When the web was scaling rapidly in the early 90s, Tim Berners-Lee didn't wait for fragmentation to set in. He founded W3C in 1994 — before the browser wars made it worse — and got the industry to agree on the rules everyone would build to.
That same moment happened for agentic AI.
It's called the Agentic AI Foundation — AAIF. Eight founding members including Microsoft, Google, AWS, and Anthropic built it under the Linux Foundation around one specific acknowledgment: two architectural gaps are breaking agentic systems in production everywhere, and they're foundational enough to require neutral, open infrastructure to solve.
And like the W3C, it was built around failure modes most teams don't know to test for.
→ State continuity — the agent hits a total reset the moment there's a session handoff. Context built over minutes or hours: gone. The next agent starts from scratch.
→ Interoperability — the agent can't pass work cleanly to another agent or system without someone manually re-prompting to pick up where it left off.
Here's what this looks like when a team hits it in production: an agent spends 20 minutes building context on a customer request — history, preferences, constraints — then hands off to a second agent for action. The second agent has no memory of any of it. The user re-prompts manually. The first agent's work is effectively thrown away.
In a vendor demo, this never happens. The demo is designed around the gap. In production, there's no safety net — and no warning that the gap exists until the system is already live.
Here's how seriously the industry has acknowledged these two gaps.
Eight of the biggest names in tech — Microsoft, AWS, Anthropic, Block, Bloomberg, Cloudflare, Google, and OpenAI — became founding members of the Agentic AI Foundation (AAIF) under the Linux Foundation, and built its entire open standards effort around solving exactly these two problems.
MCP (Model Context Protocol) → solves interoperability. Agents dynamically discover and invoke tools across systems and vendors. No manual re-prompting. No context lost at the boundary.
A2A (Agent-to-Agent) Protocol v1.0 → solves state continuity across agents. Agents running in entirely different frameworks and runtimes coordinate through structured messaging, with context intact through every handoff.
This is an industry-wide acknowledgment that these two failure modes are real, widespread, and foundational enough to require neutral, open infrastructure to solve them.
If the entire industry agreed these two gaps needed an open foundation — your vendor's sandbox demo isn't going to surface them. You have to test for them explicitly.
Here's what a stack that takes both problems seriously actually looks like under the hood.
Microsoft Agent Framework 1.0 — GA'd April 3, 2026 — was built around exactly these failure modes.
State continuity: pluggable memory with conversational history, persistent key-value state, and vector retrieval. Foundry Agent Service, Mem0, Redis, and Neo4j as backends. Checkpointing and hydration so long-running processes survive interruptions without losing context.
Interoperability: MCP for cross-system tool discovery and invocation. A2A Protocol v1.0 for structured agent-to-agent coordination across frameworks and runtimes — now fully live in .NET. A remote A2A agent is just an AIAgent in your code: same streaming, same session handling, regardless of what framework or cloud is behind it. And Azure AI Foundry Observability (currently in preview) brings OpenTelemetry-powered evaluation dashboards running continuously from development through live production traffic — so you're scoring behavior over time, not just a snapshot.
I'm not saying Microsoft's stack is the only answer. I'm saying the architecture decisions required to solve these two gaps are far more specific than most vendors want to admit in a demo — and the fact that the whole industry built an open foundation around them tells you everything about how hard they are to get right.
What to do right now:
Explore the handoff pattern using DevUI.
Microsoft Agent Framework ships DevUI — a browser-based debugger that visualizes every agent execution step in real time. Load the Interview Coach sample from the GitHub repo. It requires minimal setup — git clone, API key configuration — but no custom code of your own. Watch exactly where context survives a handoff and where it drops. You're observing behavior, not debugging logic.Ask your vendor to run your scenario, not theirs.
Give your vendor a specific test: one agent starts a task, a second agent picks it up mid-session from a different framework. Don't let them choose the demo. If they can't run your scenario on the spot, they've answered your question without you writing a single line of code.Put these two questions in your RFP — and require a live answer, not a slide.
"Show me how your agent retains state across a session interruption."
"Show me a live handoff to an agent outside your ecosystem."
A vendor who answers with a roadmap instead of a working demo is telling you something important about their production readiness.
If your current vendor fails items 2 or 3, that's not a reason to panic — it's a timeline question. Ask them where A2A support sits on their roadmap and what their committed delivery date is. A specific date means they're building it. A vague "coming soon" means you're 18 months from a problem you haven't budgeted for yet.
Every platform shift I've seen runs the same arc: early adopters compete on capability, then the market matures and competes on reliability.
We are at the capability peak of agentic AI right now.
The teams that win in 18 months are building reliability infrastructure today.
The whole industry told you which two gaps matter most.
W3C didn't make the web slower. It made it last. AAIF won't slow down agentic AI either — it's what makes it production-worthy.
Now go test whether your current stack actually closes them.
Builders: Have you explicitly tested agent handoffs in your production environment — or only in a vendor sandbox?
Engineering leads: When did you last verify that your agent retains context after a session interruption — not in a demo, in production?
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Agentic AI Foundation: aaif.io
Microsoft Agent Framework 1.0: devblogs.microsoft.com/agent-framework/microsoft-agent-framework-version-1-0/
A2A v1.0 in Agent Framework: devblogs.microsoft.com/agent-framework/a2a-v1-is-here-cross-platform-agent-communication-in-microsoft-agent-framework-for-net/
DevUI + Interview Coach sample: github.com/microsoft/agent-framework