Agents Working Together › Glossary

Agent Card: A standardized description of an agent's capabilities, inputs, outputs, and communication methods, similar to a business card for AI agents. Used in protocols like A2A to enable agent discovery and interoperability.

Agent-to-Agent (A2A) Protocol: An open standard developed by Google that enables AI agents from different platforms and frameworks to discover each other, understand capabilities, and collaborate through standardized communication.

Consensus Building: A cooperation pattern where multiple agents independently solve the same problem and then compare results to reach agreement, improving reliability and reducing errors.

Coordinator Agent: An agent responsible for orchestrating multi-agent workflows by breaking down complex requests, delegating tasks to specialist agents, and combining results.

Iterative Refinement: A cooperation pattern where agents work in cycles, with one agent generating output and another reviewing and providing feedback, repeating until quality standards are met.

Model Context Protocol (MCP): A framework developed by Anthropic for connecting agents with tools and data sources, complementary to agent-to-agent communication protocols.

Multi-Agent System: A system where multiple AI agents work together, each with specialized capabilities, to accomplish tasks that would be difficult or impossible for a single agent.

Parallel Execution: A cooperation pattern where multiple agents work simultaneously on independent tasks, then combine their results, enabling faster completion of complex requests.

Sequential Handoff: A cooperation pattern where agents work in sequence, with each agent completing its specialized task before passing results to the next agent in the workflow.

Specialization: The practice of designing agents with focused expertise in specific domains or tasks, rather than trying to make one agent handle everything.