AI agents are easy to build and hard to trust

Building an AI agent that works in a demo has become relatively simple.

A team can connect a language model to a few tools, give it a goal and watch it book a meeting, file a ticket or summarize a workflow. The harder question is what happens when that same agent runs thousands of times a day against real systems, real users and real business consequences.

That is where many agentic AI projects run into trouble. The model may be impressive, but production systems need more than a capable model. They need boundaries, contracts, monitoring and clear rules for what an agent is allowed to do.

Sreekanth Ramakrishnan has spent much of his career building systems meant to stay dependable at scale. A senior software engineer with experience in platform engineering, API systems and service contracts, he focuses on the infrastructure that makes software predictable under pressure.

His book, Contract-Driven AI Systems, argues that AI agents earn trust from the engineering around them, not just from the model at their center.

The demo is not the product

A demo proves that an agent can succeed once. Production asks whether it can behave safely over and over again.

That difference matters. Real production environments are messy. Inputs vary, tools time out, permissions change, systems return unexpected errors and the same request may run thousands of times with slightly different data.