into the agent's system prompt, and it helped a lot.

Looking back, I think the real value of AI agents isn't replacing workflows — it's making them adaptive. You still need the structure, the error handling, and the human oversight. But instead of writing ten different paths for ten different scenarios, you write one path and let the agent navigate the variations. That's the sweet spot.

I'm still cautious about where I deploy agents. High-risk workflows with legal or financial consequences? I keep those human-in-the-loop. But for internal tools — routing support tickets, generating reports, managing inventory — agents are genuinely useful. They just need the same engineering rigor you'd apply to any other component. Treat them like junior developers: give them clear instructions, monitor their work, and don't assume they'll get it right every time.