Your agent isn't hallucinating. It's reasoning correctly over the wrong inputs.
Here's a failure pattern every team running agents in production has hit:
An alert fires. The agent investigates: pulls metrics, checks recent deploys, scans logs, proposes a fix. The fix is confidently, articulately wrong. Instinct says blame the model (bad reasoning, needs a better prompt, maybe a bigger model).
Then someone reconstructs what the agent actually saw. The metrics query returned a 5-minute-old cached aggregate. The deploy list was fetched before the relevant deploy landed. The log window was truncated at 1,000 lines and the line that mattered was #1,014. Given those inputs, the agent's conclusion was reasonable. It just wasn't debugging the incident that happened.
It's garbage in, garbage out, with a twist that makes it worse for agents than for any previous software. A dashboard shows you the garbage. A human sees a stale chart and might notice the timestamp. An agent consumes the garbage silently and acts on it, with fluent reasoning layered on top. The output doesn't look like garbage; it looks like a confident, well-argued investigation. That confidence is what makes it dangerous.
