An isolated agent is a dead end. An agent's value grows exponentially when it can communicate with other agents and connect to external tools. For that, you need protocols. Not ad-hoc APIs with JSON parsing. Real standards.
At AACFlow, we support two key protocols: A2A (Agent-to-Agent) from Google and MCP (Model Context Protocol) from Anthropic.
A2A: agents talking to agents
A2A is Google's open protocol, purpose-built for AI agent interaction. Not a REST API. Not GraphQL. A protocol that understands that on the other end isn't a dumb data consumer โ it's another intelligent agent.
What this enables:
- Delegation. Agent A tells Agent B: "Here's the task, here's the context, I'm waiting for results." Agent B doesn't just parse parameters โ it understands the goal. It can ask for clarification, push back, propose alternatives.
- Agent Discovery. An agent can ask: "What agents are available in the system? Who can solve this task?" The response is a list of agents with descriptions of their competencies.
- Context Transfer. Not just JSON with fields. The agent passes the full task context: what's been done, which hypotheses were tested, what constraints exist.
Practical example: Research Agent finishes data gathering and tells Writer Agent via A2A: "Here are 15 sources, 3 key takeaways, 2 contradictions that need resolving. Report should be in business style, target audience is C-level." Writer Agent receives not raw data, but a meaningful brief.
