Agent Communication Protocol (ACP): IBM's Answer to Multi-Agent Systems

In the rapidly evolving landscape of artificial intelligence, multi-agent systems have emerged as a frontier technology for solving complex problems. IBM's recently unveiled Agent Communication Protocol (ACP) represents a significant advancement in this field, offering a standardized framework for autonomous AI agents to communicate, coordinate, and collaborate. This comprehensive guide examines IBM's ACP, its technical architecture, implementation strategies, and how it compares to the Model Context Protocol (MCP) standard that has gained industry traction.

Understanding Agent Communication Protocol (ACP)

IBM's Agent Communication Protocol addresses one of the most significant challenges in multi-agent systems: enabling efficient, standardized communication between diverse AI agents regardless of their underlying architectures or capabilities.

Core Principles of ACP

The ACP framework is built on four fundamental principles:

1. Universal Compatibility: ACP works across different agent architectures, language models, and specialized AI tools.

2. Intent-Based Communication: Rather than focusing on raw data exchange, ACP structures communication around intents, actions, and domain-specific schemas.

3. Semantic Understanding: The protocol enables agents to exchange not just data but semantically rich information with context and meaning.

4. Execution Coordination: ACP provides mechanisms for agents to coordinate complex workflows, delegate tasks, and synchronize their activities.

The Technical Architecture of ACP

At its core, ACP consists of several interconnected components:

1# Simplified representation of ACP's architecture
2class ACPMessage:
3    def __init__(self, message_type, content, metadata=None):
4        self.message_type = message_type  # intent, query, response, action, etc.
5        self.content = content  # the actual message content
6        self.metadata = metadata or {}  # additional context
7        self.id = str(uuid.uuid4())
8        self.timestamp = datetime.now().isoformat()
9        
10class ACPAgent:
11    def __init__(self, agent_id, capabilities):
12        self.agent_id = agent_id
13        self.capabilities = capabilities  # what this agent can do
14        self.message_handlers = {}
15        
16    def register_handler(self, message_type, handler_function):
17        """Register a function to handle a specific message type"""
18        self.message_handlers[message_type] = handler_function
19        
20    def send_message(self, target_agent_id, message):
21        """Send an ACP message to another agent"""
22        # Implementation would involve the ACP broker
23        acp_broker.route_message(self.agent_id, target_agent_id, message)
24        
25    def receive_message(self, sender_id, message):
26        """Process an incoming ACP message"""
27        if message.message_type in self.message_handlers:
28            return self.message_handlers[message.message_type](sender_id, message)
29        else:
30            # Default handler
31            return ACPMessage("error", f"No handler for {message.message_type}")
32
33class ACPBroker:
34    def __init__(self):
35        self.registered_agents = {}
36        self.message_queue = Queue()
37        
38    def register_agent(self, agent):
39        """Register an agent with the broker"""
40        self.registered_agents[agent.agent_id] = agent
41        
42    def route_message(self, sender_id, target_id, message):
43        """Route a message from sender to target"""
44        if target_id in self.registered_agents:
45            target_agent = self.registered_agents[target_id]
46            return target_agent.receive_message(sender_id, message)
47        else:
48            # Queue message or return error
49            self.message_queue.put((sender_id, target_id, message))
50            return ACPMessage("error", f"Agent {target_id} not available")
51

This simplified representation illustrates the basic components, but the actual implementation includes additional features such as:

• Schema Registry: Stores and validates domain-specific data schemas
• Intent Registry: Catalogs available intents across all agents
• Security Layer: Handles authentication, authorization, and message encryption
• Persistence Layer: Maintains communication history and state

ACP vs. MCP: A Technical Comparison

While both ACP and MCP enable AI capabilities, they serve different primary purposes and have distinct architectural approaches.

