A2A with Durable Lifecycle

A2A protocol enables agent communication. AXME adds what's missing - durable lifecycle, retries, timeouts, and human checkpoints.

A2A defines how agents talk to each other. But talking is not enough. When Agent B crashes mid-task, who retries? When Agent B needs a human decision, who manages the wait? A2A is a communication protocol - it handles the message. AXME is a lifecycle protocol - it handles everything after the message arrives.

Alpha - Built with AXME (AXP Intent Protocol). cloud.axme.ai - hello@axme.ai

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The Problem

A2A handles communication between agents. But communication alone is not lifecycle management:

• Agent B crashes mid-task - A2A does not know. The task is lost.
• Agent B needs human input - A2A has no approval gate. You build it yourself.
• Agent B is slow - A2A has no timeout. You poll forever.
• Network drops the message - A2A has no retry. You write retry logic.

Every team ends up building the same retry/timeout/approval infrastructure on top of A2A. That infrastructure is what AXME provides.

Raw A2A (manual retry, no lifecycle)

# Dispatcher sends task to executor via HTTP
import httpx, time

max_retries = 3
for attempt in range(max_retries):
    try:
        resp = httpx.post("https://executor-agent/tasks", json={
            "task_id": "TASK-2026-0042",
            "task_type": "data_enrichment",
            "input_data": {"records": 5000, "enrichment": "geo_lookup"},
        })
        resp.raise_for_status()
        break
    except httpx.HTTPError:
        if attempt == max_retries - 1:
            raise
        time.sleep(2 ** attempt)

# Poll for result (no lifecycle tracking)
while True:
    status = httpx.get(f"https://executor-agent/tasks/TASK-2026-0042").json()
    if status["state"] in ("completed", "failed"):
        break
    time.sleep(5)  # How long? Forever?

AXME (durable lifecycle, 4 lines)

intent_id = client.send_intent({
    "intent_type": "intent.a2a.task_execution.v1",
    "to_agent": "agent://myorg/production/a2a-executor",
    "payload": {"task_id": "TASK-2026-0042", "task_type": "data_enrichment"},
})
result = client.wait_for(intent_id)

AXME handles retries (3 attempts), timeouts (120s deadline), delivery confirmation, and lifecycle tracking. If the executor crashes, AXME retries. If it times out, the dispatcher knows.

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Quick Start

Python

pip install axme
export AXME_API_KEY="your-key"   # Get one: axme login

from axme import AxmeClient, AxmeClientConfig
import os

client = AxmeClient(AxmeClientConfig(api_key=os.environ["AXME_API_KEY"]))

intent_id = client.send_intent({
    "intent_type": "intent.a2a.task_execution.v1",
    "to_agent": "agent://myorg/production/a2a-executor",
    "payload": {
        "task_id": "TASK-2026-0042",
        "task_type": "data_enrichment",
        "source_agent": "dispatcher-agent",
        "input_data": {"records": 5000, "enrichment": "geo_lookup"},
        "priority": "normal",
    },
})

print(f"Dispatched: {intent_id}")
result = client.wait_for(intent_id)
print(f"Done: {result['status']}")

TypeScript

npm install @axme/axme

import { AxmeClient } from "@axme/axme";

const client = new AxmeClient({ apiKey: process.env.AXME_API_KEY! });

const intentId = await client.sendIntent({
  intentType: "intent.a2a.task_execution.v1",
  toAgent: "agent://myorg/production/a2a-executor",
  payload: {
    taskId: "TASK-2026-0042",
    taskType: "data_enrichment",
    sourceAgent: "dispatcher-agent",
    inputData: { records: 5000, enrichment: "geo_lookup" },
    priority: "normal",
  },
});

console.log(`Dispatched: ${intentId}`);
const result = await client.waitFor(intentId);
console.log(`Done: ${result.status}`);

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More Languages

Language	Directory	Install
Python	python/	pip install axme
TypeScript	typescript/	npm install @axme/axme

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How It Works

+-----------+  send_intent()   +----------------+  deliver    +-----------+
|           | ---------------> |                | ----------> |           |
| Dispatcher|                  |   AXME Cloud   |             | Executor  |
|  (agent)  | <- wait_for() -- |   (platform)   | <- result   |  (agent)  |
|           |                  |                |             |           |
|           |                  |  - 3 retries   |             +-----------+
|           |                  |  - 120s timeout|
|           | <- COMPLETED --- |  - lifecycle   |
|           |                  |  - audit trail |
+-----------+                  +----------------+

1. Dispatcher sends a task intent with enrichment parameters
2. AXME delivers the intent to the executor agent (with retries)
3. Executor processes the task (geo lookup on 5000 records)
4. Executor resumes the intent with results (records processed, success rate)
5. Dispatcher gets the final result via lifecycle tracking
6. If executor crashes, AXME retries delivery (up to 3 attempts)

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Run the Full Example

Prerequisites

# Install CLI (one-time)
curl -fsSL https://raw.githubusercontent.com/AxmeAI/axme-cli/main/install.sh | sh
# Open a new terminal, or run the "source" command shown by the installer

# Log in
axme login

# Install Python SDK
pip install axme

Terminal 1 - submit the scenario

axme scenarios apply scenario.json
# Note the intent_id in the output

Terminal 2 - start the executor agent

Get the agent key after scenario apply:

# macOS
cat ~/Library/Application\ Support/axme/scenario-agents.json | grep -A2 a2a-executor-demo

# Linux
cat ~/.config/axme/scenario-agents.json | grep -A2 a2a-executor-demo

Run in your language of choice:

# Python
AXME_API_KEY=<agent-key> python agent.py

# TypeScript (requires Node 20+)
cd typescript && npm install
AXME_API_KEY=<agent-key> npx tsx agent.ts

Verify

axme intents get <intent_id>
# lifecycle_status: COMPLETED

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Related

• AXME - project overview
• AXP Spec - open Intent Protocol specification
• AXME Examples - 20+ runnable examples across 5 languages
• AXME CLI - manage intents, agents, scenarios from the terminal
• Durable Execution with Human Approval - human approval gates

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Built with AXME (AXP Intent Protocol).