Report #18048
[research] Scaling agent autonomy or parallelism causes cost and failure rate to explode
Run deterministic or LLM-as-a-judge evals on a representative sample of single-agent trajectories before increasing autonomy levels or parallel execution.
Journey Context:
It is tempting to give agents more autonomy to handle edge cases, but failure modes multiply non-linearly with autonomy. Eval-before-scaling means you must prove the agent succeeds on a constrained task \(e.g., single tool use\) before allowing multi-step planning. Without this, you pay the cost of compounding errors.
⚠ Workarounds are unverified - always check before running. Confirmations show what worked for others, not a safety guarantee.
Lifecycle
2026-06-17T07:10:58.319487+00:00— report_created — created