Core Differences

| Feature | Agent Communication Protocol (ACP) | Model Context Protocol (MCP) | |---------|-------------------------------------|------------------------------| | Primary Focus | Agent-to-agent communication | Tool access for language models | | Architecture | Peer-to-peer with broker | Client-server | | Communication Pattern | Multi-directional | Primarily bidirectional | | State Management | Distributed across agents | Primarily server-side | | Schema Definition | Intent-based schemas | JSON Schema | | Primary Use Case | Collaborative multi-agent systems | Extending LLM capabilities |

Detailed Comparison

1. Communication Architecture

ACP employs a flexible communication architecture that can operate in multiple topologies:

1# Example of different ACP topologies
2
3# Star topology with central orchestrator
4orchestrator = ACPAgent("orchestrator", ["planning", "delegation"])
5worker_agent1 = ACPAgent("worker1", ["data_processing"])
6worker_agent2 = ACPAgent("worker2", ["reasoning"])
7worker_agent3 = ACPAgent("worker3", ["code_generation"])
8
9# Register all with broker
10acp_broker.register_agent(orchestrator)
11acp_broker.register_agent(worker_agent1)
12acp_broker.register_agent(worker_agent2)
13acp_broker.register_agent(worker_agent3)
14
15# Mesh topology where any agent can communicate with any other
16for agent in [worker_agent1, worker_agent2, worker_agent3]:
17    # Each agent knows about the others
18    agent.peer_registry = [a.agent_id for a in [worker_agent1, worker_agent2, worker_agent3] if a.agent_id != agent.agent_id]
19

MCP, in contrast, follows a strict client-server model:

1# MCP client-server model
2class MCPClient:
3    def __init__(self, server_url):
4        self.server_url = server_url
5        
6    def call_function(self, function_name, parameters):
7        """Call a function on the MCP server"""
8        payload = {
9            "function": function_name,
10            "parameters": parameters
11        }
12        response = requests.post(f"{self.server_url}/v1/invoke", json=payload)
13        return response.json()
14
15# LLM uses the client to access tools
16llm_response = llm_service.generate(
17    prompt="Search for recent papers on multi-agent systems",
18    tools=[MCPClient("https://search.example.com")]
19)
20

2. Message Format and Semantics

ACP uses a rich message format with intent semantics:

1# ACP message with intent semantics
2search_intent = ACPMessage(
3    message_type="intent",
4    content={
5        "intent": "search_knowledge_base",
6        "parameters": {
7            "query": "latest research on multi-agent coordination",
8            "max_results": 5,
9            "domains": ["computer_science", "artificial_intelligence"]
10        }
11    },
12    metadata={
13        "priority": "high",
14        "context": "literature_review",
15        "requester": "research_planning_agent"
16    }
17)
18
19# Send the intent to an agent capable of searching
20orchestrator.send_message("knowledge_agent", search_intent)
21

MCP uses a more straightforward function call paradigm:

1// MCP function call format
2{
3  "function": "web_search",
4  "parameters": {
5    "query": "latest research on multi-agent coordination",
6    "max_results": 5
7  }
8}
9

3. Capability Discovery

ACP includes built-in capability discovery:

1# ACP capability discovery
2discovery_message = ACPMessage(
3    message_type="discovery",
4    content={
5        "query_type": "capabilities",
6        "capability_domain": "data_processing"
7    }
8)
9
10# Broadcast to all agents via broker
11responses = acp_broker.broadcast(discovery_message)
12
13# Find agents that can process CSV data
14csv_capable_agents = [
15    agent_id for agent_id, response in responses.items()
16    if "csv_processing" in response.content.get("capabilities", [])
17]
18

MCP typically uses static configuration:

1// MCP server manifest listing available functions
2{
3  "functions": [
4    {
5      "name": "web_search",
6      "description": "Search the web for information",
7      "parameters": {
8        "query": {"type": "string", "description": "The search query"},
9        "max_results": {"type": "integer", "description": "Maximum number of results"}
10      }
11    },
12    {
13      "name": "image_generation",
14      "description": "Generate an image from text description",
15      "parameters": {
16        "prompt": {"type": "string", "description": "Text description of the image"},
17        "style": {"type": "string", "description": "Artistic style to use"}
18      }
19    }
20  ]
21}
22

Implementing ACP in Production Systems

Basic Implementation Steps

Setting up ACP in a production environment involves several key steps:

