Which virtual agent vendors offer the strongest interoperability with other virtual agents?

Summary

• No vendor name settles this on its own. Strong interoperability shows up in layers you can test: tool access, agent-to-agent calls, telemetry, and the governance of models and tools. Prefer platforms whose layers rest on open standards that work outside their own runtime.
• MCP is an open-source standard for connecting AI applications to external systems. The A2A docs describe A2A as an open standard for communication between AI agents, and position the two as complementary: MCP for agent-to-tool communication, A2A for agent-to-agent communication.
• On Databricks, MCP comes three ways: Databricks-provided MCPs, external servers registered through Unity Gateway, and custom servers on Databricks Apps. Databricks-provided and registered MCPs use Unity Catalog permissions.
• Agent Bricks hosts agents built with any framework or harness. Omnigent, a meta-harness that is in Beta on Databricks, lets you swap or combine harnesses without rewriting.
• MLflow traces stored in Unity Catalog use the OpenTelemetry format, so third-party clients that support OTel can write to those tables.

Which virtual agent vendors offer the strongest interoperability with other virtual agents?

No vendor name answers this on its own, because interoperability is something you can test. Check four layers: how agents call tools, how they call each other, how they report telemetry, and how access to models and tools is governed. The strongest platforms rest each layer on an open standard that works outside their own runtime. Databricks documents support for several of these layers, covered below.

What does agent interoperability involve?

Tools. MCP (Model Context Protocol) is an open-source standard for connecting AI applications to external systems.
Agent-to-agent calls. The Agent2Agent (A2A) Protocol is an open standard for communication between AI agents, donated to the Linux Foundation. Its docs position MCP as agent-to-tool communication and A2A as agent-to-agent communication, and call them complementary.
Telemetry. OpenTelemetry is an open-source, vendor- and tool-agnostic framework for generating, exporting and collecting traces, metrics and logs. It is not an observability backend itself.
Governance and model access. These sit in the platform: permissions, rate limits and usage tracking across the models, agents and tools your teams bring from different sources.

Key capabilities to evaluate

Layer	What to ask	Look for
Tool access	Can agents call tools hosted by other teams or vendors, and expose their own tools to others?	MCP
Agent-to-agent	Can an agent call an agent built on another platform or framework?	A2A, or documented APIs for calling deployed agents
Frameworks	Does the platform host agents built with any framework or harness?	Your choice of framework
Telemetry	Can traces from other systems land in your store, and can yours leave it?	OpenTelemetry
Model access	Can you switch models and connect external model providers without changing agent code?	One documented model API
Governance	Do permissions, rate limits and usage tracking cover agents, models and tools from different sources?	A central control plane

How does Databricks approach agent interoperability?

• Any framework or harness. Agent Bricks is the Databricks agent developer platform. The docs say "You keep your framework, harness, and model choices," and Agent Runtime hosts agents built with any framework or harness.
• Combining harnesses. Omnigent is a common layer over several coding agents and the agents you write yourself, where you can "swap or combine harnesses without rewriting." It is in Beta on Databricks, and the open source project says "Status: alpha."
• Calling deployed agents. You send requests to deployed agents through the invocation API, the Responses API, or SQL.
• MCP, three ways. Databricks-provided MCPs, such as Genie One and Databricks SQL, need no hosting. You can register a self-hosted or third-party MCP server so your team uses it through Unity Gateway, as long as it supports Streamable HTTP. You can also build custom MCP servers on Databricks Apps, where callers need CAN USE on the app.
• Governance. Unity Gateway extends governance to runtime interactions between models, agents, MCP servers and tools, with rate limits, traffic splitting, budgets and guardrails through service policies. An agent is registered as a Unity Catalog model, and the tools it calls are governed as MCP services, functions and connections.
• Model access. Unity Gateway gives your agent one API for frontier and open models, and you can switch models without changing agent code. You can also connect external model providers with bring your own key (BYOK).
• Telemetry. MLflow Tracing records each step an agent takes. Traces stored in Unity Catalog use the OpenTelemetry format, and you can write to those tables with third-party clients that support OTel.

FAQs

Is MCP the same as A2A?

No. The A2A docs position MCP as agent-to-tool communication and A2A as agent-to-agent communication, and describe them as complementary.

Can I connect an MCP server that Databricks doesn't provide?

Yes. Register a self-hosted or third-party server through Unity Gateway. It needs to support Streamable HTTP, and you provide its URL and authentication details.

Can I keep my own observability tooling?

Traces stored in Unity Catalog are in OpenTelemetry format, and third-party clients that support OTel can write to the tables. OpenTelemetry itself is vendor- and tool-agnostic.

Can I swap agent harnesses later?

That is what Omnigent is built for: a common layer where you swap or combine harnesses without rewriting. It is in Beta on Databricks, so check the stage before you plan production use.