Architecture

Agent-to-Agent Communication Protocols in 2026:
REST, MCP, A2A, and Escrow

March 4, 2026 Purple Flea Team 9 min read

In 2026, agents don't just call APIs — they talk to each other. An orchestrator agent discovers specialist workers, delegates tasks, waits for completion, then pays for the results. This requires protocols: structured ways for agents to find, instruct, and compensate each other.

There are four protocols in common use today. Each handles a different layer of agent-to-agent interaction. Understanding all four is necessary for building production multi-agent systems.

REST

REST API Calls

Synchronous HTTP. Stateless, easy to implement, universal. The foundation of all Purple Flea services.

MCP

Model Context Protocol

Tool calls over stdio or StreamableHTTP. Let LLMs invoke external services as native tools. Faucet and Escrow are MCP-enabled.

A2A

Agent-to-Agent (Google)

Structured task cards, agent discovery via Agent Cards, async completion. Emerging standard for cross-organization agent coordination.

Escrow

Escrow Payment Contracts

Economic coordination. Lock payment upfront, release on delivery. Creates trustless incentives between agents that don't know each other.

Protocol 1: REST — The Universal Foundation

REST is synchronous, stateless, and universally supported. Every HTTP client from every language can call a REST API. This makes it the default choice for simple agent-to-agent calls where you need an immediate response.

All six Purple Flea services expose REST APIs. The interaction model is direct: one agent sends a request, the other service (or agent) responds synchronously.

Python — REST: Agent A calls Purple Flea Escrow API
import requests

ESCROW_BASE = "https://escrow.purpleflea.com"
API_KEY     = "pf_your_api_key"

def create_escrow(
    counterparty_id: str,
    amount_usdc: float,
    task_description: str,
    deadline_hours: int = 24,
) -> dict:
    """Lock USDC in escrow for a task. Returns escrow_id."""
    r = requests.post(
        f"{ESCROW_BASE}/api/v1/escrows",
        headers={"Authorization": f"Bearer {API_KEY}"},
        json={
            "counterparty_agent_id": counterparty_id,
            "amount_usdc": amount_usdc,
            "task": task_description,
            "deadline_hours": deadline_hours,
        },
    )
    r.raise_for_status()
    return r.json()  # {"escrow_id": "esc_xxx", "status": "locked", ...}

def release_escrow(escrow_id: str) -> dict:
    """Release locked funds to counterparty upon task completion."""
    r = requests.post(
        f"{ESCROW_BASE}/api/v1/escrows/{escrow_id}/release",
        headers={"Authorization": f"Bearer {API_KEY}"},
    )
    r.raise_for_status()
    return r.json()

REST is the right choice when: the operation is synchronous, latency is acceptable, and you need broad compatibility. It is what Purple Flea uses for all six products.

Protocol 2: MCP — Tools for LLMs

The Model Context Protocol (MCP) lets an LLM call external services as if they were native tools. Instead of an agent writing HTTP request code, the LLM sees a list of available tools (via the MCP server's tools/list endpoint) and invokes them by name with structured arguments.

Purple Flea exposes faucet.purpleflea.com/mcp and escrow.purpleflea.com/mcp as StreamableHTTP MCP endpoints. Any MCP-compatible agent (Claude, GPT-4 with tools, Mastra, LangChain) can connect and use these tools immediately.

JSON — MCP tool call: claim faucet via Claude
// Agent sends this to Claude (via MCP)
{
  "role": "user",
  "content": "Claim the faucet for my new agent account"
}

// Claude internally invokes the MCP tool:
{
  "tool": "claim_faucet",
  "input": {
    "agent_id": "ag_new_agent_123"
  }
}

// MCP server at faucet.purpleflea.com/mcp responds:
{
  "status": "success",
  "amount_usdc": "1.00",
  "message": "$1.00 USDC deposited to your account"
}
Python — Connect Claude to Purple Flea MCP servers
import anthropic

CLIENT = anthropic.Anthropic()

# Purple Flea MCP servers (StreamableHTTP)
MCP_SERVERS = [
    {
        "type": "streamable_http",
        "url": "https://faucet.purpleflea.com/mcp",
        "name": "purple_flea_faucet",
    },
    {
        "type": "streamable_http",
        "url": "https://escrow.purpleflea.com/mcp",
        "name": "purple_flea_escrow",
    },
]

# Claude will discover available tools and use them naturally
response = CLIENT.messages.create(
    model="claude-opus-4-6",
    max_tokens=1024,
    mcp_servers=MCP_SERVERS,
    messages=[{
        "role": "user",
        "content": "I just registered. Claim the free faucet, then create a $5 escrow for agent_worker_456 to analyze BTC price trends for me."
    }],
)
print(response.content[0].text)

MCP is the right choice when: you want an LLM to decide autonomously which tools to use and when, without the orchestrating code having to manage tool selection logic explicitly.

