Experience Graphs: The Data Foundation for Self-Improving Agents
brucemacv · x · 2026-07-10
The post shared research titled "Experience Graphs: The Data Foundation for Self-Improving Agents." The core argument is that long-term agent tasks generate executable artifacts, tool outputs, rewards, failure branches, and repair processes, and these interactions shouldn't be discarded as one-off session logs. The study proposes using an experience graph—a structured representation—to preserve the causal relationships and intermediate states encountered during an agent's exploration, facilitating crash recovery, cross-user queries, and reuse for subsequent training.
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