AI Agents in the enterprise: from pilot to production

Most AI agent projects begin with an impressive proof-of-concept in a controlled environment. The real challenges emerge when you try to move to live production: inputs are inconsistent, business logic is more complex than anticipated, and legacy systems don't cooperate.

We have deployed AI agents for more than twenty enterprises over the past two years. The biggest lesson: success in a POC does not guarantee success in production. You need a separate roadmap for the operational phase.

The first phase — stabilization — is the most important yet most often skipped. During this phase the agent must run in parallel with the manual process, with mechanisms to compare outputs and catch deviations early. Do not switch off the old process until you have sufficient real-world operational data.

Monitoring is not optional. Every agent needs a dashboard tracking success rate, processing time, rate of manual intervention, and the distribution of input types. No visibility means no ability to improve.

After six weeks of stable operation with an error rate below the acceptance threshold, you are ready to retire the fallback process. Even then, keep a manual fallback mechanism for edge cases.

Real results across our deployments: average time from POC to production-grade is twelve weeks, not the two to three weeks most teams initially estimate. Investing properly in this phase is the single biggest factor determining long-term success.

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