Last month, Google launched the Agent Development Kit (ADK) and the Agent2Agent (A2A) protocol, designed to standardize how autonomous AI agents communicate. The protocol was framed as the next Kubernetes: open, composable, and ecosystem-led.
The initiative also integrates with the emerging Model Context Protocol (MCP) — a metadata framework for associating AI outputs with model provenance, context, and audit trails. If A2A and MCP gain traction, they could form the backbone of a standardized, compliant, and composable AI stack.
I wrote the post below after reviewing Google’s announcement, GitHub and the community site.
Google’s ADK & A2A — Strategic Intent, Execution Risk, and Business Value
Vision and Positioning
Google’s open-source release of the Agent Development Kit (ADK) and Agent2Agent (A2A) protocol is a bid to define foundational infrastructure for multi-agent AI workflows. With over 50 initial partners — including Salesforce, Atlassian, and Microsoft’s Semantic Kernel — the strategy echoes past platform plays like Kubernetes: open-source the coordination layer to drive ecosystem control. Integrating with MCP deepens this strategy by linking agent actions to verifiable model context and metadata.
Execution Readiness
Despite the ambitious vision, a review of the A2A GitHub repo reveals a project still in early development. This would be incredibly surprising if it weren’t, after just a month. Community traction is modest, documentation is evolving, and real-world deployments are sparse. While the protocol leverages familiar standards (HTTP, JSON-RPC), key architectural components like cross-agent security, semantic interoperability, and observability remain underdeveloped. Contribution velocity and third-party adoption are currently low. I would like to see more velocity at this stage given the number of partners/adopters Google announced.
Business Value Impact Scenarios
- If A2A alone succeeds: Organizations could modularize AI workflows using interoperable agents, improving agility and reducing operational complexity. Vendors could offer “agent-ready” platforms, and a developer ecosystem may emerge around shared orchestration standards.
- If A2A and MCP succeed together: The impact compounds. MCP introduces trust, compliance, and lineage — key for high-stakes environments like finance, healthcare, and defense. This unlocks use cases requiring explainability, auditability, and regulatory alignment. A2A becomes the execution layer; MCP, the governance and trust layer. Together, they enable responsible AI orchestration at scale.
A2A + ADK represents a strategically sound but operationally immature foundation for agent-based AI. The idea of pairing execution with accountability via MCP is compelling, but remains largely aspirational. Enterprises should track closely, experiment in low-risk environments, and prepare to engage if early adopters validate the stack. Google has laid a credible claim to the coordination layer of enterprise AI; whether it sticks depends on execution, ecosystem growth, and governance evolution.
I would love to hear thoughts, critiques, or counterpoints.
- Does the Kubernetes analogy hold, or is the abstraction layer too immature?
- What are the technical or governance blockers to A2A adoption?
- Is Google doing enough to cultivate neutral governance and credible open-source stewardship?
- What real-world signs would signal this stack is gaining traction?