Quick Answer
A2A (Agent2Agent) is Google's open protocol, now governed by the Linux Foundation's Agentic AI Foundation, that lets AI agents built on different frameworks discover each other's capabilities and delegate tasks over standard web transport. The protocol standardizes discovery and task handoffs, but it does not make those handoffs auditable on its own. Your system must record every delegation, state transition, and completion claim so an engineer can identify which agent accepted responsibility for a task.
Introduction
A2A protocol is not a payments term or a proprietary API contract. It is an open standard Google announced in April 2025 for letting AI agents from different vendors discover, delegate to, and coordinate with each other without a bespoke connector for every agent pair. Google donated the specification to the Linux Foundation in June 2025, and in August 2026 the project joined the Agentic AI Foundation alongside the Model Context Protocol, consolidating governance for the two standards that now anchor multi-agent interoperability. That history matters because an agent can be fully A2A-compliant and still produce an untraceable outcome if your system trusts a reported task status instead of verifying it.
Key Takeaways:
A2A standardizes agent discovery and task delegation, not accountability for the work performed.
Every delegation needs an immutable record of the agent card, task state, and accepting party.
A2A and MCP solve different problems and are increasingly deployed together.
How the A2A Protocol Moves a Task Between Agents
An A2A exchange usually starts when a client agent fetches another agent's Agent Card, a JSON document published at a well-known endpoint that describes the agent's skills, authentication requirements, and supported interaction modes. The client then sends a task, and the two agents exchange messages over HTTP, Server-Sent Events, and JSON-RPC 2.0 as the task moves through defined lifecycle states. Teams building agentic AI systems beyond coding should treat each of those states as a real system boundary, not a formality between two friendly services.
Where an A2A handoff becomes an accountability boundary
Every handoff should change the record from "task sent" to "responsibility accepted," with a durable event written before either agent proceeds. A signed Agent Card is not proof that the receiving agent will complete the task correctly, and a "completed" status is not proof that the underlying work was verified. The useful question is always concrete: which agent claimed which state, on whose authority, and what evidence can reproduce that claim later?
Task ID: Bind every message exchange to one delegated task.
Agent Card reference: Record which signed card authorized the receiving agent.
State transition log: Capture submitted, working, input-required, completed, failed, canceled, and rejected events.
Correlation ID: Connect requests, streamed updates, and downstream actions.
Actor record: Identify the specific agent instance accepting each task.
Discovery is not execution guarantee
An Agent Card can establish that an agent claims a capability, but it does not collapse every later processing state into success. Your system should model task submission, in-progress work, input requests, completion, failure, and cancellation as distinct states with permitted transitions enforced in code. That is where API design for AI agents matters: contracts must make retries, duplicate delivery, and out-of-order events survivable instead of silently destructive.
Who Audits Agent Handoffs in a Multi-Vendor A2A System
The engineering team owns the audit design, even when a partner agent platform provides its own logs. Third-party records can support an investigation, but they cannot replace your internal timeline because they do not know what your system requested, queued, retried, or acted on after receiving a response. In a production multi-agent system, accountability must be assigned to named services and operational owners, not to the protocol itself.
Build an evidence chain instead of a request log
A request log captures activity. An evidence chain captures causality, including the signed agent card presented, the task payload sent, the authenticated principal on each side, the policy decision to delegate, every state transition received, and the downstream action taken on a completed result. Platforms built for high-stakes research hold themselves to that same evidentiary bar; regulatory compliance software is judged on whether every conclusion traces back to a source an auditor can independently check, which is the same standard an A2A delegation chain should meet before a business treats a task as done.
The comparison below separates A2A from the other interoperability protocols engineers now have to choose between.
Protocol | Problem it solves | Governance | Status in 2026 |
|---|---|---|---|
A2A | Agent-to-agent discovery and task delegation across vendors | Agentic AI Foundation, Linux Foundation | v1.0, 150+ supporting organizations |
MCP | Agent-to-tool and agent-to-data access | Agentic AI Foundation, Linux Foundation | Widely adopted alongside A2A |
ACP | IBM's original agent communication approach | Merged into A2A | No longer a separate spec |
AGNTCY | Agent discovery and identity | Cisco-led initiative | Overlapping, still separate from A2A |
Do not assume one protocol is inherently safer because it has fewer visible intermediaries. The reliable choice is the one whose task lifecycle, evidence retention, and delegation model match the workflow your product actually runs.
