// Automata from agent traces //

How much of your agent's behavior comes from the model, and how much from the harness you wrapped around it?

New work collapses an entire corpus of agent traces into a single compact finite-state machine. Across twelve public datasets the induced machines run 7 to 43 states, replay held-out data at 0.997 fitness with near-identical topology across splits, and build in milliseconds.

FSM-state context beats Agent Workflow Memory on every ground-truth-matched dataset for next-step prediction. Per-state behavioral features reach held-out AUROC up to 0.94 for failure prediction, and an online monitor ranks failing runs above passing ones from a partial trace, triggering early stopping well before completion.

The authors suggest that behavioral topology gets shaped more by the deployment harness than by the LLM underneath it.

Paper: https://arxiv.org/abs/2608.23670

Chat with Paper: https://academy.dair.ai/papers/automata-from-agent-traces-2608.23670