Why your AI agent demo breaks in production

Every team we talk to has seen the same movie. An agent demo that books the meeting, answers the ticket, or writes the report — flawlessly, once. Then it ships, and week two looks like this: silent failures at 3am, tool calls that hallucinate arguments, and a bill nobody forecast.

The demo wasn't a lie. It was just answering a different question. A demo asks "can the model do this task?" Production asks "can this system do the task ten thousand times, unattended, on the worst inputs your users will find?"

Where agents actually fail

After building agents for support, research, and operations workflows, the failure modes are boringly consistent:

Unbounded loops. An agent that can retry can retry forever. Without hard budgets — steps, tokens, wall-clock — one confused run quietly costs more than a month of normal traffic.

Tool trust. The model treats every tool result as truth. If your search tool returns garbage, the agent doesn't get suspicious; it builds confidently on the garbage. Validation belongs at the tool boundary, not in the prompt.

No memory of failure. Most agents wake up new every run. The same edge case fails the same way every day, and nobody notices because nobody is reading ten thousand transcripts.

Vibes-based evaluation. If "it seems better" is your eval, every prompt change is a coin flip. You can't improve what you don't measure — and agent regressions hide in the tail, not the average.

What we do differently

Our production checklist is short and non-negotiable:

1. Budgets on everything. Steps, tokens, time, and money — per run and per day. Exceeding a budget is a loud failure, never a silent retry.
2. Typed tool contracts. Every tool validates its inputs and outputs. The agent gets clean errors it can reason about, not stack traces.
3. Transcripts as data. Every run is logged, sampled, and scored against a rubric. The worst 5% of runs each week become next week's test cases.
4. A human escape hatch. The agent knows what it doesn't know. Ambiguity above a threshold routes to a person — with full context attached, so the handoff costs seconds.
5. Boring rollouts. New prompts and models ship behind flags to a slice of traffic, measured against the old version on real work before they take over.

None of this is glamorous. That's the point. The glamorous part — the model reasoning through a task — is maybe 20% of a production agent. The other 80% is the harness that keeps it honest.

If you have a workflow you think an agent should own, that's exactly the conversation we like. Tell us what you're building — we'll give you a straight answer on whether an agent survives contact with it.