Production-ready AI agents
Most teams ship AI agents the same way they ship demos. Hit run, eyeball the output, ship it. The team becomes the eval. That has a ceiling, and it does not survive the first time leadership asks what happened when something broke.
Production readiness for AI is its own discipline. The work happens at the contract layer. Every agent earns autonomous status by passing a binary rubric across every axis. Telemetry catches drift before it lands in front of a customer.
Three layers.
Rule checks on every output handle the deterministic axes. Word count, schema validity, banned phrases, link validity. Cheap, fast, catches the obvious failures before they ship.
Binary scoring per axis handles the subjective ones. Tone, factual grounding, refusal behavior, relevance. Each axis is pass or fail. Scalar scoring introduces noise across raters. The eval becomes a debate about the scale.
5+ consecutive passing runs across all axes gate autonomous status. Telemetry aggregates failures so patterns surface before they become production incidents. Domain expert calibration aligns the LLM-as-judge with the human standard.
The methodology is industry standard. What ships in production is the operational wrapper that turns it into a contract every agent earns before going autonomous.
- ✓80% reduction in bad outputs across the agent fleet
- ✓Failures land on specific axes. Debug starts at the line that broke.
- ✓The contract every agent passes before going autonomous, and the ongoing telemetry that keeps autonomy gated throughout deployment.
Most teams that fail with production AI failed at production readiness. Demos pass eyes-on review. Production agents fail customers at scale because nobody set the contract.
The contract is the difference. Production agents are graded against observed failures, and the framework is what makes that real.