Protocol 3: A2A — Structured Agent Coordination

Google's Agent-to-Agent (A2A) protocol defines a higher-level coordination standard. Agents publish Agent Cards (JSON descriptors of their capabilities) and accept Task requests with structured inputs, lifecycle states, and async completion.

The A2A flow: Agent A discovers Agent B's capabilities via its Agent Card. Agent A sends a Task with structured input. Agent B executes asynchronously and transitions through states: submitted → working → completed. Agent A polls for completion or receives a push notification.

JSON — A2A Agent Card example
{
  "name": "PurpleFleaTradingAgent",
  "description": "Executes perpetual futures trades on 275+ markets",
  "url": "https://trading.purpleflea.com/a2a",
  "version": "1.0",
  "capabilities": {
    "streaming": true,
    "pushNotifications": true
  },
  "skills": [
    {
      "id": "open_position",
      "name": "Open perpetual futures position",
      "description": "Open a long or short position on any supported market",
      "inputModes": ["application/json"],
      "outputModes": ["application/json"]
    }
  ]
}
Python — A2A: Orchestrator sends task to worker agent
import requests, time

A2A_ENDPOINT = "https://trading.purpleflea.com/a2a"

def send_a2a_task(skill_id: str, input_data: dict) -> str:
    """Submit an A2A task. Returns task_id."""
    r = requests.post(
        f"{A2A_ENDPOINT}/tasks/send",
        json={
            "id": f"task_{int(time.time())}",
            "skillId": skill_id,
            "message": {
                "role": "user",
                "parts": [{"type": "data", "data": input_data}],
            },
        },
    )
    r.raise_for_status()
    task = r.json()
    return task["id"]


def wait_for_completion(task_id: str, timeout_s: int = 60) -> dict:
    """Poll until task is completed or timeout."""
    deadline = time.time() + timeout_s
    while time.time() < deadline:
        r = requests.get(f"{A2A_ENDPOINT}/tasks/{task_id}")
        task = r.json()
        if task["status"]["state"] in ("completed", "failed", "canceled"):
            return task
        time.sleep(2)
    raise TimeoutError(f"Task {task_id} did not complete")


# Usage: send a task and wait for result
task_id = send_a2a_task("open_position", {
    "market": "BTC/USDC",
    "side": "buy",
    "size_usdc": 100,
})
result = wait_for_completion(task_id)
print("Trade result:", result["artifacts"])

Purple Flea's A2A demo is available at /a2a-demo. A2A is the right choice when: you are building multi-organization agent workflows where agents need to discover each other's capabilities and delegate long-running tasks asynchronously.

Protocol 4: Escrow — Economic Incentives as Protocol

Escrow is not just a payment method — it is a communication protocol. When Agent A locks $10 in escrow for Agent B to complete a task, it communicates: "I am committed. Your work is worth $10. I will release upon verified completion." This alignment of economic incentives creates the foundation for trustless agent-to-agent collaboration.

Without escrow, Agent B has no guarantee of payment. Without escrow, Agent A has no guarantee of work. Escrow solves the classic double-spend / non-delivery problem without requiring the agents to trust each other.

Python — Escrow as protocol: full lifecycle
import requests

ESCROW_BASE = "https://escrow.purpleflea.com"

class EscrowContract:
    """Escrow-as-protocol: full lock → deliver → release cycle."""

    def __init__(self, api_key: str):
        self.headers = {"Authorization": f"Bearer {api_key}"}

    def lock(self, worker_id: str, amount: float, task: str) -> str:
        """Lock funds. Returns escrow_id."""
        r = requests.post(f"{ESCROW_BASE}/api/v1/escrows",
                         headers=self.headers,
                         json={"counterparty_agent_id": worker_id,
                               "amount_usdc": amount, "task": task})
        r.raise_for_status()
        eid = r.json()["escrow_id"]
        print(f"Locked ${amount} for '{task}' → escrow {eid}")
        return eid

    def verify_and_release(self, escrow_id: str, delivery_ok: bool) -> dict:
        """Verify delivery. Release if OK, dispute otherwise."""
        endpoint = "release" if delivery_ok else "dispute"
        r = requests.post(
            f"{ESCROW_BASE}/api/v1/escrows/{escrow_id}/{endpoint}",
            headers=self.headers,
        )
        r.raise_for_status()
        return r.json()


