Giving an AI agentAn AI system that doesn't just answer once but works toward a goal in a loop — taking actions, reading the results, and deciding what to do next.Full definition → useful information does not ensure it will change its approach. A research abstract published by Cohere describes experiments that placed complete task solutions inside agent environments. In AppWorld, one of three benchmarks tested, agents saw documentation identifying a solution-returning command in over 90% of attempts, yet used it in fewer than 7% of trials. These deliberately modified tasks test whether agents act on unexpected discoveries. The reported finding separates finding relevant information from recognizing that it should redirect the work. Only the abstract was read, so the full methods are not covered.
Terms in this piece · Glossary
AI agent — An AI system that doesn't just answer once but works toward a goal in a loop — taking actions, reading the results, and deciding what to do next.
attention — The mechanism that lets a model weigh which earlier words matter for the word it's currently processing — the core operation of a transformer.
LLM — A large language model — the neural network behind tools like Claude and ChatGPT, trained on huge amounts of text to predict what comes next.
agentic loop — The cycle an agent runs in: decide, call a tool, read the result, decide again — repeating until the goal is met or a stop condition fires.
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