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When building agents, decide how your agent interacts with external systems and performs actions beyond simple information retrieval. This article describes three key patterns for implementing tool use and action capabilities in your agent architecture:
- API plugins: Use standardized OpenAPI specifications for simple, static service integrations.
- Model Context Protocol (MCP): Implement dynamic tool discovery with flexible server management.
- Agent-to-agent communication: Enable complex workflows through collaborative AI entity interactions.
Each pattern addresses different architectural needs, from basic API connectivity to sophisticated multi-agent orchestration. Understanding these approaches helps you select the right tool use strategy for your specific requirements and constraints.
API plugin integration
API plugins use OpenAPI specifications to provide standardized interfaces for external service integration. These plugins offer simple, static configurations suitable for straightforward service integration scenarios.
API plugins don't handle multi-round or context-aware interactions by default without developer intervention to store and retain request history. Predefined API definitions established during plugin creation determine response payload trimming or incompatible data handling.
This approach works well for simple, stateless service integrations where consistent request-response patterns meet agent requirements without complex orchestration needs.
Model Context Protocol implementation
The Model Context Protocol (MCP) is an open source protocol optimized for tool use by agents. It presumes the existence of orchestration layers to select appropriate tools during discovery processes. MCP servers present tools without negotiating or adapting to invoking agent limitations.
MCP enables dynamic tool sets to be exposed to agents, reducing developer overhead for updating or adding APIs to agent capabilities. This architecture supports separate ownership models where different developers can maintain MCP servers and included tools independently of agent development teams.
The invoking agent is responsible for accepting or discarding incompatible tool schemas or responses. This approach provides flexibility but requires robust error handling and compatibility checking mechanisms.
Agent-to-agent communication
Agent-to-agent (A2A) protocols enable interactions between multiple AI-enhanced entities where both participants can negotiate and adapt interactions dynamically. The Linux Foundation's A2A represents an open source implementation optimized for complex inter-agent scenarios.
This architecture supports leader-crew agent patterns where multiple entities can negotiate tasks dynamically, including modality changes such as images or video processing. The protocol enables sophisticated coordination between specialized agents to accomplish complex workflows requiring diverse capabilities.
A2A protocols work best for scenarios that require dynamic task distribution, specialized capability access, or complex workflow orchestration that benefits from multiple AI entities working collaboratively.
Learn more: AI agent orchestration patterns