MCP, A2A, and ANP are not rival choices. They are a complementary stack for three different jobs, each stewarded by a different group: MCP (Anthropic, donated to the Linux Foundation) handles agent-to-tool access, A2A (Google) handles agent-to-agent delegation, and ANP (an open-source community) handles decentralized agent identity across organizations using W3C DIDs. You will likely run more than one, layered, and the choice between them is reversible. According to Atlan (2026), roughly 95% of AI agents fail to scale for lack of governed context, not connectivity, and MCP has already crossed 97M+ monthly SDK downloads, so the protocols themselves are settling fast.
The one decision that is hard, durable, and unsolved by any protocol is the governed Context Layer for AI underneath all three. The protocol is the pipe. It does not govern whether what flows through it is certified, current, or correct. Platforms such as Atlan, alongside the protocol stewards at Anthropic, Google, and the ANP community, treat the protocol as the connection and the context layer as the trust layer that decides whether an agent’s answer is right.
Why choosing a protocol is the wrong first question
Permalink to “Why choosing a protocol is the wrong first question”Protocol selection is the easy, reversible part of building an agent stack, and the durable bet is the governed Context Layer for AI underneath all three. Almost every guide on MCP, A2A, and ANP resolves to the same conclusion, that the protocols are complementary rather than competing, and then stops. According to the arXiv survey of agent interoperability protocols (Ehtesham et al., 2025), these are distinct standards for distinct jobs, and practitioner write-ups agree that a real agent stack probably needs more than one of them. None of them challenges the premise that picking a protocol is the first decision.
The protocols compose, and that is what makes the choice reversible. An A2A agent calls MCP to fetch a tool or context, so MCP lives inside each agent and A2A runs between agents. This layering is the architectural default for multi-agent systems, which means you are rarely choosing one protocol instead of another. You are deciding which jobs you have today and adopting the matching protocol as each job appears.
The harder question sits one level down. The protocol governs the connection, not the trustworthiness of what flows through it. MCP is the pipe, and context quality is what flows through it. Two protocol-compliant agents can still return contradictory answers because the catalog underneath was never certified, current, or correct. That is why the durable decision is the governed enterprise Context Layer for AI, where lineage, ownership, and policy travel with every query. For the two-way detail, the MCP vs A2A deep dive covers that pair specifically.
MCP vs A2A vs ANP: the decision matrix
Permalink to “MCP vs A2A vs ANP: the decision matrix”The three protocols differ by the job they do, who governs them, their transport, their identity model, their degree of centralization, and their 2026 maturity, and the final row is the decision no protocol makes for you. Keep the per-protocol detail light here and go deep through the linked pages: What is Model Context Protocol for MCP, Google A2A protocol for A2A, and agent interoperability protocols for the full set. MCP standardizes how an agent reaches a tool or data source. A2A standardizes how one agent delegates structured work to another. ANP standardizes how agents from different organizations authenticate each other without a shared broker.
| Dimension | MCP (Model Context Protocol) | A2A (Agent2Agent) | ANP (Agent Network Protocol) |
|---|---|---|---|
| The job it does | Agent to tool / data access (vertical) | Agent to agent delegation and coordination (horizontal) | Agent to agent across organizations / open internet |
| Created / governed by | Anthropic (Nov 2024); donated to Linux Foundation / Agentic AI Foundation, Dec 2025 | Google (Apr 2025); broad framework support | Open-source community; W3C-aligned |
| Transport / shape | JSON-RPC client and server; tool + resource exposure | HTTP + Server-Sent Events; capability-based Agent Cards | DID documents over HTTPS; JSON-LD; meta-protocol negotiation |
| Identity / trust model | Host-mediated; OAuth2 / bearer / mTLS at the connection | Authenticated Agent Cards; OAuth / OIDC | Decentralized W3C DIDs (did:wba); cryptographic mutual auth, no central authority or shared API keys |
| Centralized vs decentralized | Centralized (host controls connections) | Centralized-ish (enterprise / known agents) | Decentralized (trustless, cross-network) |
| Maturity / adoption (2026) | Production standard; 97M+ monthly SDK downloads; ~9.6K registry servers | Production-credible; Google-backed; common in enterprise multi-agent stacks | Experimental; not yet production; matters as ecosystems cross org boundaries |
| Use it when… | Your agent needs to call a tool, query a database, or read external data | One agent must hand structured work to another agent (possibly another vendor’s) and track the result | Agents from different companies must authenticate without sharing infrastructure or a broker |
| What it does NOT solve | Whether the data returned is certified, current, correct | Whether two agents share the same definitions / truth | Whether the exchanged content is governed, accurate, trustworthy |
| The durable decision (all three) | A governed Context Layer for AI underneath every protocol: lineage, ownership, certification, and policy rules travel with each query. No protocol provides this, and it is needed from day one regardless of which protocols you run. |
The identity and centralization rows are where this 3-way view adds what the 2-way comparison cannot. MCP and A2A both lean on centralized, host-mediated trust through OAuth or OIDC, while ANP introduces decentralized W3C DID identity so agents from different companies can verify each other cryptographically with no shared authority. According to the ANP technical white paper (arXiv 2508.00007, 2026), each did:wba identifier maps to an HTTPS-hosted DID document, which removes the need to share API keys or route through a third-party identity provider. One related option sits outside this matrix: ACP, the Linux Foundation and IBM-BeeAI REST standard, handles many internal frameworks behind a single ingress.