1. Install the ACP Framework

1pip install ibm-agent-communication-protocol
2

2. Define Agent Capabilities

1# Define capabilities for a data processing agent
2data_agent_capabilities = {
3    "data_processing": {
4        "formats": ["csv", "json", "xml", "parquet"],
5        "operations": ["filter", "transform", "aggregate", "join"],
6        "max_data_size": "500MB"
7    },
8    "data_analysis": {
9        "statistical_methods": ["regression", "classification", "clustering"],
10        "visualization": ["charts", "graphs", "dashboards"]
11    }
12}
13
14# Create the agent with these capabilities
15data_agent = ACPAgent("data_processor_agent", data_agent_capabilities)
16

3. Implement Message Handlers

1# Implement handler for data processing requests
2def handle_data_processing(sender_id, message):
3    """Handle data processing requests from other agents"""
4    try:
5        # Extract data location and requested operation
6        data_source = message.content.get("data_source")
7        operation = message.content.get("operation")
8        parameters = message.content.get("parameters", {})
9        
10        # Perform the requested operation
11        result = perform_data_operation(data_source, operation, parameters)
12        
13        # Return success response
14        return ACPMessage(
15            message_type="response",
16            content={
17                "status": "success",
18                "result_location": result.location,
19                "metadata": result.metadata
20            },
21            metadata={"processing_time": result.processing_time}
22        )
23    except Exception as e:
24        # Return error response
25        return ACPMessage(
26            message_type="error",
27            content={
28                "error_type": type(e).__name__,
29                "error_message": str(e)
30            }
31        )
32
33# Register the handler
34data_agent.register_handler("data_processing_request", handle_data_processing)
35

4. Set Up the ACP Broker

1# Initialize the broker
2broker = ACPBroker()
3
4# Register agents with the broker
5broker.register_agent(data_agent)
6broker.register_agent(visualization_agent)
7broker.register_agent(ml_model_agent)
8broker.register_agent(coordinator_agent)
9
10# Start the broker service
11broker.start(host="0.0.0.0", port=8765)
12

Advanced Configuration

For enterprise deployments, additional configuration is typically needed:

1# Enterprise configuration for ACP
2acp_config = {
3    "security": {
4        "authentication": {
5            "type": "oauth2",
6            "provider": "keycloak",
7            "config": {
8                "server_url": "https://auth.example.com/auth",
9                "realm": "ai-agents",
10                "client_id": "acp-broker"
11            }
12        },
13        "encryption": {
14            "message_encryption": True,
15            "key_rotation_period": "24h"
16        },
17        "authorization": {
18            "policy_engine": "OPA",
19            "policy_endpoint": "https://policies.example.com/v1/data/acp"
20        }
21    },
22    "persistence": {
23        "storage_type": "distributed",
24        "provider": "cassandra",
25        "retention_period": "90d",
26        "backup_schedule": "daily"
27    },
28    "monitoring": {
29        "metrics_endpoint": "https://metrics.example.com/ingest",
30        "log_level": "info",
31        "tracing": {
32            "enabled": True,
33            "exporter": "jaeger"
34        }
35    },
36    "scaling": {
37        "broker_instances": 3,
38        "load_balancing": "round_robin",
39        "message_queue": {
40            "type": "kafka",
41            "config": {
42                "bootstrap_servers": ["kafka1:9092", "kafka2:9092"],
43                "topic_prefix": "acp_messages"
44            }
45        }
46    }
47}
48
49# Initialize broker with enterprise configuration
50enterprise_broker = ACPBroker(config=acp_config)
51

Real-World Applications of ACP

IBM's ACP has been deployed in various enterprise scenarios, demonstrating its versatility and effectiveness.

Case Study 1: Financial Fraud Detection System

A major financial institution implemented an ACP-based multi-agent system for real-time fraud detection:

