A2A Protocol

The Agent2Agent (A2A) Protocol defines structured rules, message types, and interaction primitives that allow autonomous agents to collaborate, debate, delegate, and escalate tasks in coordinated multi-agent environments.

A2A acts as the language of coordination, enabling agents to reach consensus, maintain context, and reason collectively.

Motivation

Modern AI systems need more than isolated intelligence—they need collective reasoning. A2A provides:

A formal message structure for inter-agent communication
Context preservation across task chains
Role-based behavior through action types
Interruption, delegation, and escalation control

Without this, agents function as isolated tools rather than components of a dynamic system.

Core Message Schema

Every A2A message uses a consistent envelope format:

{
  "from": "agent.researcher",
  "to": "agent.critic",
  "type": "assertion",
  "intent": "evaluate_risk",
  "payload": {
    "claim": "Synthetic datasets increase distribution drift."
  },
  "context": {
    "thread_id": "9e3f-2341",
    "depth": 2,
    "memory_ref": "session-428b"
  }
}

Field Definitions

Field	Description
`from` / `to`	Unique agent IDs or roles
`type`	Message type: `inform`, `ask`, `assert`, `delegate`, `escalate`
`intent`	Action label understood across agents
`payload`	Structured message content
`context`	Thread metadata: IDs, depth, memory references

Supported Message Types

Type	Purpose
`inform`	Share passive state (e.g., "Task complete.")
`ask`	Request information or clarification
`assert`	Make a claim that can be validated or challenged
`delegate`	Assign a task to another agent
`escalate`	Pass task to a higher-level or supervisory agent

These primitives support building rich dialogue trees with predictable semantics..

Example Interaction Flow

[Planner]     → (delegate: plan_research) → [Researcher]
[Researcher]  → (assert: hypothesis)      → [Critic]
[Critic]      → (ask: clarify scope)      → [Researcher]
[Researcher]  → (inform: clarification)   → [Critic]
[Critic]      → (assert: approved)        → [Planner]

This results in an interaction graph with memory of all prior assertions, task states, and agent roles.

Features

Threaded message history with causal references
Depth tracking to avoid infinite loops
Memory references for context (e.g., vector DB chunks)
Extensible intents that map to model actions and tool calls

Integration Points

A2A messages are transmitted via:

In-memory during live agent sessions
TensorOne serverless endpoints for async flows
MCP for mid-chain model handoff coordination
TensorOne Evals for logging and traceability

Future Directions

We’re exploring:

Message reputation scoring (agent trustworthiness)
Conversation summarization (for audits/post-mortems)
Secure A2A signing (agent identity verification)
Graph-based visualizations (agent dialogue graphs)