From AI Agent POC to Production.

POCs are built on clean sample data with the failure cases edited out. Production adds messy data, edge cases the demo never saw, missing API permissions, latency budgets, compliance review and a token bill that scales with usage. Without an evaluation suite there is no objective way to prove the agent is ready — so the project stalls in demo-review cycles.

Hardening a working POC typically takes 6–14 weeks depending on the number of production integrations, compliance requirements and how far the POC architecture is from something that can scale. A POC built with production patterns — durable state, typed tools, traced runs — lands at the short end of that range.

Demo agents call mock APIs, skip authentication, retry nothing and measure nothing. Production agents get OAuth-scoped tool layers, idempotent retries, human-in-the-loop gates on irreversible actions, evaluation suites with regression thresholds, full decision traces, per-run cost budgets and monitoring with alerting. The reasoning loop is usually the only part that carries over unchanged.

An evaluation suite is a versioned set of real tasks with expected outcomes, run automatically against every agent change. It converts "the agent feels worse this week" into a measurable regression. Without it, prompt or model updates are guesswork and there is no defensible answer when leadership asks whether the agent is accurate enough to expose to customers.

Stream tokens to the user, parallelize independent tool calls, cache repeated retrievals, route simple steps to smaller faster models, and cap the number of reasoning iterations per task. Multi-step agents are latency multipliers — each sequential LLM call adds seconds — so the loop structure matters as much as model choice.

A POC at ten runs a day hides token consumption that becomes a real bill at ten thousand. Production cost control means per-task token budgets, semantic caching, smaller models for routine steps, batch APIs for non-urgent work and dashboards that attribute spend to specific workflows. We model this during hardening, not after the first invoice.

Yes. We start with a code and architecture audit of the existing POC — framework, tool layer, state handling, prompts and data access — then produce a gap assessment and a fixed-price hardening plan. Sometimes the POC carries over cleanly; sometimes the reasoning loop survives but the plumbing gets rebuilt.