Your Agent Is Getting Weird

Why production AI needs testing, observability, feedback loops, and lifecycle ownership long after the demo ends

We all know the story.

A programmer builds a tool for one very specific use case. QA tests it upside down, backward, and under conditions no reasonable person would ever attempt. Somehow, it passes every test.

Then a user gets hold of it.

They use it in an unexpected workflow, apply it to a problem nobody anticipated, and ask it to do something that was never included in the requirements. Think of the McDonalds customer-service chatbot that somehow becomes somebody’s emergency code generator when they run out of tokens somewhere else.

AI agents are not immune to this. In fact, they can be even more convincing when they go wrong.

Have you ever interacted with an agent that was confidently, impressively wrong? The kind of wrong that makes you pause and wonder, How did this get past QA?

Congratulations. Your AI agent made it to production.

Unfortunately, production has users.

The agent that performed beautifully in a controlled demo may still respond quickly, return healthy APIs, and show a green monitoring dashboard while quietly doing the wrong thing.

Nothing is technically “down.”

And yet everyone using the system knows something is off.

Welcome to production AI.

The world changed. Your agent didn’t.

AI agents operate inside organizations where everything around them changes: data, APIs, permissions, models, workflows, and user behavior.

That creates several distinct flavors of production weirdness:

1. Knowledge drift: The agent retrieves information that used to be correct but no longer is.
2. Tool drift: The API still exists, but it no longer behaves the way the agent expects.
3. Permission drift: The agent loses access to something it needs—or gains access to something it should not have.
4. Model and prompt drift: A seemingly minor change produces unexpectedly different behavior.
5. Workflow drift: The agent faithfully follows a process the business changed three months ago and forgot to tell the robot about.
6. Usage drift: Humans do something nobody designing the system thought they would do.

That sixth flavor deserves special attention.

And then humans arrive

Users will combine several intents into one request, invent workflows you never designed, push edge cases, and use the system for things it was never meant to do.

They will also correct it, work around it, and sometimes teach it habits you did not want it to learn.

That human creativity is not necessarily a problem. In fact, production usage is one of the richest sources of information available to an AI engineering team. Your users effectively become your largest test suite.

Unfortunately, they do not file Jira tickets.

In systems with persistent memory, state, retrieval, and orchestration, those interactions matter because elements of prior use can influence what happens next.

But accumulating experience is not the same thing as learning.

Memory without feedback is accumulation, not learning

One of the more dangerous assumptions in production AI is that because an agent has memory, it will somehow improve naturally over time.

Memory alone creates accumulation.

Improvement requires an engineered feedback loop.

A thumbs-up or thumbs-down button is not one. Neither are logs or telemetry. Those mechanisms produce signals; the important question is what happens to those signals afterward.

A real feedback loop exists when feedback drives engineering action:

Observe → Evaluate → Correct → Validate → Improve

If users repeatedly receive poor retrieval results, the retrieval strategy may need to change. If the agent consistently chooses the wrong tool, the tool definition or routing logic may be at fault. If users uncover a new edge case, that interaction should become a new evaluation scenario.

And if people are constantly overriding the agent, that is not just frustration. It is operational data.

A thumbs-down button nobody reviews is just a tiny digital complaint box.

Your monitoring dashboard may be lying to you

Traditional monitoring can tell you whether the service is available, whether the API is healthy, whether latency is acceptable, and whether an exception occurred.

An AI agent can pass every one of those checks and still perform badly.

That is why AI observability has to extend beyond infrastructure health into agent behavior, output quality, and business outcome.

If a user tells you, “The agent said something weird yesterday,” your engineering team should be able to reconstruct the path: the user request, available memory and context, retrieval result, routing decision, tool selection, tool execution, response, and outcome. In other words, you need to be able to recreate the crime scene. 

“It said something weird yesterday” is not an observability strategy.

Tracing lets you identify where behavior diverged instead of merely discovering that the final answer was bad.

Turn the weirdness into a test

Production usage should continuously improve the evaluation set.

The scenarios used before launch should not remain frozen forever. Users will expose cases you did not anticipate, and the business will create situations you could not predict when the system was first designed.

Every sufficiently strange interaction should therefore become a candidate regression test.

Weird once is an incident.

Weird twice is a pattern.

Weird three times? Congratulations, you may have a feature.

The joke matters less than the principle: production should continuously expand your understanding of how the system can succeed and fail.

The users who expose your strangest failures are often identifying requirements you did not know you had.

Version everything that can betray you

Agentic systems can change behavior even when the application code does not.

Models, prompts, retrieval indexes, tool definitions, orchestration rules, memory policies, guardrails, and evaluation data can all alter performance.

That makes traceability essential.

If you cannot identify what changed, you cannot reliably determine what broke.

Rollback is more complicated too. You may need to revert a model, restore an earlier prompt, roll back a retrieval configuration, change a tool definition, undo an orchestration change, or intervene in persistent memory and state.

Production AI therefore requires operational discipline across all of the components that shape behavior, not just the application code.

Who owns this thing now?

Organizations spend enormous amounts of energy deciding who will build the agent. They spend considerably less time deciding who owns it after launch.

Someone needs to review feedback and evaluation performance. Someone needs to know when knowledge sources are stale or integrations change. Someone needs authority to approve changes to prompts, tools, models, and orchestration. Someone needs to define when the agent should stop, escalate to a human, or eventually be retired.

Without that ownership, production AI quickly becomes everybody’s responsibility and therefore nobody’s responsibility.

Deploying an agent without clear ownership is a little like releasing a raccoon into the enterprise.

It may be intelligent. It may be surprisingly capable. It may even accomplish some impressive things.

But someone still needs to be responsible for what it is doing.

Deployment is not the end of an AI project.

Deployment is the beginning of operations.

Production AI is a lifecycle

Agentic systems need ongoing mechanisms to catch strange behavior, diagnose it, correct it, prevent recurrence, and eventually retire what no longer serves the business.

That means testing, evaluations, observability, tracing, human review, versioning, rollback, governance, and ownership all belong to the same lifecycle.

Eventually, some agents should disappear.

A workflow may become obsolete. Functionality may become redundant. Maintenance costs may outweigh value. A newer capability may replace the original agent entirely.

Keeping an agent alive simply because somebody once built it is not an operating strategy.

Planned retirement is considerably better than slow drift into chaos.

Please stop setting AI and forgetting it

The next phase of enterprise AI maturity will not be defined only by who can build agents.

It will be defined by who can operate them.

That requires more than prompt writing or model selection. It requires architects, engineers, security teams, business leaders, and end users who understand how these systems behave once they enter a living organization.

The goal should not simply be to build more agents.

It should be to build organizations capable of keeping them useful, secure, observable, and aligned over time.

Your AI agent probably isn’t haunted.

You just forgot to operate it.