Agent Benchmarks
Standardized evaluation suites that measure agent capabilities across tasks like web navigation, coding, tool use, and multi-step reasoning. Benchmarks provide comparable metrics for assessing different agent implementations and model versions.
Agent benchmarks evaluate whole-system performance rather than isolated model capabilities. Suites like SWE-bench (software engineering tasks), WebArena (web navigation), GAIA (general assistant tasks), and ToolBench (tool use scenarios) test agents on realistic, multi-step problems that require planning, tool use, and error recovery.
For teams selecting models or frameworks for agent systems, benchmarks provide objective comparison data. However, interpret results carefully: benchmark performance does not always translate to your specific use case. A model that excels at coding benchmarks might underperform on your customer support workflow. Use public benchmarks as a starting filter, then build custom evaluations that reflect your actual agent tasks, tools, and success criteria. Track your custom benchmark scores over time as you iterate on prompts, tools, and model versions. The most valuable benchmarks test failure modes (how gracefully does the agent handle errors) and efficiency (how many steps and tokens does it take) alongside raw task completion rates.
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.