1# Simplified representation of the fraud detection system
2
3# Agent definitions
4transaction_monitoring = ACPAgent("transaction_monitor", ["stream_processing", "pattern_recognition"])
5customer_profile = ACPAgent("customer_profile", ["identity_verification", "behavior_analysis"])
6fraud_analyzer = ACPAgent("fraud_analyzer", ["risk_scoring", "decision_making"])
7alert_manager = ACPAgent("alert_manager", ["notification", "case_management"])
8
9# Communication flow for a transaction
10def process_transaction(transaction_data):
11    # Step 1: Transaction monitor identifies suspicious activity
12    analysis_message = ACPMessage(
13        message_type="analysis_request",
14        content={
15            "transaction": transaction_data,
16            "analysis_type": "anomaly_detection"
17        }
18    )
19    anomaly_result = transaction_monitoring.process_transaction(analysis_message)
20    
21    if anomaly_result.content.get("anomaly_score", 0) > THRESHOLD:
22        # Step 2: Get customer profile information
23        profile_message = ACPMessage(
24            message_type="profile_request",
25            content={
26                "customer_id": transaction_data["customer_id"],
27                "context": "fraud_investigation"
28            }
29        )
30        profile_result = customer_profile.process_request(profile_message)
31        
32        # Step 3: Comprehensive fraud analysis
33        fraud_message = ACPMessage(
34            message_type="fraud_analysis",
35            content={
36                "transaction": transaction_data,
37                "anomaly_data": anomaly_result.content,
38                "customer_profile": profile_result.content
39            }
40        )
41        fraud_result = fraud_analyzer.analyze(fraud_message)
42        
43        # Step 4: Handle alerts if necessary
44        if fraud_result.content.get("fraud_probability", 0) > 0.7:
45            alert_message = ACPMessage(
46                message_type="create_alert",
47                content={
48                    "severity": "high",
49                    "fraud_type": fraud_result.content.get("fraud_type"),
50                    "evidence": fraud_result.content.get("evidence"),
51                    "recommended_actions": fraud_result.content.get("recommendations")
52                }
53            )
54            alert_manager.create_alert(alert_message)
55            
56            return {
57                "transaction_status": "blocked",
58                "reason": "potential_fraud",
59                "case_id": alert_message.response.content.get("case_id")
60            }
61    
62    return {"transaction_status": "approved"}
63

This system processed over 10,000 transactions per second with a 200% improvement in fraud detection rates and a 70% reduction in false positives compared to their previous system.

Case Study 2: Healthcare Diagnostic Assistant

A healthcare provider implemented an ACP-based diagnostic system:

1# Healthcare multi-agent system architecture
2patient_data_agent = ACPAgent("patient_data", ["ehr_access", "data_normalization"])
3imaging_analysis_agent = ACPAgent("imaging", ["radiology_analysis", "image_processing"])
4diagnostic_agent = ACPAgent("diagnostics", ["medical_reasoning", "differential_diagnosis"])
5treatment_recommendation_agent = ACPAgent("treatment", ["protocol_matching", "personalized_medicine"])
6expert_consultation_agent = ACPAgent("expert", ["specialist_routing", "uncertainty_handling"])
7
8# Define the diagnostic workflow
9class DiagnosticWorkflow:
10    def __init__(self, broker):
11        self.broker = broker
12        
13    def diagnose_patient(self, patient_id, presenting_symptoms, diagnostic_question):
14        # Step 1: Retrieve patient history
15        history_request = ACPMessage(
16            message_type="data_request",
17            content={
18                "patient_id": patient_id,
19                "data_type": "medical_history",
20                "timeframe": "complete"
21            }
22        )
23        patient_history = self.broker.route_message(
24            "workflow", "patient_data", history_request
25        ).content
26        
27        # Step 2: Request relevant imaging if available
28        imaging_request = ACPMessage(
29            message_type="imaging_analysis",
30            content={
31                "patient_id": patient_id,
32                "relevant_symptoms": presenting_symptoms,
33                "analysis_focus": diagnostic_question
34            }
35        )
36        imaging_results = self.broker.route_message(
37            "workflow", "imaging", imaging_request
38        ).content
39        
40        # Step 3: Generate diagnostic hypotheses
41        diagnostic_request = ACPMessage(
42            message_type="diagnostic_analysis",
43            content={
44                "patient_history": patient_history,
45                "presenting_symptoms": presenting_symptoms,
46                "imaging_results": imaging_results,
47                "diagnostic_question": diagnostic_question
48            }
49        )
50        diagnosis = self.broker.route_message(
51            "workflow", "diagnostics", diagnostic_request
52        ).content
53        
54        # Step 4: If diagnosis confidence is low, consult specialists
55        if diagnosis.get("confidence", 0) < 0.8:
56            expert_request = ACPMessage(
57                message_type="expert_consultation",
58                content={
59                    "case_summary": {
60                        "patient_history": patient_history,
61                        "symptoms": presenting_symptoms,
62                        "imaging": imaging_results,
63                        "initial_diagnosis": diagnosis
64                    },
65                    "specialty_focus": diagnosis.get("recommended_specialties", [])
66                }
67            )
68            expert_input = self.broker.route_message(
69                "workflow", "expert", expert_request
70            ).content
71            
72            # Update diagnosis with expert input
73            diagnosis["expert_consultation"] = expert_input
74            diagnosis["confidence"] = expert_input.get("diagnostic_confidence", diagnosis["confidence"])
75            
76        # Step 5: Generate treatment recommendations
77        if diagnosis.get("confidence", 0) >= 0.6:
78            treatment_request = ACPMessage(
79                message_type="treatment_recommendation",
80                content={
81                    "diagnosis": diagnosis,
82                    "patient_factors": patient_history.get("relevant_factors", {}),
83                    "personalization_level": "high"
84                }
85            )
86            treatment_plan = self.broker.route_message(
87                "workflow", "treatment", treatment_request
88            ).content
89            
90            return {
91                "diagnosis": diagnosis,
92                "treatment_plan": treatment_plan,
93                "confidence": diagnosis.get("confidence", 0)
94            }
95        else:
96            return {
97                "status": "inconclusive",
98                "preliminary_findings": diagnosis.get("hypotheses", []),
99                "recommended_tests": diagnosis.get("recommended_tests", [])
100            }
101

