An AI agent survives production when three things exist before launch, not after: an evaluation suite that runs on every change, a guardrail layer that constrains what the agent is actually allowed to do, and a human review queue for the decisions a model shouldn't make alone.
Start with evals, not prompts
Teams treat the prompt as the product and iterate on wording until a handful of examples look good. That approach breaks the moment real users send inputs nobody tested. Before writing a single prompt, we define the eval set: the inputs, edge cases, and adversarial examples the agent has to handle correctly, plus a scoring method that runs automatically on every change to the prompt, model, or tool definitions.
Guardrails are a contract, not a suggestion
A guardrail layer sits between the model and anything that touches real data or real money — schema validation on every tool call, allowlists for which actions an agent can take unsupervised, and hard limits on spend, rate, and blast radius. If the model can't violate the contract even when it hallucinates, most production incidents disappear before they start.
- Validate every tool call against a strict schema before execution
- Log full traces (inputs, outputs, tool calls) for every run, not just failures
- Route low-confidence or high-stakes decisions to a human review queue
- Ship a kill switch and a rollback path before the first real user touches it
Reliability is a feature, not a post-launch patch.
What this looks like in practice
Across the agent deployments we've shipped, the pattern holds: teams that treat evals and guardrails as day-one infrastructure reach production in weeks and stay there. Teams that skip straight to prompt tuning usually ship something impressive in a demo and then spend months firefighting after launch.
How long does it take to move an AI agent from pilot to production?
With evals and guardrails built in from the start, most teams we work with reach a production-ready agent in 6-10 weeks. Without them, the same agent typically stalls indefinitely in pilot.
What is the biggest reason AI pilots fail to reach production?
Missing evaluation infrastructure. Teams can't tell whether a change made the agent better or worse, so every iteration is a guess, and stakeholders lose confidence before the agent ever ships.
Hayden Jackson
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