Everyone's building AI agents. The demos are impressive. The production deployments? Mostly a disaster. Here's what we've learned from running agents in production across multiple enterprise environments.
An AI agent that works in a demo has exactly one thing going for it: a controlled environment. In production, you face:
None of these show up in demos. All of them show up in production within the first week.
Every agent action should pass through a validation layer before execution. Not just input validation — output validation. Before an agent writes to a database, sends an email, or modifies a file, a deterministic guard rail checks:
Borrowed from microservices architecture: if an agent fails N times in a row, stop trying and alert a human. This prevents the nightmare scenario where an agent keeps retrying a broken operation, burning tokens and potentially corrupting state with each attempt.
We implement this at two levels: per-tool (if a specific API keeps failing) and per-task (if the overall goal isn't making progress).
Every agent action, every tool call, every decision point gets logged to an immutable audit trail. Not just for debugging — for compliance, for rollback, and for improving the agent over time.
We log: the prompt, the model's reasoning, the tool calls, the results, and the final output. This makes post-incident analysis actually possible.
Design your agent to know when it's uncertain. This sounds obvious, but most agent frameworks don't handle it well. We use confidence scoring: if the agent's certainty drops below a threshold for a high-stakes action, it pauses and asks a human.
The key is calibrating the threshold. Too low, and the agent escalates everything (defeating the purpose). Too high, and it confidently does the wrong thing.
Standard application monitoring isn't enough for AI agents. You need:
Agent tries action → gets error → tries slightly different action → gets same error → repeat forever. Solution: track action history within a session and hard-limit retries.
Agent generates a plausible-sounding answer that's completely wrong, and acts on it. Solution: fact-check critical outputs against ground truth before acting. Use retrieval, not generation, for factual claims.
User asks agent to "clean up the data" and it deletes half the database. Solution: explicit scope boundaries and magnitude limits on every destructive operation.
Agent encounters a large context (big file, long conversation) and costs spike 100x. Solution: context windowing, summarization, and hard token budgets per task.
After iterating through multiple frameworks, here's what we've settled on:
AI agents in production are hard. They're non-deterministic systems running in deterministic environments, and every edge case is a potential incident. But when they work — when they're properly guarded, monitored, and scoped — they're transformative.
The difference between a toy demo and a production system is about 10x the engineering effort. Budget for it.