Agent Communication Protocol: The Complete Guide from Basic to Intermediate

Introduction: What is Agent Communication Protocol (ACP)?

The Agent Communication Protocol (ACP) is an open-source protocol that enables AI agents to communicate with each other across different frameworks, technologies, and organizations. Think of it as the "HTTP for AI agents" - just like how HTTP enables web browsers and servers to communicate, ACP enables AI agents to talk to each other regardless of whether they're built using CrewAI, LangChain, AutoGen, or any custom code.adasci+2

Why Do We Need ACP?

Modern AI development faces a critical challenge: agent fragmentation. Agents are built in isolation using different frameworks, making collaboration between them nearly impossible. This creates several problems:adasci

• Integration barriers - connecting different agents requires custom solutions

• Duplicated effort - teams rebuild similar functionality

• Scalability challenges - point-to-point integrations don't scale

• Inconsistent developer experience - each framework has its own patternsagentcommunicationprotocol

ACP solves these problems by providing a standardized RESTful interface that works across any technology stack.towardsdatascience+1

Core Concepts: Understanding the Foundation

1. Client-Server Architecture

ACP follows a classic client-server model with two main components:agentcommunicationprotocol

ACP Client: An agent, application, or service that makes requests to an ACP server using the ACP protocol.

ACP Server: Hosts one or more ACP agents, executes requests, and returns results using the ACP protocol. The server exposes agents through a REST interface.

The beauty of ACP is that a single process can act as both client and server - handling incoming requests while making outbound requests to other agents.agentcommunicationprotocol

2. RESTful Communication Pattern

Unlike complex protocols that require specialized communication methods, ACP uses simple HTTP patterns. This means you can interact with ACP agents using familiar tools like:towardsdatascience

• curl commands

• Postman requests

• Standard web browsers

• Any HTTP client libraryagentcommunicationprotocol

3. Agent Run Lifecycle

Every interaction with an ACP agent follows a predictable lifecycle with clearly defined states:

• created - Run request accepted but processing hasn't begun

• in-progress - Agent is actively processing the request

• awaiting - Agent pauses to request additional information from client

• completed - Agent finished successfully with final output

• failed - Agent encountered an error

• cancelling - Cancellation requested but not yet confirmed

• cancelled - Agent execution successfully cancelled

Architecture Patterns: From Simple to Advanced

Basic Single-Agent Architecture

The simplest ACP deployment connects a client directly to a single agent:agentcommunicationprotocol

[ACP Client] ←→ HTTP ←→ [ACP Server] → [Single Agent]

When to use: Direct communication with specialized agents, lightweight setups, development environments, proof-of-concept implementations.

Multi-Agent Single Server

An ACP Server can host multiple agents behind a single HTTP endpoint:agentcommunicationprotocol

[ACP Client] ←→ HTTP ←→ [ACP Server] → [Agent A]

→ [Agent B]

→ [Agent C]

Benefits: Resource efficiency, simplified deployment, centralized logging and monitoring, consistent authentication.

Distributed Multi-Server Architecture

Multiple independent servers, each hosting one or more agents:agentcommunicationprotocol

[ACP Client] ←→ [Server 1] → [Agent A]

←→ [Server 2] → [Agent B, Agent C]

←→ [Server 3] → [Agent D]

Benefits: Independent scaling, load distribution, fault isolation, technology stack diversity.

Topic 1: CrewAI Insurance Agent with ACP

Concept Explanation

A CrewAI Insurance Agent wrapped with ACP becomes a specialized service that can handle insurance-related tasks while being accessible to other agents through a standardized REST interface.

