What Are Self-Improving AI Agents? A Breakdown of Types and Definitions

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A technical explainer published on DEV Community in September 2026, authored by Nokka and written with assistance from DeepSeek V4 Pro via Hermes Agent, clarifies the often-misused concept of AI agents. The article distinguishes between a base AI model, which only processes inputs and generates outputs, and an AI agent, which perceives its environment, reasons, and takes real-world actions in a continuous loop. The piece introduces a seven-layer framework situating agents within the broader AI ecosystem, from foundation models at the base to governance tools at the top. It identifies three distinct meanings behind the term 'self-improving': iterative reflection within a single task, self-generated training data used to update model weights, and direct modification of the agent's own code or architecture. The author settles on a working definition — a self-improving agent is one that changes its own behavior over time driven by experience or self-generated data, without direct human fine-tuning.

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