AI Agent Task Failures Explained by 'Half-Life' Concept
TL;DR. New research suggests AI agent failures in multi-step tasks follow a predictable 'half-life' decay model, not increasing difficulty. - A 95% reliable agent finishes a 10-step job only 60% of the time, illustrating the rapid decay. - The problem is 'exposure' to failure rate over time, not model intelligence or task difficulty. - Engineers should focus on 'resumable runs' or reducing exposure, rather than just improving models.
- AI agents fail multi-step tasks due to a 'half-life' effect, where per-step reliability compounds into rapid job failure.
- A 95% reliable agent performing a 10-step task has only a 60% chance of completion; for 48 steps, it drops to 8.5%.
- Research indicates failures are due to a constant 'hazard rate' per unit of task duration, not increasing task complexity.
- The solution involves reducing 'exposure' (task length or number of steps) or implementing resumable workflows, rather than solely improving model intelligence.
Sources
- AI Agents have a half-life — construct.computer