When Your Agent Gets It Wrong

Failure in agent systems is not an edge case — it is a core design constraint. Unlike traditional software, where errors are often deterministic and predictable, agent failures are probabilistic and emergent.

We categorize failures into three main types.

Hallucinations occur when the model produces plausible but incorrect information. These are particularly dangerous because they often appear confident and well-structured.

Misrouting happens when the system selects the wrong model or tool for a task. This is often a failure of the routing layer rather than the model itself.

Incomplete execution occurs when a multi-step workflow breaks midway, leaving partial state changes or unresolved outputs.

To handle these, we design for graceful degradation rather than perfection.

Fallback mechanisms ensure that if a primary model fails, a secondary path can recover partial results. Verification layers check critical outputs before they are committed or acted upon. And execution logs provide full traceability for debugging and improvement.

One of the most important design principles we adopted is this: every failure should increase system knowledge.

Instead of treating errors as noise, we log them as structured signals that improve routing, memory, and orchestration over time.