The 80% problem
Any developer with an API key can build a demo AI agent in an afternoon. The demo will handle the happy path beautifully. Then it meets production — the long tail of edge cases, the partial outages, the unexpected user inputs — and everything falls apart.
The jump from demo to deployed is where 80% of AI projects die.
What production-ready actually means
An AI agent is production-ready when:
- Its inputs are validated. Users send weird things. APIs return nulls. Agents that trust their inputs fail loudly in production.
- Its outputs are validated. LLMs hallucinate. Structured output with schema validation is table stakes.
- It has a fallback for every tool call. APIs go down. Your agent's retry logic matters more than its prompt.
- It has observability. If you can't see what your agent decided and why, you can't debug it.
- It has cost guardrails. An agent in a loop costs real money. Rate limits, token limits, circuit breakers.
- It has a human-in-the-loop mode. Some decisions are too consequential to leave to the model. Build escalation paths from day one.
What we recommend
Start narrow. One workflow. One user type. One measurable outcome. Ship it, instrument it, iterate on real usage — not on imagined usage in a planning doc.
Scope creep kills AI agents faster than any prompt engineering mistake.