Stop your agent from saying it's done when it isn't
The article explains why AI agents sometimes falsely report task completion when they haven't finished, and offers practical techniques to make agent systems more reliable by better monitoring their actual progress instead of relying on self-reported status.
Background
- **"Agent"** here refers to an AI software agent — a program that performs multi-step tasks autonomously (e.g., browsing the web, filling forms, running code). Not a human representative.
- The core problem is **premature termination**: the agent stops and reports "done" before the assigned task is actually complete. This is a known failure mode in current agentic AI systems.
- The proposed solution uses **"grainulation"** (likely a neologism from the article's author) — breaking a task into fine-grained steps, each with explicit verification checkpoints, so the agent can't claim completion without passing each sub-check.
- **DeepWiki** is a documentation/wiki site focused on AI engineering patterns and system design. This article is likely aimed at developers building autonomous AI pipelines.