Shipping AI agents to production: a field guide

The gap between an impressive agent demo and a reliable production agent is enormous. Demos forgive; production does not. Over dozens of deployments we've converged on a set of practices that reliably close that gap.

Start with the smallest useful loop

The instinct to give an agent every tool and full autonomy is the fastest path to an unreliable system. Instead, define the smallest end-to-end loop that delivers value, ship it with tight guardrails, and expand scope only as evidence accrues.

Instrument everything

You cannot improve what you cannot see. Every decision, tool call and token should be traceable. When an agent misbehaves — and it will — you need to replay exactly what happened.

• Trace every tool invocation with inputs and outputs
• Log the model's reasoning where policy allows
• Capture latency and cost per step
• Alert on guardrail violations

Autonomy without observability is just hope with extra steps.

Design for human oversight

The best agentic systems keep humans in the loop for high-stakes actions. Approval gates, permission scoping and clear escalation paths turn autonomy from a liability into an asset.

Evaluate continuously

Ship an evaluation harness alongside the agent. Simulate real tasks, score outcomes, and gate deploys on quality. This is what lets you move fast without breaking trust.