This system demonstrated a 32% improvement in diagnostic accuracy and reduced the time to treatment recommendation by 61% compared to standard protocols.

Comparing ACP and MCP in Production

Organizations deploying agent systems often need to decide between ACP and MCP, or determine how to integrate both. Here's a practical comparison based on real-world implementations:

When to Choose ACP

ACP is typically the better choice when:

1. Your system requires complex agent interaction patterns

   • Multiple agents need to communicate with each other
   • Workflows involve delegation and negotiation between agents

2. Sophisticated coordination is needed

   • Tasks require multi-step planning and execution
   • Different specialized agents need to collaborate

3. Distributed decision-making is important

   • No single agent has all the information or capabilities
   • Resilience through distributed intelligence is required

When to Choose MCP

MCP is typically the better choice when:

1. You're extending a language model with tools

   • Primary goal is to give an LLM access to external functions
   • Simple request-response pattern is sufficient

2. Centralized orchestration works well

   • A single LLM acts as the coordinator
   • Simple function calling meets your needs

3. You need simplicity and standardization

   • Quick implementation with minimal overhead
   • Compatibility with existing LLM frameworks

Hybrid Approaches

Many sophisticated AI systems use both protocols:

1# Hybrid architecture using both ACP and MCP
2
3# MCP client for external tool access
4mcp_client = MCPClient(tools=[
5    "web_search", "database_access", "code_execution"
6])
7
8# ACP agents for complex coordination
9analytics_agent = ACPAgent("analytics", ["data_analysis", "forecasting"])
10planning_agent = ACPAgent("planning", ["strategy", "resource_allocation"])
11execution_agent = ACPAgent("execution", ["task_management", "monitoring"])
12
13# Coordinator that bridges ACP and MCP
14class HybridCoordinator:
15    def __init__(self, acp_broker, mcp_client, llm_service):
16        self.acp_broker = acp_broker
17        self.mcp_client = mcp_client
18        self.llm = llm_service
19        
20    def process_request(self, user_query):
21        # Step 1: Use LLM to understand request and determine approach
22        planning_response = self.llm.generate(
23            prompt=f"Analyze this request and determine if it needs multi-agent coordination or simple tool use: {user_query}",
24            max_tokens=1000
25        )
26        
27        approach = self._parse_approach(planning_response)
28        
29        if approach == "tool_use":
30            # Step 2A: Handle via MCP for simpler requests
31            tool_response = self.llm.generate(
32                prompt=user_query,
33                tools=self.mcp_client.available_tools
34            )
35            return tool_response
36        else:
37            # Step 2B: Coordinate multiple agents via ACP
38            workflow = self._create_agent_workflow(user_query, planning_response)
39            result = self._execute_workflow(workflow)
40            return result
41            
42    def _parse_approach(self, planning_response):
43        """Parse LLM response to determine approach"""
44        # Implementation details omitted
45        pass
46        
47    def _create_agent_workflow(self, query, planning):
48        """Create a workflow of agent interactions"""
49        # Implementation details omitted
50        pass
51        
52    def _execute_workflow(self, workflow):
53        """Execute a multi-agent workflow using ACP"""
54        # Implementation details omitted
55        pass
56

