6 capabilities to evaluate in AI agent platforms.
Support for Model Context Protocol servers, enabling standardized tool and data source integrations.
Must explicitly support MCP protocol or demonstrate compatibility with MCP servers. Look for MCP-specific documentation or integrations.
Systems that coordinate multiple specialized agents using graph-based workflows or decision trees.
Must support multiple agents working together with defined relationships and handoff logic. Look for visual workflow representations or agent collaboration features.
Support for both delegating tasks to sub-agents while maintaining control, and fully handing off conversations to specialized agents.
Must demonstrate both patterns - delegation (supervisor remains involved) and handoff (full transfer of control). Should show clear examples of each.
Direct communication channels between agents without human intervention, enabling collaborative problem-solving.
Must show agents communicating directly with each other, sharing context, or collaborating on tasks. Should be more than just sequential workflows.
Individual authentication and authorization systems for each agent, allowing different access levels and API keys.
Must allow different agents to have different credentials, API keys, or access permissions. Should support credential isolation and management.
Detailed logging and tracing of agent actions with visual interfaces and industry-standard telemetry.
Must provide visual trace interfaces showing agent decision-making and support OpenTelemetry standards for observability.
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