From research demos to deployed agents — patterns we've used to wire autonomous agents into mission-critical business processes.
Most of what gets called an "AI agent" in enterprise software today is actually a workflow with an LLM call inside it. That's not an insult — it's usually the right architecture. The teams that try to build truly autonomous, multi-step agents in 2026 are almost always solving the wrong problem.
The deterministic backbone
Production agents have a deterministic skeleton with LLM-powered joints. The control flow — when to call what tool, when to escalate to a human, when to stop — should be code you can read. The LLM handles the reasoning steps that genuinely need flexibility: extracting fields, deciding intent, summarizing a long thread. Everything else is a state machine.
Where agents actually win
We've shipped agents into support triage, sales research, internal IT, and contract review. The pattern is consistent: agents win in the long tail of workflows that are too varied for traditional automation but too high-volume for humans to handle one by one.
- Multi-step research tasks where the next step depends on what was found
- Routing decisions across many possible destinations
- Document understanding where the schema is loose
- Workflows where 80% of cases are easy and 20% need judgment
“A reliable agent is mostly code. The LLM is the part that lets you skip writing the last 20% of the if-statements.”
Failure modes you'll meet
Agents fail in ways monolithic systems don't. Token explosions when a tool returns more data than expected. Infinite loops when the model decides to retry the same failed call. Silent quality regressions when a tool's API changes and the agent quietly stops using it. You need bounded retries, hard token budgets, explicit per-tool eval coverage, and observability deep enough to replay any trace.
And keep humans in the loop for anything reversible costs money to undo. The biggest agent failures in production aren't from the model being wrong — they're from the system being too confident to ask for help.
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