Beyond the Benchmark: How to Evaluate AI Agents in the Real World

A strong benchmark score does not mean an AI agent is ready for production. Once an agent starts using tools, calling APIs, and handling multi-step tasks, quality depends on much more than the model alone. You need to know whether the agent can complete real work reliably, choose the right actions, recover from failures, and behave consistently under realistic conditions. This talk explores practical ways to evaluate AI agents beyond model-level benchmarks. We’ll walk through agent-specific evaluation patterns for task success, tool and MCP correctness, multi-step reliability, latency, and human-reviewed quality. We’ll also examine the failure modes that traditional benchmarks miss and why evaluating agents at scale requires more than isolated scripts or one-time tests. As agents move into production, evaluation becomes a platform problem as much as a model problem. Teams need shared infrastructure for tracing, experiment tracking, repeatable test conditions, and regression analysis. With an AI platform approach and MLflow-based evaluation workflows, it becomes possible to turn agent evaluation into a repeatable engineering loop instead of a collection of ad hoc checks. You’ll leave with a practical framework for measuring whether an agent is actually ready for production.