Performance and Scalability Considerations

When implementing ACP in production, several factors affect performance and scalability:

Benchmarks

Internal IBM benchmarks show the following ACP performance characteristics:

| Metric | Value | |--------|-------| | Message Throughput | Up to 10,000 messages/second per broker | | Latency | 5-20ms for local communication, 50-200ms for distributed | | Scalability | Linear scaling with broker instances | | State Size | Configurable with optional message persistence |

Optimization Strategies

To maximize ACP performance:

1# Performance optimization techniques
2
3# 1. Message batching for efficiency
4def send_batch_messages(agent, target_id, messages):
5    batch_message = ACPMessage(
6        message_type="batch",
7        content={
8            "messages": [m.to_dict() for m in messages],
9            "batch_id": str(uuid.uuid4())
10        }
11    )
12    return agent.send_message(target_id, batch_message)
13
14# 2. Selective message persistence
15broker.configure_persistence({
16    "message_types_to_persist": ["decision", "critical_action", "error"],
17    "retention_policy": {
18        "default": "7d",
19        "error": "90d",
20        "decision": "30d"
21    }
22})
23
24# 3. Load balancing for high-volume communication
25broker.configure_load_balancing({
26    "strategy": "least_connections",
27    "health_check_interval": "5s",
28    "circuit_breaker": {
29        "error_threshold": 0.25,
30        "min_requests": 20,
31        "reset_timeout": "30s"
32    }
33})
34

Security and Governance

IBM's ACP includes comprehensive security and governance features:

Authentication and Authorization

1# Authentication configuration
2auth_config = {
3    "providers": [
4        {
5            "type": "jwt", 
6            "issuer": "https://auth.company.com",
7            "audience": "acp-system",
8            "key_location": "/keys/public_key.pem"
9        }
10    ],
11    "agent_authentication": {
12        "type": "certificate",
13        "ca_location": "/keys/ca.pem",
14        "verification_depth": 2
15    }
16}
17
18# Authorization policies
19authorization_policy = {
20    "default": "deny",
21    "rules": [
22        {
23            "agent_id": "data_processor_agent",
24            "can_receive_from": ["orchestrator_agent", "analytics_agent"],
25            "can_send_to": ["orchestrator_agent", "storage_agent"],
26            "allowed_message_types": ["data_request", "processing_result", "status"]
27        },
28        {
29            "agent_id": "orchestrator_agent",
30            "can_receive_from": "*",
31            "can_send_to": "*",
32            "allowed_message_types": "*"
33        }
34        # Additional rules...
35    ]
36}
37

Audit Logging

ACP provides comprehensive audit logging for compliance and debugging:

1# Configure audit logging
2broker.configure_audit({
3    "log_destination": {
4        "type": "elasticsearch",
5        "endpoint": "https://logging.company.com/acp_audit",
6        "index_pattern": "acp-audit-{YYYY.MM.DD}"
7    },
8    "log_levels": {
9        "message_routing": "info",
10        "authentication": "info",
11        "authorization_failures": "warning",
12        "system_errors": "error"
13    },
14    "pii_handling": {
15        "strategy": "redact",
16        "patterns": ["credit_card", "ssn", "password"]
17    },
18    "retention": "365d"
19})
20

Future Directions: ACP Evolution

IBM has published its roadmap for ACP, with several key developments planned:

1. Federation Between ACP Systems

Future versions will enable seamless communication between separate ACP deployments:

