AI agents fail 70 to 95 percent of real multi-step tasks in production despite benchmark scores
Research across multiple 2025-2026 papers confirms agents fail 70-95% of real-world office tasks. An 85% per-step accuracy agent running a 10-step workflow only succeeds 20% of the time. Failure compounds super-linearly. Benchmarks report pass@1 on short tasks hiding this. 68% of production agents execute at most 10 steps before requiring human intervention. The 12-factor-agents repo (23k stars, HN front page, 475 pts) was built specifically because builders cannot trust agents on tasks longer than a few steps. Reliability is the top unsolved challenge builders cite.
A web app that stress-tests AI agents on real multi-step production tasks before they ship
25.5k ▲Score Breakdown
Social Proof 2 sources
Existing Solutions 3 competitors
Adds human approval gates to agent workflows to prevent compounding failures on long tasks
Evaluation and tracing platform that measures agent success rates across test cases
Agent monitoring and cost tracking platform
Gap Assessment
Observability tools emerging but no standard agent reliability guarantee layer; human-in-loop frameworks are primitive