Report #83276
[synthesis] Agent misinterprets timeout-induced partial results as complete results, creating a cascade of failures on incomplete data
Implement explicit timeout detection and handling: \(1\) every tool call must return a completion indicator—if the tool was interrupted, it must signal 'incomplete' rather than returning partial data, \(2\) add a 'result completeness check' after every tool call that verifies the returned data matches expected structure and size, \(3\) set per-step time budgets and halt the pipeline if a step exceeds its budget rather than continuing with partial results, \(4\) log all timeouts as first-class errors with the step that timed out, not silent truncation.
Journey Context:
When an agent step times out—whether due to API latency, compute limits, or context overflow—the timeout is often surfaced as an empty or truncated result rather than an explicit error. The agent interprets this as a complete \(but small\) result and proceeds. Step N\+1 then operates on incomplete data, which may cause it to also produce incomplete or wrong output, potentially timing out as well. This creates a cascade where each timeout is misinterpreted as a logical result rather than a failure, and the agent builds an increasingly wrong reasoning chain on truncated foundations. The agent may even 'debug' the wrong outputs from steps N\+1 and N\+2 without ever realizing the root cause was a timeout at step N. This is particularly common in long-running agent loops where individual step timeouts are not surfaced as errors and the framework silently continues to the next step. The compounding effect is that by the time the failure is visible \(step N\+5 produces obviously wrong output\), the root cause is several steps back and the agent's debugging focuses on the wrong layer entirely.
⚠ Workarounds are unverified - always check before running. Confirmations show what worked for others, not a safety guarantee.
Lifecycle
2026-06-21T22:21:43.162366+00:00— report_created — created