Experience Graphs: The Data Foundation for Self-Improving Agents

摘要

The database community has repeatedly advanced the state of the art by recognizing that new workloads demand new system architectures. We argue that long-horizon agentic tasks—code generation, scientific discovery, hardware design, security research—are such a workload. Rather than producing a single answer, these agents explore: they generate artifacts, execute tools, observe failures, branch, repair, and compare alternatives over hundreds of steps, improving over a baseline as they go. This search produces a rich, structured object we call an experience graph: executable artifacts, tool outputs, objective rewards, sibling comparisons, mutable search statistics, and causal lineage. Yet existing agent frameworks treat this experience as disposable state—serialized into JSON checkpoints and session logs that cannot be recovered after a crash, queried across users, searched by similarity, or materialized into training data without brittle, after-the-fact scraping. Even the file-based memory that production agents have converged on—declarative facts, procedural skills, episodic logs—captures what an agent knows, not the reward-bearing experience graph of what its search tried.