AI Agent Orchestration: 5 Ways to Evaluate Platforms

Enterprises are increasingly relying on AI agent orchestration platforms to manage complex workflows. These platforms coordinate various AI agents, tools, data, and people into multistep processes. They are crucial for organizations scaling from a few to thousands of AI agents in production. Two open standards, MCP and A2A, facilitate this connectivity. MCP provides governed access to tools and data, while A2A allows agents to discover and delegate to each other. The orchestration layer adds routing, shared state, guardrails, governance, security, and observability for workflows ranging from fully autonomous to human-in-the-loop. One key consideration when evaluating these platforms is observable control, oversight, and trust. This involves how administrators implement controls and guardrails over agent interactions and when human involvement is required. Heather Richards, global vice president at Verint, emphasizes the importance of clear controls over autonomous decision-making and built-in governance. The bottom line is that choosing the right AI agent orchestration platform is vital for managing risk and maximizing value in your AI operations.