The consolidation happening around A2A is a useful reminder that agent interoperability is still an active engineering problem, not a solved one: the protocol reached v1.0 in March 2026 with multi-tenancy support and cryptographically signed agent cards, then joined the Agentic AI Foundation alongside MCP five months later. Agent2Agent joining the Agentic AI Foundation consolidated governance with MCP under one body, while a competing specification, Cisco's AGNTCY, still addresses overlapping ground in discovery and identity.
Verification must close the loop
Verification should compare a receiving agent's claimed completion state against independently observable evidence and route unresolved claims into a review queue. That requires an honest approach to distributed systems: streamed updates can arrive out of order, a task can be marked complete before downstream effects are visible, and a status can change after your workflow has already acted on an earlier state. The audit record must show the original claim and every correction, rather than overwriting history.
A2A Versus MCP and Other Agent Interoperability Protocols
A2A versus MCP is not a contest between competing standards. They solve different layers of the same problem. MCP standardizes how a single agent accesses tools and data sources, while A2A standardizes how separate agents discover each other and hand off tasks, and production systems increasingly need both working together.
What engineers should compare before adopting a protocol
Compare the full delegation lifecycle, not just the initial handshake. Ask whether the agent card is cryptographically signed, whether task status is push-based or polled, whether multi-tenancy isolation is documented, and whether a completed task exposes enough evidence to verify the claim without re-running the work. Weighing these tradeoffs deserves the same discipline behind how senior engineers choose architecture generally, not a checklist applied once and forgotten.
The pace of change here is real: A2A moved from initial release to a production-ready standard with over 150 supporting organizations in about a year, with IBM's competing Agent Communication Protocol merging into A2A in August 2025 rather than persisting as a rival spec. That consolidation reduces the number of standards engineers need to evaluate, but it does not reduce the need to verify what a specific agent actually did once a task is marked complete.
Production requirements that should be non-negotiable
Demand signed Agent Cards, documented task-state transitions, versioned error handling, retrievable delegation histories, and an audit export that maps every task ID to the agents involved and the evidence behind each state change. Those are practical requirements for services that communicate at scale, not paperwork. When evaluating any agent platform your organization delegates work to, require proof that agent identity, task authority, and event integrity survive every handoff.
Conclusion
A2A deserves the same skepticism engineers apply to any distributed system: no agent gets to declare a task successful without evidence. Model task states explicitly, preserve immutable delegation records, and verify completion claims against your own evidence rather than trusting a reported status. The protocol's consolidation under the Agentic AI Foundation alongside MCP shows the ecosystem is still settling, which makes internal audit discipline more important, not less. DevvPro frames the engineering questions that turn agent interoperability into systems you can actually operate.
Need a sharper review of your agent integration assumptions? Explore DevvPro for practitioner-focused engineering analysis.
Frequently Asked Questions (FAQs)
What is the A2A protocol in AI agent systems?
The A2A protocol is an open standard, created by Google and now governed by the Linux Foundation's Agentic AI Foundation, that lets AI agents built on different frameworks discover each other's capabilities through signed Agent Cards and delegate tasks using a shared lifecycle over standard web transport.
How does A2A differ from MCP?
A2A differs from MCP because A2A standardizes how separate AI agents discover and delegate tasks to each other, while MCP standardizes how a single agent connects to tools and data sources, and the two protocols are commonly deployed together rather than as alternatives.
What are the security implications of using A2A protocols?
The security implications of using A2A protocols include verifying signed Agent Card authenticity, enforcing multi-tenancy isolation, validating task authority before acting on a delegated result, and preventing a compromised or misconfigured agent from claiming a completion state that was never independently verified.
How do developers audit A2A task handoffs?
Developers audit A2A task handoffs by joining the signed agent card, task ID, request, streamed state transitions, and downstream action with consistent correlation identifiers, then reviewing whether each completion claim has a verified source and an accountable receiving agent.
How to debug A2A task failure states?
To debug A2A task failure states, trace the task ID from the original delegation through every streamed state update, then distinguish a canceled task, a rejected task, a transport failure, an out-of-order update, or a false completion claim before retrying or escalating.
What technical challenges exist in A2A implementation?
The technical challenges in A2A implementation include verifying agent identity through signed cards, handling asynchronous and out-of-order task updates, enforcing multi-tenancy boundaries, and building an audit trail that survives a completion claim from an agent your system does not directly control.
About the Author
Marcus Rhee is a Developer Advocate and Tech Strategist focused on APIs, developer tools, SaaS architecture, and software-driven business models. His work connects implementation details with the operational decisions that determine whether a system remains reliable after launch.