When should you use MCP vs A2A vs ANP?
Permalink to “When should you use MCP vs A2A vs ANP?”You route by the job in front of you, not by ranking, and you adopt each protocol as its job appears rather than deploying all three on day one. There is no single best agent protocol, so the common “which is best in 2026” question is mis-specified. Practitioner guidance is direct on this point: for most setups today you only need MCP, and you reach for the others when a specific new job shows up. The table below maps situations to the protocol that fits.
| Your situation | Reach for | Why |
|---|---|---|
| An agent needs to query a warehouse, call an API, or read a document | MCP | Standardizes tool and data access (N+M, not N×M) |
| You add a second specialized agent that must delegate work to the first | A2A + MCP | A2A coordinates the agents; each still uses MCP for tools and context |
| Agents from different companies must trust each other with no shared broker | ANP (emerging) | Decentralized DID identity across organizational boundaries |
| Any of the above, in production, where answers must be correct | Governed Context Layer for AI | Lineage, ownership, certification, and policy travel with every query |
It is worth steel-manning the one source that looks like a ranking. According to Ehtesham et al. (arXiv 2505.02279, 2025), agent protocols arrive in a four-stage order as systems scale, from MCP for tool invocation through to ANP for open agent markets. Read that as a sequence of when each job becomes relevant, not as a verdict on which protocol is best, and note that the survey says nothing about governed context at all. The practical takeaway holds in either reading: route by job, do not assume everyone needs all three today, and treat the governed context layer as required from day one no matter how many protocols you run.
What do agent protocols not solve?
Permalink to “What do agent protocols not solve?”Protocols govern the connection, not whether what flows through is certified, current, or correct, and that gap is why agents fail in production. According to Atlan (2026), roughly 95% of AI agents fail to scale because of missing governance and context rather than missing connectivity. A perfectly compliant protocol call can still return stale or contradictory data, because the protocol never inspected the trustworthiness of the source.
Communication is not the same as shared truth. Two protocol-compliant agents can produce conflicting outputs when they use different definitions of the same term, and no protocol provides the lineage, policy enforcement, or shared semantics that would reconcile them. The standard moves the message; it does not decide whose definition is authoritative. That is the difference between agents that talk and agents that agree.
This is where lineage becomes the highest-value governed context. Root-cause analysis only works when ownership, quality, and upstream lineage travel with the protocol query, so an agent tracing a broken dashboard can follow the chain back to the column that changed. The cohort goes deeper on this in MCP for data lineage and data lineage RCA with MCP. The most expensive mistake in agent design is treating protocol choice as the hard decision and skipping the governed context layer entirely.
How Atlan makes any protocol production-ready
Permalink to “How Atlan makes any protocol production-ready”Atlan is the governed Context Layer for AI underneath MCP, A2A, and ANP, exposing certified, current, and correct context through whichever protocol an agent speaks. The challenge is consistent across every protocol: the standard connects agents but says nothing about whether the catalog underneath is trustworthy, and that is the reason agents fail once they reach production.
