Evaluating Multi-Turn Agents: Beyond Single-Turn Benchmarks
Methodologies for measuring error propagation, goal drift, and recovery rates in autonomous workflows.
Standard static benchmarks fail to capture the reality of multi-turn autonomous agent execution. In real-world environments, an agent’s current decision is conditioned on all prior tool results, environment observations, and dynamic feedback.
Key Metrics for Multi-Turn Evaluation
When evaluating agentic systems, we focus on three core dimensions:
- Recovery Rate: How effectively the agent recovers when a tool returns an error or unexpected output.
- Context Efficiency: The ratio of useful tokens to total tokens consumed across the conversation lifecycle.
- Trajectory Divergence: Measuring when and why an agent strays from the optimal path toward the user’s objective.
Systematic evaluation requires simulated environments that inject controlled noise and latency, testing the resilience of adaptive planning algorithms.