Mistakes when deploying AI agents in a company

The most expensive mistakes when deploying an AI agent in a company are almost never technical: underestimating the data work, releasing the agent without evaluating its reliability, skipping human control and starting without an agreed success metric. The model is rarely the bottleneck; what sinks the deployment is everything around it. Knowing these failures in advance is the cheapest way to avoid them.

Treating the data as a detail

The mistake that paralyzes the most projects is assuming the information is already ready. In practice, the data the agent needs lives scattered across systems that do not talk to each other, with mismatched permissions, formats and quality. In the demo that is dodged with a hand-prepared extract; in production you have to connect the real sources. This is where techniques like RAG come in, giving the agent access to your sources with verifiable answers instead of trusting everything to its training. Whoever treats the data as a detail discovers too late that it was 80% of the work.

Releasing the agent without evaluating it

Moving to production "just to see how it goes" is betting the operation blind. Without evals —systematic tests with real cases, including the hard ones— you do not know whether the agent is right 95% or 60% of the time. That difference decides whether it can run work on its own or needs review at every step. Deploying without that measurement is not moving fast: it is shifting the risk to the users and finding out about the failures once they already have a cost.

Giving it full autonomy too soon

The urge to automate the process end to end from day one usually comes at a high price. An agent with no person overseeing at the points where human judgment matters —the human-in-the-loop pattern— acts on the operation without a safety net. Autonomy is not a switch you flip at deployment, but a margin that widens as evaluation proves reliability. Starting with human control and letting out the line gradually is faster in the medium term than cleaning up the errors of an unleashed agent.

Deploying without traceability or an owner

An agent that acts on real data has to leave a record of which information, steps and decisions produced each result —that is, traceability. Without it, nobody can audit an error or authorize the agent to keep operating. The other frequent oversight is not deciding who will own the system: if the agent is built by one team and operated by another without clarity from the start, it is orphaned as soon as the deployment ends.

Starting without a success metric

Deploying without an agreed criterion turns the project into a loop of "let's improve it a little more" that never ends. A single business metric, set before you begin —time saved, volume handled, errors avoided—, is what gives a defined point of success. If the case cannot be measured, it probably was not mature for production.

How we approach it at Codara

At Codara we avoid these mistakes by design: we connect the agent to your data and tools, evaluate its reliability, add human control and traceability, and build it as Codara's agentic orchestration layer that we then hand over so your team can run it without us.