By Sagar Shankaran, Founder of CallSphere
Google's A2A protocol shipped in 2025 to let agents from different vendors talk to each other. Here is what it does, where it differs from MCP, and who is adopting.
Key takeaways
MCP (Model Context Protocol) standardized how an agent talks to a tool. A2A (Agent-to-Agent) standardizes how an agent talks to another agent. They sit at different layers and complement rather than compete. Google open-sourced A2A in mid-2025 with backing from 50+ vendors. By 2026 it is the dominant cross-vendor agent interop spec.
This is what A2A is, what it is not, and how to think about it relative to the rest of the agent stack.
flowchart TB
User[User] --> Host[Host App<br/>Claude Desktop, ChatGPT, Cursor]
Host -->|MCP| Tool[MCP Tool/Server]
Host -->|A2A| Agent[Remote Agent]
Agent -->|MCP| Tool2[Tool]
Agent -->|A2A| Agent2[Sub-agent]
MCP is host-to-tool. A2A is agent-to-agent. An agent can be an MCP host AND an A2A peer at the same time.
A2A defines four primary objects:
/.well-known/agent.json that describes who the agent is, what it can do, what skills it advertises, and how to authenticateThe transport is HTTP with JSON-RPC over either polling or Server-Sent Events for streaming updates.
sequenceDiagram
participant Cli as Client Agent
participant Srv as Server Agent
Cli->>Srv: GET /.well-known/agent.json
Srv-->>Cli: agent card (skills, auth)
Cli->>Srv: POST tasks/send (task, message)
Srv-->>Cli: task accepted (id)
Cli->>Srv: GET tasks/{id}/events (SSE)
Srv-->>Cli: status: working
Srv-->>Cli: artifact: partial result
Srv-->>Cli: status: completed
| Feature | MCP | A2A |
|---|---|---|
| Direction | Host → Tool | Agent → Agent |
| Transport | stdio / SSE / streamable HTTP | HTTP + SSE |
| State | tool calls are stateless | tasks are stateful |
| Discovery | server registries | well-known agent cards + DNS |
| Auth | OAuth 2.1 (extension) | OAuth 2.1 native |
The two are designed to compose. An A2A server agent typically uses MCP servers internally for its own tool calls.
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By Q1 2026 the public adopters include Salesforce (Agentforce), ServiceNow, SAP, Atlassian (Rovo), Box, and dozens of smaller agent platforms. Microsoft's Copilot Studio supports A2A as of February 2026. Anthropic shipped A2A as a Claude plugin standard in March 2026.
The notable holdout: OpenAI's Agent Builder uses its own protocol (closer to function-calling-as-a-service) and has not committed to A2A. The 2026 betting line is that interop will eventually force everyone in.
Three things A2A explicitly leaves to higher-level systems:
These are being addressed by adjacent specs (Agent Communications Protocol, Agent Capability Negotiation), but A2A itself is deliberately narrow.
In 2026 the pattern emerging is "agent as a microservice." A specialist agent (say, a billing-resolution agent) lives at https://billing.example.com with an agent card, exposes its capabilities, and any other agent in the org's catalog can invoke it. Internal teams ship agents the way they used to ship microservices.
flowchart LR
Triage[Triage Agent] -->|A2A| Billing[Billing Agent]
Triage -->|A2A| Returns[Returns Agent]
Triage -->|A2A| Tech[Tech Support Agent]
Billing -->|MCP| Stripe
Returns -->|MCP| WMS
Tech -->|MCP| Sentry
If you are shipping an A2A-compatible agent, a few practical patterns:
/.well-known/agent.json first; clients discover capabilities hereWhen teams move beyond agent-to-Agent Protocol (A2A) Deep Dive, one question shows up first: where does the agent loop actually end? In practice, the boundary is rarely the model — it is the contract between the orchestrator and the tools it calls. The teams that ship fastest treat agent-to-agent protocol (a2a) deep dive as an evals problem first and a modeling problem second. They write the failure cases into the regression set on day one, not after the first incident.
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Agentic AI in a real call center is a different beast than a single-LLM chatbot. Instead of one model answering one prompt, you orchestrate a small team: a router that decides intent, specialists that own a vertical (booking, intake, billing, escalation), and tools that read and write to the same Postgres your CRM trusts. Hand-offs are where most production bugs hide — when Agent A passes context to Agent B, anything that isn't explicit in the message gets lost, and the user feels it as the agent "forgetting." That's why the systems that hold up under load are the ones with typed tool schemas, deterministic state stored outside the conversation, and a hard ceiling on tool calls per session. The cost story is just as important: a multi-agent loop can quietly burn 10x the tokens of a single-LLM design if you let it think out loud at every step. The fix isn't a smarter model, it's smaller agents, shorter prompts, cached system messages, and evals that fail the build when p95 latency or per-session cost regresses. CallSphere runs this pattern across 6 verticals in production, and the rule has held every time: the agent you can debug in five minutes will out-survive the agent that's "smarter" on a benchmark.
Q: What's the hardest part of running agent-to-Agent Protocol (A2A) Deep Dive live?
A: Scaling comes from constraint, not capability. The deployments that hold up keep each agent narrow, cap tool calls per turn, cache the system prompt, and pin a smaller model for routing while reserving the larger model for synthesis. CallSphere's stack — 37 agents · 90+ tools · 115+ DB tables · 6 verticals live — is sized that way on purpose.
Q: How do you evaluate agent-to-Agent Protocol (A2A) Deep Dive before shipping?
A: Hard ceilings beat heuristics. A maximum step count, an idempotency key on every tool call, and a fallback to a deterministic script when confidence drops below a threshold are what keep the loop bounded. Evals that simulate noisy inputs catch the rest before they reach a real caller.
Q: Which CallSphere verticals already rely on agent-to-Agent Protocol (A2A) Deep Dive?
A: It's already in production. Today CallSphere runs this pattern in Healthcare and Real Estate, alongside the other live verticals (Healthcare, Real Estate, Salon, Sales, After-Hours Escalation, IT Helpdesk). The same orchestrator code path serves voice and chat — the difference is the tool set the router exposes.
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Written by
Sagar Shankaran· Founder, CallSphere
Sagar Shankaran is the founder of CallSphere, where he builds production AI voice and chat agents deployed across healthcare, hospitality, real estate, and home services. He writes about agentic AI, LLM engineering, and shipping voice agents that handle real calls in production.
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