On 17 August, Google’s A2A protocol became a hosted project of the Agentic AI Foundation at the Linux Foundation, the same neutral body that already stewards Anthropic’s MCP. Axios broke the story first, and coverage followed within two days across the AI trade press. Nothing about the technology changed. No specification was rewritten and no API broke. What changed is who is responsible for where these standards go next, and that answers a genuine, practical fear for anyone buying agent technology right now: whether committing to one vendor’s agents locks you out of everyone else’s.
At the same time, the agentic AI adoption numbers tell a very different story from the governance news. Most enterprises still cannot get an agent past a pilot. Both things are true at once, and understanding why matters more than either headline on its own.
What actually changed on 17 August
Two protocols now sit under the same governance roof at the Agentic AI Foundation, AAIF for short.
MCP, the Model Context Protocol, was Anthropic’s founding contribution to AAIF when the foundation launched on 9 December 2025. MCP defines how a single AI agent connects to tools, data and applications, essentially the wiring between an agent and everything it needs to actually do work.
A2A, the Agent to Agent protocol, is what Google built to solve a different problem, how independent agents built by different vendors discover each other, hand off tasks and coordinate across organisational boundaries. Google had already donated A2A to the Linux Foundation back in June 2025. What happened on 17 August was narrower and more specific, A2A moved from the Linux Foundation’s general portfolio into AAIF specifically, the foundation built for agentic AI, joining MCP under one roof.
It is worth being precise about what this was not. Coverage of the move has been careful to point out that this is not an ownership event. Each protocol keeps its own maintainers, its own specification process and its own release schedule. What the foundation actually settled is who stewards the specifications going forward, not who controls them technically. What it left unsettled is arguably more important for a business evaluating this, which agent gets to read which data when multiple vendors are involved. Shared governance answers the communication question. It does not answer the access and responsibility question, and that gap is still something every enterprise has to work out contractually, not technically.
AAIF has grown fast since December, from under 40 member organisations to more than 250. The platinum members list reads like most of the industry that matters here, AWS, Anthropic, Block, Bloomberg, Cloudflare, Google, Microsoft and OpenAI all sit on it together, which is notable given how rarely these companies share governance of anything.
Google Cloud’s VP for agent infrastructure, Rao Surapaneni, explained the practical reasoning to Axios plainly. Enterprises are deploying agentic systems from multiple technology providers, and those agents need to actually work together. A protocol that only one vendor controls does not solve that problem. A protocol under neutral, shared governance at least has a chance to.
Why this is a bigger deal than it sounds
If you are not building agent infrastructure yourself, a protocol governance change can sound like plumbing news, and mostly it is. But the practical question underneath it is one every business evaluating AI agent vendors right now should be asking.
Before this kind of consolidation, building a multi-agent system meant either committing to one vendor’s full stack, or writing custom integration code for every agent-to-agent handoff yourself. Neither is a comfortable position. The first creates lock-in. The second creates a maintenance burden that grows with every new agent you add.
With MCP and A2A both governed by the same neutral body, the practical effect is that agents built on different platforms have a real, shared standard for talking to each other, rather than a patchwork of vendor specific bridges that break every time one side updates. That does not eliminate vendor lock-in entirely, plenty of it can still happen at the platform layer above these protocols, but it meaningfully lowers the risk of choosing a vendor today and finding yourself boxed in a year from now.
This is not purely theoretical. A2A has already moved past the pilot stage in its first year, with reported adoption spanning supply chain, financial services, insurance and IT operations, sectors where coordinating autonomous systems across multiple tools, vendors and environments is not optional, it is the whole point. Those are also, not coincidentally, some of the same sectors leading on actual production use of agents generally, which suggests the businesses that need multi-vendor coordination most are the ones pushing hardest for exactly this kind of shared standard.
For a business evaluating who to build an AI agent strategy with, this is now a fair question to ask any vendor directly: do your agents speak MCP and A2A, or only your own proprietary protocol.
The number that matters more than the governance news
Here is the part most coverage of this story skipped entirely, and it is the more important half.
Gartner’s most recent enterprise survey found that 80 percent of enterprise applications shipped or updated in the first quarter of 2026 now embed at least one AI agent, up from just 33 percent two years earlier. The range is wide, everything from a customer service deflection bot handling routine queries to a fully autonomous coding agent opening pull requests against a shared repository. That sounds like near universal adoption, and by one measure, it is.
Separate research from S&P Global Market Intelligence and McKinsey tells a different part of the story. Only 31 percent of enterprises actually have an agent in production. Adoption and production are not the same thing, and the gap between them is enormous. Banking and insurance lead at roughly 47 percent in production. Healthcare sits at 18 percent, government at 14 percent.