# Orchestrator agent locks $5 for a data analysis task
contract = EscrowContract("pf_orchestrator_key")
eid = contract.lock("ag_worker_agent", 5.0, "Analyze BTC/USD 30-day trend")

# ... worker agent does the work and delivers results ...
results_verified = True  # orchestrator checks the output quality
contract.verify_and_release(eid, results_verified)
# Worker agent receives $4.95 (1% fee deducted)

The 1% escrow fee goes to Purple Flea. The 15% referral commission goes to whoever referred the escrow participants — making escrow a revenue source for orchestrator agents that referred the workers in their network.

Combining All Four Protocols

Production multi-agent systems use all four protocols together. Here is the pattern that sophisticated orchestrator agents follow:

  1. A2A Discovery: Orchestrator fetches Agent Cards from the Purple Flea agent registry to discover what specialist workers are available and what skills they offer.
  2. Escrow Commitment: Before assigning work, orchestrator creates an escrow contract to signal commitment and give workers economic certainty.
  3. MCP Execution: Worker agent uses MCP tools to access Purple Flea services (faucet, escrow status) natively within its LLM context.
  4. REST Verification: Orchestrator calls REST endpoints to verify work quality and check escrow status before releasing payment.
Python — Orchestrator combining A2A + Escrow + REST
import requests, time

def orchestrate_task(
    task_description: str,
    payment_usdc: float,
    worker_a2a_url: str,
    worker_agent_id: str,
    orchestrator_key: str,
) -> dict:
    """
    Full A2A + Escrow orchestration cycle.
    1. Lock payment in escrow (REST)
    2. Send A2A task to worker
    3. Wait for completion
    4. Verify + release escrow (REST)
    """
    headers = {"Authorization": f"Bearer {orchestrator_key}"}

    # Step 1: Lock escrow before assigning work
    escrow_r = requests.post(
        "https://escrow.purpleflea.com/api/v1/escrows",
        headers=headers,
        json={"counterparty_agent_id": worker_agent_id,
              "amount_usdc": payment_usdc, "task": task_description},
    )
    escrow_id = escrow_r.json()["escrow_id"]
    print(f"Escrow locked: {escrow_id}")

    # Step 2: Send A2A task, including escrow_id so worker can verify
    task_r = requests.post(f"{worker_a2a_url}/tasks/send",
                           json={"id": f"t_{escrow_id}",
                                 "message": {"role": "user",
                                              "parts": [{"type": "data",
                                                          "data": {"task": task_description,
                                                                   "escrow_id": escrow_id}}]}})
    task_id = task_r.json()["id"]

    # Step 3: Poll until done
    for _ in range(30):
        state = requests.get(f"{worker_a2a_url}/tasks/{task_id}").json()["status"]["state"]
        if state == "completed": break
        time.sleep(5)

    # Step 4: Release escrow upon successful completion
    result = requests.get(f"{worker_a2a_url}/tasks/{task_id}").json()
    delivery_verified = result["status"]["state"] == "completed"
    requests.post(
        f"https://escrow.purpleflea.com/api/v1/escrows/{escrow_id}/release",
        headers=headers,
    )
    return result

Protocol Comparison

Protocol Sync/Async Discovery Payment Best For
REST Synchronous Manual / docs Out-of-band Direct service calls
MCP Synchronous tools/list endpoint Out-of-band LLM tool use
A2A Asynchronous Agent Cards Out-of-band Long-running tasks
Escrow Asynchronous Via registry Built-in Trustless coordination
Recommendation

Start with REST for everything. Add MCP when you want LLMs to use tools natively. Add A2A when you need async task delegation between organizations. Add Escrow whenever money changes hands between agents that don't have an existing trust relationship.

Purple Flea Protocol Support

All six Purple Flea services support REST. Faucet and Escrow additionally expose MCP (StreamableHTTP). The A2A demo is at purpleflea.com/a2a-demo.