The most dangerous thing an LLM can say is:
"This is the definitive solution."
I know, because I heard it six times in a row, each time about a different fix for the same bug.
I was building a Kubernetes Operator and hit a race condition in the reconcile loop.
The Operator would update the custom resource status, then immediately get overwritten by a previous state that hadn't been flushed yet. The result was a phase transition loop: the resource cycling through states endlessly, never stabilizing.
I turned to the LLM. Internet search enabled. Full context provided.
The first suggestion was confident and specific. A pattern for handling concurrent reconciliation. Reasonable. I tried it. Didn't work.
The second suggestion went deeper, a sub-function within the controller runtime. Also confident. Also specific. Also wrong.
By the fourth or fifth iteration, the model was descending into progressively more obscure corners of the Kubernetes internals. Each time with the same tone:
"This is a common race condition in Kubernetes Operators. This is the definitive solution."
It wasn't lying. It wasn't hallucinating functions that didn't exist. It was finding real patterns, real APIs, real Kubernetes behaviors and misapplying every single one of them to my specific context, with complete confidence.
Internet search didn't help. It just gave the model more ammunition to be wrong with.
What finally solved it had nothing to do with the model.
I stopped asking. I did the research myself, read through the controller-runtime source, found the specific behavior causing the overwrite, understood the exact sequence of events in my reconcile loop. Then I came back to the model with a clear, grounded explanation of the root cause and explicit instructions for the fix.
It worked immediately.
The model didn't solve the problem. I solved the problem. The model wrote the code.
That distinction matters more than most people want to admit.
Now think about what happens when you add agentic flow.
No human in the loop. The model hits the same kind of problem: a subtle race condition, a context-specific behavior it can't reason about correctly from training data alone. It generates a fix. It runs it. It observes the output. It decides the fix didn't work and tries something deeper.
It does this autonomously. Confidently. Repeatedly.
Each iteration, it goes further into the codebase. Refactoring here. Adding an abstraction layer there. Patching a symptom in one place while introducing a new one in another. Burning tokens at every step.
By the time a human looks at the output, the codebase is a spaghetti mess and the original bug is still there, buried under six layers of confident, well-intentioned, completely misguided changes.
The model didn't fail because it was dumb. It failed because it was confident about something it fundamentally could not know without the right context. And nothing stopped it from acting on that confidence, repeatedly, at speed.
This is the part of agentic AI development that the demos don't show you.
The capability is real. The productivity gains are real. But autonomous execution amplifies both good judgment and bad judgment equally. A model that would have wasted 30 minutes of a developer's time in a chat session can waste 3 hours of compute and leave a codebase significantly worse in an agentic loop.
The solution isn't to avoid agentic workflows. It's to understand where models fail and build human checkpoints, grounding steps, and context injection at exactly those points.
I learned this the hard way, one race condition at a time.
Have you seen an agentic workflow make a problem worse before it made it better? What guardrails have you built?