Experiential Memory: How AI Agents Learn from Doing

Experiential memory enables learning from doing—agents that get better over time without explicit retraining. It’s the bridge from static tool to adaptive partner, capturing the wisdom accumulated through action and reflection.

The Learning Loop

Experiential memory operates through a continuous cycle: Act → Reflect → Store → Apply. The agent takes action, evaluates outcomes, stores successful patterns, and applies learned strategies to future situations. This creates a self-improving system that develops expertise through use.

Three Types of Experiential Memory

Case-Based: Past problem-solution pairs. When facing a similar problem, retrieve the case. Systems like Voyager, GITM, and Reflexion maintain libraries of “when X happened, doing Y worked.” Useful for domains with recurring patterns.

Strategy: High-level plans and approaches. Rather than specific solutions, these capture reusable tactical knowledge—goal decomposition patterns, approach selection heuristics. AppAgent and AutoManual exemplify this level of abstraction.

Skill: Executable code and procedures. The most concrete form—composable, reusable actions that the agent can invoke directly. Voyager’s skill library, JARVIS, and SkillGPT generate actual functions: def mine_diamond(): find_ore(); use_pickaxe(); return diamond.

The Abstraction Spectrum

Experiential memory spans from raw episodes (“Did X, got Y”) through processed cases (“When X, do Y”) and abstract strategies (“For goals like X…”) to executable skills (function mine_ore()). Higher abstraction enables broader application but risks losing context-specific nuance.

Representative Systems by Domain

Gaming: Voyager (skill library), Reflexion (self-reflection). Coding: GITM (case-based), ExpeLance (experience pool). Robotics: ExpeL (exploration), AppAgent (UI automation). Web Browsing: SkillGPT (skill synthesis), AutoManual (strategy documentation).

Key Insight

Experiential memory enables learning from doing—agents that get better over time without explicit retraining. This is how AI systems evolve from tools you configure to partners that develop alongside you.

Read the full analysis: The AI Agents Memory Ecosystem

Source: Hu et al. (2025) “Memory in the Age of AI Agents” arXiv:2512.13564

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