Replacing CLAUDE.md with a POMDP State-Action Graph Boosts Agent Success to 95%
Mysterious-Try-1966 · reddit · 2026-07-30
The author notes that while flat markdown files or GraphRAG provide context, they make it hard to pinpoint failures in multi-step actions, leading to slow convergence and a performance ceiling.
To solve this, the agent workflow is modeled as a Partially Observable MDP (POMDP):
- Nodes: States the agent can observe.
- Edges: Actions weighted by success probability, token cost, and outcome.
The agent explores to complete the graph and then exploits it like standard RL. In identical task batches (tested with Claude Opus 4.8 and Codex GPT-5.5), this approach increased the success rate from an average of 0.78 to 0.95, with a lower token cost than the flat file baseline.
Related event: POMDP State Graphs Boost Agent Success Rates(2 posts)→
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