Building Adaptive AI Agents

This article provides a comprehensive glossary for developers building adaptive AI agents, focusing on systems that improve over time through behavior, knowledge, and weight adaptation. It defines core concepts such as the agent loop, memory types (working, episodic, semantic, and procedural), and the distinction between token space and weight space adaptation. The text explores technical strategies for enhancing agent performance, including skill induction—where agents learn reusable procedures from past traces—and the implementation of code knowledge graphs to solve retrieval bottlenecks in large codebases. Additionally, it covers advanced techniques like LoRA for parameter-efficient fine-tuning and the use of model routers to manage specialized behaviors. By outlining these foundational components, the guide offers a roadmap for creating autonomous agents that can effectively reason, act, and evolve their capabilities through structured learning and human-in-the-loop review processes.