AI Agents and Multi-Agent Systems: Building Intelligent Autonomous Applications
The rise of Large Language Models (LLMs) has ushered in a new era of AI applications. Beyond simple chatbots, we're now building AI agents - autonomous systems that can perceive their environment, make decisions, and take actions to achieve specific goals. When multiple agents work together, they form multi-agent systems that can tackle complex problems no single agent could solve alone.
What Are AI Agents?
An AI agent is an autonomous entity that:
- Perceives its environment through sensors or APIs
- Reasons about the information it receives
- Plans sequences of actions to achieve goals
- Acts on the environment to effect change
- Learns from outcomes to improve future performance
Unlike traditional software that follows predetermined paths, AI agents can adapt their behavior based on context and experience.
Core Components of an AI Agent
1. Perception Module
class PerceptionModule:
def __init__(self):
self.sensors = []
self.data_processors = []
def perceive(self, environment):
raw_data = self.gather_sensor_data(environment)
processed_data = self.process_data(raw_data)
return self.extract_features(processed_data)
2. Memory System
Agents need both short-term (working) and long-term memory:
class AgentMemory:
def __init__(self):
self.short_term = deque(maxlen=100) # Recent observations
self.long_term = VectorDatabase() # Persistent knowledge
self.episodic = [] # Past experiences
def remember(self, information, importance=0.5):
self.short_term.append(information)
if importance > 0.7:
self.long_term.store(information)
3. Reasoning Engine
The brain of the agent that makes decisions:
class ReasoningEngine:
def __init__(self, llm_model):
self.llm = llm_model
self.tools = ToolRegistry()
async def reason(self, context, goal):
# Generate potential actions
actions = await self.generate_actions(context, goal)
# Evaluate each action
evaluated = []
for action in actions:
score = await self.evaluate_action(action, context)
evaluated.append((action, score))
# Select best action
return max(evaluated, key=lambda x: x[1])[0]
Building a Simple AI Agent
Let's build a research agent that can autonomously gather information on a topic:
from langchain.agents import Tool, AgentExecutor
from langchain.memory import ConversationSummaryMemory
from langchain.chat_models import ChatOpenAI
from langchain.agents import initialize_agent, AgentType
class ResearchAgent:
def __init__(self):
self.llm = ChatOpenAI(temperature=0.7)
self.memory = ConversationSummaryMemory(llm=self.llm)
self.tools = self._setup_tools()
self.agent = self._initialize_agent()
def _setup_tools(self):
return [
Tool(
name="Web Search",
func=self.search_web,
description="Search the web for information"
),
Tool(
name="Read Webpage",
func=self.read_webpage,
description="Extract content from a webpage"
),
Tool(
name="Summarize",
func=self.summarize_text,
description="Summarize long text"
)
]
def _initialize_agent(self):
return initialize_agent(
self.tools,
self.llm,
agent=AgentType.CHAT_CONVERSATIONAL_REACT_DESCRIPTION,
memory=self.memory,
verbose=True
)
async def research(self, topic):
prompt = f"""Research the topic: {topic}
1. Search for recent information
2. Read relevant sources
3. Summarize key findings
4. Provide citations
"""
return await self.agent.arun(prompt)
Multi-Agent Systems Architecture
When agents work together, they need coordination mechanisms:
1. Communication Protocol
class AgentCommunication:
def __init__(self):
self.message_queue = asyncio.Queue()
self.agents = {}
async def register_agent(self, agent_id, agent):
self.agents[agent_id] = agent
async def send_message(self, from_agent, to_agent, message):
await self.message_queue.put({
'from': from_agent,
'to': to_agent,
'message': message,
'timestamp': datetime.now()
})
async def broadcast(self, from_agent, message):
for agent_id in self.agents:
if agent_id != from_agent:
await self.send_message(from_agent, agent_id, message)
2. Task Coordination
class TaskCoordinator:
def __init__(self):
self.task_queue = PriorityQueue()
self.agent_capabilities = {}
def decompose_task(self, complex_task):
# Break down complex task into subtasks
subtasks = self.llm.generate_subtasks(complex_task)
return subtasks
def assign_task(self, task, available_agents):
# Match task requirements with agent capabilities
best_agent = None
best_score = 0
for agent in available_agents:
score = self.calculate_match_score(
task.requirements,
self.agent_capabilities[agent.id]
)
if score > best_score:
best_score = score
best_agent = agent
return best_agent
Real-World Example: Customer Support System
Let's build a multi-agent customer support system:
class CustomerSupportSystem:
def __init__(self):
self.triage_agent = TriageAgent()
self.technical_agent = TechnicalSupportAgent()
self.billing_agent = BillingAgent()
self.escalation_agent = EscalationAgent()
self.coordinator = TaskCoordinator()
async def handle_request(self, customer_request):
# Triage agent analyzes the request
analysis = await self.triage_agent.analyze(customer_request)
# Route to appropriate specialist
if analysis.category == "technical":
response = await self.technical_agent.resolve(customer_request)
elif analysis.category == "billing":
response = await self.billing_agent.resolve(customer_request)
else:
response = await self.escalation_agent.handle(customer_request)
