Agent Evaluation
Systematic methods for measuring agent performance including task completion rate, accuracy, latency, cost, and user satisfaction. Agent evaluation is more complex than model evaluation because it must assess multi-step reasoning and tool use.
Agent evaluation goes beyond traditional model benchmarks because agents exhibit emergent behaviors across multiple steps. A model might score well on individual reasoning tasks but fail when those tasks are chained together in an agent loop. Evaluation must cover end-to-end task success, intermediate step quality, tool selection accuracy, error recovery behavior, and resource efficiency.
For production agent systems, establish evaluation at three levels. Unit-level evaluation tests individual capabilities like tool calling accuracy and output formatting. Integration-level evaluation tests complete workflows against golden datasets with known correct outcomes. System-level evaluation measures real-world performance through user satisfaction metrics, task completion rates, and cost per successful outcome. Build evaluation into your CI/CD pipeline so agent regressions are caught before deployment. The most common mistake is evaluating only the final output without examining the intermediate steps, which hides inefficiencies and fragile reasoning chains that will eventually cause production failures.
Related Terms
Model Context Protocol (MCP)
An open standard that defines how AI models connect to external tools, data sources, and services through a unified interface. MCP enables agents to dynamically discover and invoke capabilities without hardcoded integrations.
Tool Use
The ability of an AI model to invoke external functions, APIs, or services during a conversation to perform actions beyond text generation. Tool use transforms language models from passive responders into active problem solvers.
Function Calling
A model capability where the AI generates structured JSON arguments for predefined functions rather than free-form text. Function calling provides a reliable bridge between natural language understanding and programmatic execution.
Agentic Workflow
A multi-step process where an AI agent autonomously plans, executes, and iterates on tasks using tools, reasoning, and feedback loops. Agentic workflows go beyond single-turn interactions to accomplish complex goals.
ReAct Pattern
An agent architecture that interleaves Reasoning and Acting steps, where the model thinks about what to do next, takes an action, observes the result, and repeats. ReAct combines chain-of-thought reasoning with tool use in a unified loop.
Chain of Thought
A prompting technique that instructs the model to break down complex problems into sequential reasoning steps before producing a final answer. Chain of thought significantly improves accuracy on math, logic, and multi-step tasks.