Agent Interoperability: Can AI Agents Work Across Different Platforms?
An organization might use one agent to search internal policies, another to manage calendars, and a third to coordinate procurement requests. Each can perform its own job well, but when they need to exchange information or hand work to one another issues can arise.
Different platforms often describe capabilities, permissions, and task status in their own way. Agent interoperability aims to give independently developed agents a common method for discovering one another, delegating work, exchanging results, and reporting progress.
A shared protocol can establish the connection, but it cannot resolve every problem. Reliable collaboration also depends on common meanings, clear authorization, trusted identities, and accountability for the actions each agent takes.
What Interoperability Requires
Two agents need more than a network connection. They must agree on several parts of the exchange:
Discovery: One agent needs a way to find another and understand its capabilities.
Task exchange: Both sides must recognize instructions, status updates, cancellations, and completed work.
Content formats: Files, structured data, and messages need formats that both systems can process.
Identity: Each agent must establish which organization, application, or user it represents.
Authorization: The receiving system needs to know which resources and operations are permitted.
Failure handling: Both sides must know what to do when a request times out, fails, or returns an incomplete result.
Without shared conventions, developers often need custom integration code. Maintaining those integrations can become harder as agents, platforms, and capabilities change.
MCP and A2A Address Different Connections
The Model Context Protocol, commonly called MCP, and the Agent2Agent Protocol, or A2A, address different parts of this problem.
MCP standardizes how an AI application connects to tools, resources, and reusable prompts exposed by an MCP server. It can give an agent a consistent way to work with a database, code repository, search service, or business application.
A2A focuses on communication between agents. Its specification covers agent discovery, messages, tasks, progress updates, cancellations, and output artifacts. An agent can accept work and return a result without requiring the requesting agent to understand how that work is performed internally.
In a larger workflow, MCP might connect an agent to the tools it needs, while A2A helps that agent delegate work to another agent. The protocols can appear in the same architecture, but they address different connections. The A2A specification gives a concrete structure for this type of cross-platform exchange.
Shared Syntax Does Not Guarantee Shared Meaning
A protocol can define the shape of a message without ensuring that both agents interpret it the same way. Consider the instruction “review this contract.” One agent might check formatting and missing fields, while another performs legal-risk analysis. Both could complete the request successfully according to their own definitions and still produce the wrong result for the user.
Capability descriptions need to explain what an agent does, which inputs it accepts, what it returns, and where its limits lie. Structured schemas reduce ambiguity, while shared domain vocabularies help align terms such as approval, completion, and classification.
The receiving system also needs to understand where a result came from. Useful provenance may include the agent that created an artifact, the sources it consulted, when the work occurred, and which policies governed the task. A response can be technically valid and still be difficult to trust when that context is missing.
Delegation Creates a Security Boundary
When one agent delegates a task, the receiving agent should not inherit every permission available to the sender. Reviewing one document does not, by itself, justify access to an entire repository.
Credentials should be limited to the resources and actions required for the task. The delegation context should identify the agents involved, the user or service being represented, the permitted operations, and any expiration or usage limits. Trusted identity and policy services may carry or maintain this information.
Authorization checks are needed each time work crosses a boundary. That includes the initial handoff, any tool calls made by the receiving agent, and further delegation to another system. A valid request at the beginning of a workflow does not automatically authorize every action that follows.
Cross-organizational workflows add another complication. An external agent may follow different rules for data retention, logging, or residency. Security requirements need to be agreed upon. They can be established through configuration, organizational agreements, or task metadata. The receiving system must reject work that conflicts with its local policies.
NIST’s AI Agent Standards Initiative reflects these concerns. The initiative supports agent standards, community-led open-source protocol development, and research into agent security and identity.
Testing Cross-Platform Workflows
A successful message exchange proves only that two systems can communicate. It does not show that the correct agent was chosen, permissions were enforced, progress reports were accurate, or the final result satisfied the original request.
Contract tests can verify message schemas and task states. Simulated agents can introduce delays, malformed responses, authorization failures, and partial results. End-to-end evaluations can then measure completion rates, delegation accuracy, latency, cost, and recovery behavior across the entire workflow.
Production systems should also preserve enough information to reconstruct a failure’s sequence of actions. Logs should show which agent accepted the task, which permissions were granted, what tools were called, how the task changed, and where the final result originated.
Production Takeaways
Agent interoperability requires several layers working together:
Standardize capability discovery and task exchange.
Define clear expectations for inputs, outputs, and task completion.
Preserve identity, provenance, and delegation history.
Limit permissions to the current task and required resources.
Test failure handling across platform boundaries.
Keep people accountable for consequential actions.
AI agents can work across different platforms when communication standards are supported by clear meanings, scoped authority, and reliable operational controls. A protocol can carry a task from one system to another. The surrounding architecture determines whether that task remains understandable, authorized, and accountable throughout the journey.