# Quality check
if not response.satisfactory:
response = await self.coordinate_agents(customer_request)
return response
async def coordinate_agents(self, request):
# Multiple agents collaborate
context = await self.gather_context(request)
solutions = await asyncio.gather(
self.technical_agent.propose_solution(context),
self.billing_agent.check_account(context),
self.escalation_agent.find_precedent(context)
)
# Synthesize best response
return await self.synthesize_response(solutions)
Advanced Patterns
1. Hierarchical Agent Systems
class HierarchicalAgentSystem:
def __init__(self):
self.ceo_agent = StrategicAgent()
self.manager_agents = [ManagerAgent() for _ in range(3)]
self.worker_agents = [WorkerAgent() for _ in range(9)]
async def execute_strategy(self, goal):
# CEO breaks down strategic goal
strategic_plan = await self.ceo_agent.create_plan(goal)
# Managers create tactical plans
tactical_plans = []
for i, objective in enumerate(strategic_plan.objectives):
plan = await self.manager_agents[i].create_tactical_plan(objective)
tactical_plans.append(plan)
# Workers execute tasks
results = []
for plan in tactical_plans:
for task in plan.tasks:
worker = self.assign_worker(task)
result = await worker.execute(task)
results.append(result)
return self.aggregate_results(results)
2. Competitive Multi-Agent Systems
class MarketSimulation:
def __init__(self, num_traders=10):
self.traders = [TradingAgent(i) for i in range(num_traders)]
self.market = Market()
async def run_simulation(self, timesteps=1000):
for t in range(timesteps):
# Each agent makes decisions independently
orders = []
for trader in self.traders:
market_state = self.market.get_state()
order = await trader.decide_action(market_state)
orders.append(order)
# Market processes all orders
self.market.process_orders(orders)
# Agents learn from outcomes
for trader in self.traders:
await trader.learn_from_outcome(self.market.get_state())
Best Practices for Agent Development
1. Safety and Alignment
class SafetyModule:
def __init__(self):
self.constraints = []
self.safety_checks = []
def add_constraint(self, constraint):
self.constraints.append(constraint)
async def validate_action(self, action, context):
for check in self.safety_checks:
if not await check(action, context):
return False, f"Failed safety check: {check.__name__}"
return True, "Action approved"
2. Monitoring and Observability
class AgentMonitor:
def __init__(self):
self.metrics = defaultdict(list)
self.alerts = []
def track_metric(self, agent_id, metric_name, value):
self.metrics[f"{agent_id}:{metric_name}"].append({
'value': value,
'timestamp': datetime.now()
})
def check_anomalies(self):
for metric_key, values in self.metrics.items():
if self.is_anomalous(values):
self.trigger_alert(metric_key, values)
3. Testing Agent Systems
class AgentTestFramework:
def __init__(self):
self.test_environments = []
self.test_scenarios = []
async def test_agent(self, agent, scenario):
env = self.create_test_environment(scenario)
results = []
for step in scenario.steps:
action = await agent.act(env.get_state())
outcome = env.execute(action)
results.append({
'step': step,
'action': action,
'outcome': outcome,
'success': self.evaluate_outcome(outcome, step.expected)
})
return TestReport(results)
Tools and Frameworks
Popular Agent Frameworks
- LangChain - Comprehensive framework for LLM applications
- AutoGPT - Autonomous agent with internet access
- BabyAGI - Task-driven autonomous agent
- Microsoft AutoGen - Multi-agent conversation framework
- CrewAI - Role-based agent collaboration
Example with CrewAI
from crewai import Agent, Task, Crew
# Define specialized agents
researcher = Agent(
role='Senior Research Analyst',
goal='Uncover cutting-edge developments in AI',
backstory='You work at a leading tech think tank...',
verbose=True
)
writer = Agent(
role='Tech Content Writer',
goal='Create engaging content about AI developments',
backstory='You are a renowned content creator...',
verbose=True
)
# Define tasks
research_task = Task(
description='Research the latest AI breakthroughs',
agent=researcher
)
writing_task = Task(
description='Write a comprehensive article about the findings',
agent=writer
)
# Create and run crew
crew = Crew(
agents=[researcher, writer],
tasks=[research_task, writing_task],
verbose=True
)
result = crew.kickoff()
Future Directions
The field of AI agents is rapidly evolving:
- Embodied Agents - Agents with physical presence in robotics
- Swarm Intelligence - Large-scale coordination of simple agents
- Adaptive Learning - Agents that continuously improve through experience
- Cross-Domain Transfer - Agents that apply knowledge across different domains
- Human-AI Collaboration - Seamless integration of human and AI capabilities
Conclusion
AI agents and multi-agent systems represent a paradigm shift in how we build intelligent applications. By combining the reasoning capabilities of LLMs with autonomous action and multi-agent coordination, we can create systems that tackle complex, open-ended problems.
Whether you're building a simple task automation agent or a complex multi-agent marketplace, the key is to start simple, test thoroughly, and gradually increase complexity. As these systems become more sophisticated, they'll play an increasingly important role in everything from scientific research to business operations.
The age of AI agents has just begun, and the possibilities are limitless.