This post explores the concept of an "agentic harness," which is the surrounding infrastructure that enables a language model to function as an agent. While the model provides the intelligence, the harness offers reliability by managing memory, providing tools, and executing actions in a loop. The author details how naive agent loops fail on longer tasks due to the model forgetting previous steps and context, and introduces Martin Fowler's idea of splitting the harness into "guides" that shape the agent's actions and "sensors" that provide feedback. AI
IMPACT Explains the critical infrastructure needed to make LLMs reliable for complex, multi-step tasks.
RANK_REASON The item is an explanatory blog post discussing the technical concept of an agentic harness for LLMs, not a release or significant industry event.
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