Building Adaptive AI Agents
Building Adaptive AI Agents — Course Glossary Key terms for building AI agents that improve over time through behavior adaptation, knowledge adaptation, and weight adaptation. Foundations Agent A system that takes in information from its environment, reasons about what to do next using an LLM, and uses tools to act on that environment. An agent that also has memory can retain useful experience…
Building Adaptive AI Agents focuses on creating systems that improve over time through various forms of memory and adaptation. Agents, which interact with their environment and use large language models (LLMs) to reason and act, can retain useful experience from past interactions through memory systems. There are three types of memory: working memory, which exists only during a single run; episodic memory, which persists across sessions to recall specific past interactions; and semantic memory, which stores project-specific facts and knowledge.
The agent loop is the repeating cycle that allows an agent to complete a task, with the LLM supplying reasoning and the harness supplying structure around it. Memory systems, including trace, working memory, episodic memory, and semantic memory, enable agents to learn and improve over time. Two forms of adaptation are token space adaptation, which improves performance by changing the context and instructions without altering the model's weights, and weight space adaptation, which directly modifies the model's weights, typically through fine-tuning.
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