Agentic Protocols: Cutting Through the Noise

There’s a growing buzz around agentic protocols—MCP, A2A, ACP—and like most new waves in AI infrastructure, the hype is as thick as the confusion. Everyone’s trying to standardize how agents communicate and act, and while that’s a good thing in theory, it's easy to lose the signal in the noise.

Let’s get one thing out of the way: most of these protocols weren’t created for you. They were created by large players to formalize their approach, influence ecosystems, and sometimes gain a strategic foothold in the emerging agent economy.

That doesn’t make them bad. It just means you need to evaluate them as tools, not truths.

The Players and Their Protocols

🧠 MCP – Model Context Protocol (Anthropic)

A typed‑function interface wrapped in JSON. Tools are explicit, arguments are typed, and every call sits inside a reflection loop (planning → execution → critique → retry).

Sweet spot.

• Coding agents
• Knowledge‑work assistants that need precise, traceable steps

You get.

• Zero‑ambiguity tool calls
• Replay‑friendly execution traces

You pay.

• More ceremony than ad‑hoc chat
• Tighter coupling to Anthropic‑style loop semantics

Maturity: Reference impl ✓ | SDKs (Python, TypeScript, Java, C#) ✓

Reference: Anthropic MCP

🌐 A2A – Agent-to-Agent Protocol (Google)

A peer‑to‑peer message protocol that lets agents exchange capability‑based Agent Cards over HTTP/SSE topics. Google’s reference stack uses pub/sub semantics, but the spec is transport‑agnostic. Think “Kafka topics for agents.”

Sweet spot.

• Swarm robotics
• Supply‑chain sims
• Edge/IoT fleets

You get.

• Horizontal scale out of the box
• Loose coupling; agents can churn without killing the mesh

You pay.

• Global state & observability are now your problems
• Long‑running reasoning still needs another layer (often MCP)

Maturity: Reference impl ✓ | SDK (Go, Python) ✓

Reference: Google A2A

🗞 ACP – Agent Communication Protocol (IBM)

A semantic contract (OpenAPI‑ish) for discovering, negotiating, and invoking agent endpoints over HTTP. Less “agent swarm,” more “LLM talking to enterprise SaaS.”

Sweet spot.

• CRM hooks, ticketing systems, data dashboards
• Anywhere REST got you 80 % of the way before

You get.

• Zero‑friction path from today’s APIs to tomorrow’s agents
• Solid discoverability story

You pay.

• No built‑in planning or coordination
• Can drift into “JSON‑over‑HTTPS and nothing else” if you’re not careful

Maturity: Reference impl ✓ | SDK (Python) ✓

Reference: IBM ACP

Cross‑cutting Concerns

The unglamorous details that break everything if you ignore them.

Security & Auth (why mTLS matters)

Regardless of the acronym you pick, a mutual‑TLS handshake (or at minimum signed JWTs) is table stakes once agents cross organisation lines. Tool calls are code execution—treat them like prod API calls, not casual chat.

Observability

Structured traces (OpenTelemetry) + semantic logs are the only way to debug an emergent swarm at 3 a.m. Bolt this on early.

Data Privacy

Prune PII from logs before they hit disk. Encrypt sensitive fields at rest. Retain raw inference logs only as long as debugging requires—then delete.

Layering—Not Choosing—Protocols

Don't be afraid to run MCP alongside A2A: A2A moves messages, MCP shapes the content. Mix and match; standards are modular, not monogamous.

Stories & Decision Guide

Real deployments rarely fit a single acronym—layer to taste.

Closing Thoughts

Standards in AI aren’t neutral. They’re as much about ecosystem control as they are about making systems work together. That’s not cynical—it’s just how influence plays out in emerging spaces.

But as builders, our job isn’t to pick sides. It’s to ship value.

So use what fits. MCP, A2A, ACP—these are just starting points. The real work is in the loops, the memory, the feedback, and the nuance of how your agents evolve over time.