How It Works

1. Agent Creation: Build your CrewAI agent with specific insurance domain knowledge

2. ACP Wrapping: The ACP server wraps your CrewAI agent and exposes HTTP endpoints

3. REST Interface: Other agents or applications can interact via standard HTTP requests

4. Standardized Response: All responses follow ACP's standardized format

   
   

Simple Example Workflow

# Conceptual CrewAI Insurance Agent (wrapped in ACP Server)

@server.agent()

def insurance_agent(input: str) -> ACPResponse:

# CrewAI agent logic for insurance processing

crew = Crew(

agents=[insurance_specialist],

tasks=[analyze_claim, verify_coverage]

)

result = crew.kickoff(input)

return ACP_formatted_response(result)

Key Concept: The agent handles complex insurance logic internally, but presents a simple REST interface externally. Any other agent can send a claim request and receive a standardized response without knowing the internal CrewAI implementation.crewai

Topic 2: Sequential Agent Hospital Insurance ACP

Concept Explanation

Sequential agents work in a chain or pipeline where the output of one agent becomes the input for the next agent. In hospital insurance scenarios, this creates an efficient workflow for processing medical claims through multiple specialized stages.agentcommunicationprotocol

Sequential Workflow Pattern

The sequential pattern in ACP enables prompt chaining - where agents are executed one after another, with each agent receiving context from previous steps:agentcommunicationprotocol

Patient Data → [Medical Review Agent] → [Insurance Verification Agent] → [Claim Processing Agent] → Final Decision

Hospital Insurance Sequential Example

Let's break down a typical hospital insurance workflow:

Step 1 - Medical Document Analysis Agent:

• Receives patient medical records

• Extracts key clinical information

• Validates medical codes and proceduresakira

Step 2 - Insurance Verification Agent:

• Takes medical analysis results

• Checks patient coverage and policy limits

• Verifies pre-authorization requirements

Step 3 - Claim Processing Agent:

• Receives both medical and insurance verification data

• Calculates eligible benefits

• Generates final claim decisionmckinsey

Key Implementation Concept

# Sequential execution using ACP

async def sequential_hospital_insurance_workflow(patient_data):

# Step 1: Medical analysis

medical_result = await acp_client.run_sync(

agent="medical_analyzer",

input=patient_data

)

# Step 2: Insurance verification

insurance_result = await acp_client.run_sync(

agent="insurance_verifier",

input=medical_result.output

)

# Step 3: Final processing

final_result = await acp_client.run_sync(

agent="claim_processor",

input={

"medical_analysis": medical_result.output,

"insurance_verification": insurance_result.output

}

)

return final_result

Core Benefits:

• Specialization: Each agent focuses on its domain expertise

• Maintainability: Easy to update individual agents without affecting others

• Reusability: Agents can be used in different sequential workflows

• Error Isolation: Problems in one step don't crash the entire pipeline

Topic 3: ACP Client for Sequential Agent

Concept Explanation

An ACP Client is the component that initiates communication with ACP servers and orchestrates interactions between multiple agents. For sequential agents, the client manages the workflow, passing data between agents and handling the overall execution pipeline.adasci

Client Responsibilities

The ACP client serves as the orchestrator for sequential workflows:

1. Workflow Coordination: Manages the order of agent execution

2. Data Flow Management: Ensures outputs from one agent become inputs for the next

3. Error Handling: Manages failures and implements retry logic

4. State Management: Tracks the progress of the entire sequential process

5. Result Aggregation: Combines outputs from multiple agents into a final result

   
   

Client Architecture Patterns

Simple Sequential Client:

class SequentialACPClient:

def __init__(self):

self.client = ACPClient()

async def execute_workflow(self, initial_input, agent_sequence):

current_input = initial_input

results = []

for agent_name in agent_sequence:

result = await self.client.run_sync(

agent=agent_name,

input=current_input

)

results.append(result)

current_input = result.output # Chain the outputs

return self.aggregate_results(results)

Advanced Client Features

Error Handling and Retry Logic:

async def robust_sequential_execution(self, workflow_steps):

for step in workflow_steps:

max_retries = 3

for attempt in range(max_retries):

try:

result = await self.client.run_sync(

agent=step.agent_name,

input=step.input_data

)

break # Success, continue to next step

except ACPException as e:

if attempt == max_retries - 1:

raise # Final attempt failed

await asyncio.sleep(2 ** attempt) # Exponential backoff

Parallel Branch Execution:

async def parallel_then_sequential(self, parallel_agents, sequential_agents):

# Execute multiple agents in parallel

parallel_tasks = [

self.client.run_sync(agent=agent, input=shared_input)

for agent in parallel_agents

]

parallel_results = await asyncio.gather(*parallel_tasks)