The approach is to keep the context layer protocol-agnostic so it outlives any single standard. The Context Lakehouse is one open store, every protocol, serving governed context through MCP, A2A, SQL, or API. The Atlan MCP server returns governed asset search, lineage traversal, business glossary, policy and approval rules, data quality, and write-back, and ownership, certification, quality, and access classifications travel with every asset returned. That last point is a claim no protocol vendor can make, because the protocol only standardizes the call. The Enterprise Data Graph and cross-system column-level lineage power the root-cause analysis proof point.
The outcome shows up in customer language. Joe DosSantos, VP of Enterprise Data and Analytics at Workday, describes how the shared language his team built can be used by AI via Atlan’s MCP server, and Sridher Arumugham, Chief Data and Analytics Officer at DigiKey, calls Atlan a context operating system rather than a catalog of catalogs. Whichever protocols you adopt, the governed Context Layer for AI is the layer that makes them production-ready.
Real stories from real customers: governed context under the MCP server
Permalink to “Real stories from real customers: governed context under the MCP server”"We're excited to build the future of AI governance with Atlan. All of the work that we did to get to a shared language at Workday can be leveraged by AI via Atlan's MCP server…as part of Atlan's AI Labs, we're co-building the semantic layer that AI needs with new constructs, like context products."
Joe DosSantos, VP of Enterprise Data & Analytics, Workday
"Atlan is much more than a catalog of catalogs. It's more of a context operating system…Atlan enabled us to easily activate metadata for everything from discovery in the marketplace to AI governance to data quality to an MCP server delivering context to AI models."
Sridher Arumugham, Chief Data & Analytics Officer, DigiKey
The protocol is the easy part; the context layer is the decision
Permalink to “The protocol is the easy part; the context layer is the decision”Choosing between MCP, A2A, and ANP is the reversible part of building an agent stack. They are a complementary set of standards for three different jobs: MCP for agent-to-tool access, A2A for agent-to-agent delegation, and ANP for decentralized cross-organization identity. Route by the job in front of you, adopt each protocol as that job appears, and treat ANP as the emerging cross-org layer rather than a today requirement. The decision that actually compounds is the governed Context Layer for AI underneath all of them, where lineage, ownership, certification, and policy rules travel with every query so the answers your agents return are certified, current, and correct. Get the protocols right and you have connectivity. Get the context layer right and you have agents that work in production.
FAQs
Permalink to “FAQs”1. Are MCP, A2A, and ANP competitors or complementary?
Permalink to “1. Are MCP, A2A, and ANP competitors or complementary?”Complementary. They solve three different jobs, with MCP for agent-to-tool access, A2A for agent-to-agent delegation, and ANP for decentralized cross-organization identity, and they commonly compose, with an A2A agent calling MCP to fetch context.
2. Do I need all three protocols in my agent stack?
Permalink to “2. Do I need all three protocols in my agent stack?”Not on day one. Most setups today need only MCP, you add A2A when multiple specialized agents must delegate work, and you add ANP when agents must trust each other across organizational boundaries. Adopt each one as its job appears.
3. When should I use MCP vs A2A vs ANP?
Permalink to “3. When should I use MCP vs A2A vs ANP?”Use MCP when an agent needs to call a tool or read data, A2A when one agent delegates structured work to another, and ANP when agents from different companies must authenticate without a shared broker. Route by the job, not by ranking.
4. Is ANP production-ready yet?
Permalink to “4. Is ANP production-ready yet?”Not yet. ANP is experimental in 2026 and matters most as agent ecosystems cross organizational boundaries using decentralized W3C DIDs. Treat it as the cross-org future layer, not a current requirement.
5. What do agent protocols not solve?
Permalink to “5. What do agent protocols not solve?”They govern the connection, not the content. No protocol guarantees the data an agent retrieves is certified, current, or correct, which is why a governed Context Layer for AI with lineage, ownership, and policy is needed regardless of which protocols you run.
Sources
Permalink to “Sources”- A Survey of Agent Interoperability Protocols (MCP, ACP, A2A, ANP), Ehtesham et al., arXiv
- ANP Technical White Paper, arXiv
- Donating the Model Context Protocol and Establishing the Agentic AI Foundation, Anthropic
- A2A vs MCP: AI Agent Protocols, DigitalOcean
- MCP Adoption Statistics 2026, MCP Manager
- A Year of MCP: 2025 Review, Pento / The New Stack
- Agent Interoperability Protocols: MCP, A2A, and OSI, Atlan
- What Is Model Context Protocol, Atlan
- Atlan MCP Server