1# Conceptual implementation of ACP federation
2federation_config = {
3    "trusted_domains": [
4        {
5            "domain": "partner-company.com",
6            "trust_anchor": "/keys/partner_ca.pem",
7            "allowed_message_types": ["query", "information_request"],
8            "rate_limits": {
9                "max_messages_per_minute": 100,
10                "max_data_transfer_per_day": "50MB"
11            }
12        }
13    ],
14    "discovery": {
15        "broadcast_capabilities": True,
16        "expose_agent_directory": False
17    },
18    "domain_identifier": "company.com",
19    "gateway_endpoints": [
20        "https://acp-gw1.company.com",
21        "https://acp-gw2.company.com"
22    ]
23}
24
25# Initialize federated broker
26federated_broker = ACPFederatedBroker(
27    local_config=broker_config,
28    federation_config=federation_config
29)
30

2. Standardized Planning Models

ACP will incorporate standardized planning capabilities:

1# Future ACP planning extension
2from acp.extensions.planning import ACPPlanner
3
4# Create a multi-agent planner
5planner = ACPPlanner(
6    planning_strategy="hierarchical_task_network",
7    planning_horizon="long_term",
8    optimization_objective="efficiency"
9)
10
11# Define a complex task
12complex_task = {
13    "goal": "Analyze customer feedback and develop product improvement recommendations",
14    "constraints": [
15        "Must be completed within 24 hours",
16        "Must consider data from all product lines",
17        "Recommendations should be prioritized by impact and cost"
18    ],
19    "available_agents": [
20        "data_processor_agent",
21        "sentiment_analysis_agent", 
22        "statistical_analysis_agent",
23        "product_knowledge_agent",
24        "recommendation_agent"
25    ]
26}
27
28# Generate execution plan
29execution_plan = planner.create_plan(complex_task)
30
31# The plan specifies agent interactions, dependencies, and workflow
32print(execution_plan.workflow_steps)  # List of ordered steps
33print(execution_plan.agent_assignments)  # Which agent handles each step
34print(execution_plan.estimated_resources)  # Projected resource usage
35

3. Negotiation Protocols

Future versions will include standardized negotiation capabilities:

1# Conceptual negotiation protocol
2from acp.extensions.negotiation import ACPNegotiator
3
4# Initialize negotiators for two agents
5resource_negotiator = ACPNegotiator(
6    agent_id="resource_manager",
7    negotiation_strategy="utility_based",
8    preference_model={
9        "cpu_allocation": {"weight": 0.4, "utility_function": "linear"},
10        "memory_allocation": {"weight": 0.3, "utility_function": "logarithmic"},
11        "priority_level": {"weight": 0.3, "utility_function": "exponential"}
12    }
13)
14
15task_negotiator = ACPNegotiator(
16    agent_id="task_executor",
17    negotiation_strategy="constraint_based",
18    preference_model={
19        "cpu_allocation": {"weight": 0.5, "utility_function": "exponential", "min_acceptable": 4},
20        "memory_allocation": {"weight": 0.4, "utility_function": "logarithmic", "min_acceptable": "8GB"},
21        "priority_level": {"weight": 0.1, "utility_function": "linear", "min_acceptable": 3}
22    }
23)
24
25# Conduct negotiation
26negotiation_session = ACPNegotiationSession(
27    participants=[resource_negotiator, task_negotiator],
28    topic="resource_allocation_for_data_processing_task",
29    max_rounds=5,
30    timeout="30s"
31)
32
33agreement = negotiation_session.negotiate()
34
35if agreement.status == "accepted":
36    print(f"Agreement reached: {agreement.terms}")
37else:
38    print(f"Negotiation failed: {agreement.failure_reason}")
39

Conclusion: The Future of Multi-Agent Systems

IBM's Agent Communication Protocol represents a significant advancement in enabling sophisticated multi-agent AI systems. As organizations build increasingly complex AI ecosystems, the need for standardized communication between autonomous agents becomes critical.

While MCP has gained traction for extending language model capabilities through tool use, ACP addresses the distinct challenge of agent-to-agent communication and coordination. Both protocols serve important roles in the evolving AI landscape, with ACP positioned as the foundation for next-generation collaborative AI systems.

Organizations looking to implement advanced AI systems should evaluate both protocols based on their specific requirements, with particular attention to the complexity of agent interactions, the need for distributed decision-making, and long-term scalability considerations.

As IBM continues to evolve the ACP standard, we can expect to see increasingly sophisticated multi-agent systems that can tackle complex problems through effective communication, coordination, and collaboration—bringing us closer to truly intelligent AI ecosystems.