# Then execute sequential workflow with combined results

combined_input = self.combine_parallel_results(parallel_results)

return await self.execute_sequential(sequential_agents, combined_input)

REST API Fundamentals for ACP

Core Endpoints

ACP defines several standard REST endpoints that every ACP server must implement:

Agent Discovery:

• GET /agents - List available agents

• GET /agents/{agent_name} - Get specific agent details

Agent Execution:

• POST /runs - Start a new agent run

• GET /runs/{run_id} - Check run status

• POST /runs/{run_id} - Resume paused run

• POST /runs/{run_id}/cancel - Cancel running agent

Message Structure

ACP uses a standardized message format for all communications:

{

"parts": [

{

"content": "Process this insurance claim",

"content_type": "text/plain"

},

{

"content": "base64-encoded-file-data",

"content_type": "application/pdf"

}

],

"role": "user"

}

Key Features:

• Multimodal Support: Handle text, images, files, audio, video

• MIME Type Identification: Automatic content type detection

• Role-based Messaging: Clear identification of message sourcegithub

Production Considerations and Best Practices

Authentication and Security

• Implement proper authentication mechanisms

• Use HTTPS for all communications

• Validate all inputs and sanitize outputs

• Implement rate limiting and request quotas

Scalability Patterns

Horizontal Scaling:

• Deploy multiple ACP server instances

• Use load balancers to distribute requests

• Implement session affinity for stateful agents

  
  

Vertical Scaling:

• Optimize agent performance

• Use caching for frequently accessed data

• Implement connection pooling

  
  

Monitoring and Observability

• Log all agent interactions and performance metrics

• Implement health checks for all ACP servers

• Monitor workflow execution times and success rates

• Set up alerting for failed agent runs

  
  

Interview Preparation: Key Concepts to Remember

Fundamental Questions

Q: What is ACP and why is it important?A: ACP is an open protocol that enables AI agents to communicate across different frameworks using RESTful APIs. It solves the agent fragmentation problem by providing a standardized communication layer.

Q: How does ACP differ from direct API calls?A: ACP provides standardized message formats, agent discovery, lifecycle management, and error handling that raw API calls don't offer. It's specifically designed for agent-to-agent communication.

Q: Explain the client-server model in ACP.A: ACP clients make requests to ACP servers. Servers host agents and expose them through REST endpoints. A single process can be both client and server, enabling peer-to-peer agent communication.

Technical Questions

Q: What are the main states in the ACP agent lifecycle?A: created → in-progress → {completed|failed|awaiting} with additional states for cancellation (cancelling → cancelled).

Q: How do sequential agents work in ACP?A: Sequential agents form processing pipelines where each agent's output becomes the next agent's input. The ACP client orchestrates this workflow and manages data flow between agents.

Q: What's the difference between sync, async, and streaming modes?A: Sync mode waits for complete response, async mode returns immediately with a run_id for later polling, and streaming mode provides incremental updates as the agent processes.

Practical Implementation Tips

Starting Simple

1. Begin with Single Agent: Start with one ACP-wrapped agent before building complex workflows

2. Use Official SDKs: Leverage Python or TypeScript SDKs rather than raw HTTP calls

3. Implement Proper Error Handling: Always handle network failures and agent errors gracefully

4. Test with curl: Verify your ACP server works with simple curl commands before integration

   
   

Scaling Up

1. Design for Statelessness: Keep agents stateless when possible for easier scaling

2. Implement Circuit Breakers: Protect against cascading failures in sequential workflows

3. Use Async Patterns: Leverage async/await for better performance in client code

4. Monitor Everything: Implement comprehensive logging and monitoring from day one

Conclusion

Agent Communication Protocol represents a significant step forward in AI agent interoperability. By providing a standardized RESTful interface, ACP enables agents built with different frameworks to work together seamlessly. Whether you're building simple single-agent systems or complex multi-agent workflows, understanding ACP's core concepts - client-server architecture, sequential processing patterns, and REST-based communication - will prepare you for the future of collaborative AI systems.

The three topics covered - CrewAI Insurance Agent with ACP, Sequential Agent Hospital Insurance ACP, and ACP Client for Sequential Agent - demonstrate how ACP enables practical, production-ready solutions across different domains while maintaining simplicity and standardization.