Traces are the universal substrate for agent learning — all three layers consume the same execution logs

Chase’s key unifying claim: “All of these flows are powered by traces — the full execution path of what an agent did.” The same traces feed three different improvement loops: model training (collect traces, train with Prime Intellect), harness optimization (give a coding agent access to traces via LangSmith to suggest harness changes), and context learning (extract insights from traces to update memory/skills at the agent, user, or org level).

This elevates Decision traces are the missing data layer — a trillion-dollar gap from a storage problem to a learning infrastructure problem. Traces aren’t just records — they’re the raw material that every improvement pathway consumes. It also explains why AI trace data has an indefinite useful lifespan — SaaS observability's 30-day retention model destroys institutional knowledge matters at a systems level: if you delete traces after 30 days, you’ve cut off the input to all three learning loops simultaneously. The Meta-Harness paper Chase references makes this concrete — it runs tasks, evaluates them, stores logs, then uses a coding agent to propose harness changes from those logs. This is exactly the pattern Traces not scores enable agent improvement — without trajectories, improvement rate drops hard describes: without the full trajectory, improvement rate drops hard.