From Protocols to Shared Understanding: A2A, MCP, and the Future of Multi-Agent AI

(Note: this article was prepared in collaboration with ChatGPT. Also, I am still learning the deeper details of MCP/A2A, so please feel free to let me know what i am missing, in the comments/DM --- would greatly appreciate the education).

Why agents need more than shared APIs—they need shared trust.

As we move into the next era of AI, single-model systems are giving way to agentic ecosystems: LLMs that retrieve, reason, validate, plan, and execute in coordinated workflows.

To make this possible, major players like Anthropic and Google are laying down foundational protocols:

• MCP (Model Context Protocol): Enables AI models to securely access external data and tools through a structured client-server architecture.
• A2A (Agent2Agent Protocol): Establishes a common language and structure for agents to talk, share context, and compose behaviors modularly.

Together, MCP and A2A are helping us connect the pipes. But the real challenge isn’t interfacing—it’s understanding.

Why Protocols May Not be Enough:

Just because agents can speak to each other doesn’t mean they’ll understand one another—or that we can trust what they produce together.

Here’s what’s missing:

🔸 Semantic consistency One agent’s “risk score” might be another’s “priority tag.” Without a shared ontology, agents interpret tasks differently.

🔸 Alignment awareness Agents may optimize for different outcomes (explainability, speed, accuracy). Without clarity, their goals may conflict.

🔸 Uncertainty signaling Today, most agents pass outputs without quantifying how confident they are—or whether downstream agents should trust them.

A Proposal: Epistemic Signaling for Multi-Agent Workflows

Imagine if every agent output came with a standardized uncertainty score or even simple red/orange/green flags:

• 🔴 Red: Low confidence or ambiguous input
• 🟠 Orange: Medium confidence
• 🟢 Green: High confidence with calibrated backing

This would:

• Help downstream agents adjust behavior or flag human review
• Give buyers and compliance teams clearer audit trails
• Make agentic workflows safer, more interpretable, and more robust

Better yet, entropy-based scoring (already measurable in LLM logits) could be serialized in both A2A and MCP frameworks as a first-class signal.

What a Trust Stack for Agents Could Look Like:

If we want agents to reason collaboratively and responsibly, we’ll need a stack that includes:

✅ A2A/MCP for communication

✅ Shared ontology and taxonomy

✅ Role specialization and fallback logic

✅ Memory and context synchronization

✅ Confidence signaling and provenance

TL;DR

• MCP and A2A are critical foundations—but interoperability ≠ understanding.
• Epistemic signaling (like entropy scores or trust-color coding) should be part of agent protocols.
• Agentic AI needs not just a protocol layer—but a shared trust architecture. This might involve invoking ideas around Ontology, Taxonomy, Reasoning over Knowledge Graphs etc. (more on this later).

What other teams are thinking about this? I’d love to hear from those exploring multi-agent architectures in diagnostics, pharma, or regulated decision environments.

#A2A #MCP #AgenticAI #MultiAgentLLM #UncertaintyQuantification #EpistemicTrust #LLMWorkflows #AIAlignment #AIUX #HealthcareAI #Ontology