Banger paper from IBM on decision gates for agents.
They add a runtime gate to tool-using agents and keep the models and tools the same.
JevAct is a check that sits between the agent and its tools. An LLM first turns the domain policy into code checks and yes/no questions. Before a selected tool call runs, the code checks the facts in the tool records, and a Jev-like decision model answers the questions about the conversation. If the call fails a check, it goes back to the agent with feedback so the agent can revise it.
On τ²-bench Telecom, Opus 4.6's success across all three runs rose from 39.5% to 80.7%.
On Telecom, all eight agents improved by 16.9 points on average in three-run success. All nine improved on BFCL. Airline results were smaller and mixed.
They show that the gains come from the harness.
https://arxiv.org/abs/2610.38620
Chat with Paper: https://academy.dair.ai/papers/think-before-you-act-decision-gate-for-llm-agents-2610.38620