A separate KPMG survey from earlier this year adds another layer to the same picture. 42 percent of US enterprises have tested or deployed AI agents in some form, but only 15 percent have actually achieved scaled, orchestrated multi-agent adoption, meaning multiple agents working together rather than one isolated tool running in one workflow. Most organisations that have started are still stuck well short of the coordinated setup that actually needs a protocol like A2A in the first place.
Research from MIT’s NANDA initiative puts a number on exactly where projects stall. Of custom enterprise AI tools built, roughly 60 percent get evaluated, about 20 percent make it to a pilot, and only 1 in 20, five percent, actually reach production. The drop happens overwhelmingly between pilot and production, not between idea and pilot. Getting something working in a demo has never been the hard part.
Gartner’s own forecast is blunter still, more than 40 percent of agentic AI projects are expected to be cancelled before the end of 2027, cited reasons being escalating costs, unclear business value and inadequate risk controls.
A governance standard for how agents talk to each other does not fix any of that. AI agent interoperability was never the reason most agent projects stall between pilot and production. The reasons are the same ones we wrote about a fortnight ago covering AI readiness generally, the wrong use case gets chosen, the data underneath the agent was never actually ready, nobody owns the system once it is live, and success was never clearly defined before the build started. A protocol under good governance still fails against a process that never met those four conditions to begin with.
What this actually means if you are building an agent strategy
Two separate decisions are getting easier to make right now, and it is worth treating them as separate, because conflating them is exactly how a business ends up with a technically sound agent that never earns its keep.
Which protocol standard to build on just got clearer. MCP and A2A, under shared neutral governance with the backing of every major cloud and AI lab, are a safer long term bet than a vendor’s proprietary alternative. Ask any agent vendor directly whether they support both, and treat a vague answer as a warning sign rather than a technicality to sort out later.
Whether your business is actually ready to run an agent in production has nothing to do with this news at all. That question is still answered the same way it always was, by checking whether the process you want to automate is repeatable, runs at real volume, follows consistent rules, and is expensive enough in hours that solving it is worth the build. A governance standard makes the plumbing safer. It does not make a badly scoped project succeed, and it will not move you from the 69 percent still stuck outside production into the 31 percent who have actually gotten there.
The businesses currently sitting in that 31 percent did not get there because they picked the right protocol first. They got there because they picked the right process first, and only then worried about the technical layer underneath it.
Frequently asked questions
1. Did the protocol itself change on 17 August?
Answer: No. No specification was rewritten and no API changed for existing users of either MCP or A2A. What changed is which body stewards A2A going forward, moving it into the Linux Foundation’s Agentic AI Foundation alongside MCP rather than sitting in the Foundation’s broader, less specialised portfolio.
2. What is the actual difference between MCP and A2A?
Answer: MCP connects a single AI agent to the tools, data and applications it needs to do its job, essentially agent to tool. A2A connects independent agents to each other, so agents built by different vendors or teams can discover one another and hand off work, essentially agent to agent. They solve different layers of the same overall problem.
3. Does this mean vendor lock-in with AI agents is solved?
Answer: No, and no announcement has claimed that. Lock-in can still happen at the platform layer above these protocols through proprietary extensions or platform specific features. What has improved is the baseline, the core communication layer is now under shared governance rather than controlled by any single company. Data access and responsibility between agents from different vendors is still something you need to settle contractually, governance of the protocol does not settle it for you.
4. Why do 80 percent of companies have agents but only 31 percent have them in production?
Answer: The two figures measure different things. Having at least one agent embedded somewhere in an application is a low bar that most enterprises have now cleared. Running an agent reliably in production, with proper monitoring, ownership and a clear success measure, is a much higher bar, and most organisations have not built the operating discipline for it yet. Separate research puts the actual production rate for custom built tools as low as 5 percent, with most of the drop-off happening between pilot and production specifically.
5. Which industries are actually using this in production already?
Answer: Banking and insurance lead general agent production use at around 47 percent. On the interoperability side specifically, A2A’s first year of adoption has concentrated in supply chain, financial services, insurance and IT operations, sectors where coordinating agents across multiple vendors and systems is a core requirement rather than a nice to have.
6. Should this change which AI agent vendor we choose?
Answer: It is worth adding as one factor. Ask whether a vendor’s agents support MCP and A2A as open standards, since that reduces your long term switching cost. It should not be the only factor. Whether the vendor can actually help you scope a use case that clears the readiness bar matters more than protocol support alone.
If you are trying to work out whether a specific process in your business is actually ready for an AI agent, or whether the agent conversation you are having is solving the wrong problem entirely, that is exactly what we walk through in a free AI Readiness Audit.