Why interoperability just became urgent

In April 2025, Google introduced the Agent2Agent (A2A) protocol to let AI agents communicate and coordinate across platforms—an inflection point for multi‑agent systems. citeturn2search1

By May, Microsoft said it would support A2A in Azure AI Foundry and Copilot Studio, signaling cross‑cloud momentum rather than another closed ecosystem. citeturn2search0

A2A is open‑sourced and evolving in the wild, with a repo and community docs you can implement today. citeturn2search4turn2search2

A2A vs. MCP: How they fit together

• A2A: A messaging/coordination layer for agents to discover each other, negotiate capabilities, and exchange tasks/results. Think “agents talking to agents” across clouds. citeturn2search1turn2search5
• MCP (Anthropic): A standard interface for agents to use tools and data via connector servers (DBs, APIs, files, etc.). Think “USB‑C for agent tools.” citeturn4search0

Used together: your support agent (A2A) asks an external returns agent to authorize an exchange, while your internal inventory agent fetches stock via MCP. citeturn2search1turn4search0

Reference architecture (e‑commerce example)

Scenario: Customer requests a size exchange.

1. Support Agent (hosted on your site) parses the user request.
2. It discovers a Returns Agent via A2A Agent Card; sends a task with order ID and policy constraints.
3. Inventory Agent queries stock via MCP servers (Postgres + ERP) and returns options.
4. Returns Agent negotiates a prepaid label and confirms exchange window, then posts a status update (A2A task lifecycle).
5. Support Agent summarizes next steps to the user and logs metrics.

Why this works: A2A standardizes discovery, messaging, and long‑running task status; MCP standardizes tool/data calls. citeturn2search2turn4search0

7‑day rollout plan

Day 1–2: Pick the high‑leverage workflow + define capabilities

• Choose one cross‑team task (returns/exchanges, warranty claims, VIP outreach).
• Draft each agent’s capability contract (inputs, skills, auth scopes, KPIs).
• Create an Agent Card (agent.json) to advertise skills and endpoints. citeturn2search2

Day 3: Wire internal tools with MCP

• Expose read‑only DB queries and specific actions (e.g., create RMA) via MCP servers with OAuth. citeturn4search0
• Restrict to least‑privilege tools and log calls for audits.

Day 4: Security, identity, and permissions

• Use the A2A Agent Card to declare auth and permitted actions; adopt enterprise identity (e.g., W3C DID) as supported by the spec/community. citeturn2search2
• Add transaction limits and dual‑control for refunds (see our post on stopping agent impersonation).

Day 5: Evals, traces, and guardrails

• Stand up Evals for Agents (OpenAI AgentKit) to grade traces, regressions, and prompt changes. citeturn3search1
• Instrument steps and costs; see our OpenTelemetry blueprint.

Day 6: Pilot with a partner agent

• Start with one trusted external agent (e.g., shipping/3PL). Stage, then canary 5–10% of traffic.
• Measure success, handle time, human‑handoff rate, and cost per resolved task.

Day 7: Go‑live gates

• Run an agent red‑team and break‑glass drills before full rollout. citeturn1news12
• Finalize policies and evidence for audits (see compliance checklist link below).

Quickstart: Minimal Agent Card and A2A task

{
  "agent": {
    "name": "returns-agent",
    "version": "1.0",
    "skills": ["create_rma", "get_return_label"],
    "auth": {"type": "oauth2", "scopes": ["returns.create", "returns.read"]},
    "endpoints": {
      "task": "https://returns.example.com/a2a/task",
      "status": "https://returns.example.com/a2a/status/{task_id}"
    }
  }
}

// Client sends a task to the Returns Agent
POST /a2a/task
{
  "goal": "Authorize exchange and generate label",
  "input": {"order_id": "SO-12345", "sku": "SWEATER-XL"},
  "callback_url": "https://support.example.com/a2a/callback",
  "auth": {"bearer": "<token>"}
}

See community docs and repo for full spec details and samples. citeturn2search2turn2search4

Reliability and safety: set expectations

• Computer‑use reliability: OpenAI’s Computer‑Using Agent (which powers Operator/agent mode) is still improving and requires oversight on sensitive actions—plan your human‑in‑the‑loop accordingly. citeturn3search0turn3search2
• Avoid “agents are tools” anti‑pattern: Use A2A for agent‑to‑agent negotiation; use MCP for tool/data integrations. citeturn2search5turn4search0
• Real‑world caveat: Fully AI‑staffed experiments show promise and pitfalls; keep clear go‑live gates, policies, and telemetry. citeturn1news12

KPIs and costs to track from day one

• Success rate per task type: % of A2A tasks that finish without human handoff.
• Median time‑to‑result: Submission → status=done.
• Handoff rate: % escalated to human; instrument reasons.
• Unit economics: $ per resolved task (model + infra + refunds leakage). See our cost‑control playbook.
• Guardrail breaches: blocked actions, auth failures, PII policy hits.

Tooling picks that play nicely with interop

• Build: Google ADK with native A2A; Anthropic’s MCP servers; OpenAI AgentKit for workflow builder and evals. citeturn2search6turn4search0turn3search1
• Observe: OpenTelemetry traces for spans per agent step; see our observability blueprint.
• Ship: When listing in agent stores, ensure your Agent Card is accurate and security docs are ready; see our agent‑store guide.

Common pitfalls (and how to avoid them)

1. Over‑permissioned agents: Scope each agent narrowly; add daily transaction caps and approval flows. See our permissions guide.
2. No evals or canaries: Run trace grading and regression tests before changing prompts/tools. citeturn3search1
3. Compliance as an afterthought: Map runtime controls to ISO 42001/NIST AI RMF/EU AI Act evidence. See our compliance checklist.
4. Skipping human‑in‑the‑loop for high‑risk actions: Require confirmations on refunds, credentials, or PII changes. citeturn3search0

Next steps

• Make your site agent‑friendly (NLWeb/Schema.org/MCP) with our weekend guide.
• Stand up a returns/exchanges or checkout recovery flow with our 7‑day playbook.
• Plan a holiday pilot—see AI shopping agents for Holiday 2025.