Chapter 4: Agent Types & Patterns

Types of AI Agents

AI agents can be categorized based on their capabilities, complexity, and interaction patterns. Understanding these types helps in selecting the right approach for specific use cases.

Agent Classification by Capability

🔄 Reactive Agents

Basic "input → output" behavior with no memory or planning

Characteristics:

• Stateless operation
• Immediate response to current input
• No planning or goal decomposition
• Simple stimulus-response mapping

Use Cases:

• Simple Q&A chatbots
• Basic text generation
• Single-turn conversations
• Immediate response systems

Examples:

• Vanilla LLM interactions (GPT-3.5, Claude)
• Basic customer service bots
• Simple text completion tools

Implementation Pattern:

class ReactiveAgent:
    def __init__(self, llm):
        self.llm = llm
 
    def respond(self, input_text):
        return self.llm.generate(input_text)

🛠️ Tool-Using Agents

Uses external tools/APIs via function calling

Characteristics:

• Can call predefined functions
• Enhanced capabilities through external services
• Basic tool selection logic
• Structured function call formatting

Use Cases:

• Weather information retrieval
• Calculator functions
• Database queries
• API integrations

Examples:

• GPT-4 with function calling
• Claude with tools
• ChatGPT plugins

Tool Integration Pattern:

class ToolUsingAgent:
    def __init__(self, llm, tools):
        self.llm = llm
        self.tools = {tool.name: tool for tool in tools}
 
    def execute(self, query):
        # Determine if tools are needed
        response = self.llm.generate_with_tools(query, self.tools)
 
        if response.requires_tool:
            tool_result = self.tools[response.tool_name].execute(
                response.tool_args
            )
            return self.llm.synthesize(query, tool_result)
 
        return response.text

🧠 Planning Agents

Plans multi-step actions to accomplish goals

Characteristics:

• Task decomposition capabilities
• Sequential reasoning
• Tool chaining strategies
• Adaptive execution

Planning Strategies:

• Chain of Thought: Step-by-step reasoning
• Tree of Thoughts: Exploring multiple paths
• ReAct: Reasoning + Acting pattern
• Plan-and-Execute: Separate planning and execution phases

Use Cases:

• Research projects
• Multi-step problem solving
• Workflow automation
• Complex data analysis

Examples:

• AutoGPT
• ReAct agents
• LangChain planning agents

🧠💾 Memory-Augmented Agents

Retains and recalls context from past interactions

Memory Types:

• Working Memory: Current task context
• Episodic Memory: Specific interaction history
• Semantic Memory: General knowledge
• Procedural Memory: Task execution patterns

Storage Technologies:

• Vector databases (Pinecone, Weaviate, Chroma)
• Graph databases (Neo4j)
• Traditional databases with embeddings
• File-based storage systems

Use Cases:

• Personal assistants
• Customer support with history
• Long-term project management
• Personalized recommendations

🤖 Autonomous Agents

Operates independently with feedback loops

Characteristics:

• Self-correcting behavior
• Goal-oriented operation
• Minimal human supervision
• Adaptive learning mechanisms

Key Features:

• Performance monitoring
• Error recovery
• Dynamic strategy adjustment
• Continuous improvement

Examples:

• Autonomous software developers (Devin)
• Self-managing system agents
• Autonomous research assistants

👥 Multi-Agent Systems

Coordinates multiple specialized agents

Coordination Patterns:

• Hierarchical: Manager-worker relationships
• Peer-to-peer: Equal collaboration
• Pipeline: Sequential processing
• Market-based: Competitive task assignment

Communication Protocols:

• Direct messaging
• Shared memory spaces
• Event-driven notifications
• Auction-based coordination

Examples:

• CrewAI teams
• MetaGPT organizations
• LangGraph multi-agent workflows

Agent Design Patterns

🔄 ReAct Pattern (Reasoning + Acting)

Core Concept: Alternates between reasoning and action steps

Flow:

1. Thought: Agent reasons about the problem
2. Action: Agent executes a specific tool/function
3. Observation: Agent observes the result
4. Repeat: Continue until goal achieved

Example Implementation:

class ReActAgent:
    def execute_task(self, goal):
        thoughts = []
        while not self.goal_achieved(goal):
            # Reasoning step
            thought = self.reason(goal, thoughts)
            thoughts.append(thought)
 
            # Action step
            if thought.requires_action:
                action_result = self.execute_action(thought.action)
                thoughts.append(f"Observation: {action_result}")
 
        return self.synthesize_result(thoughts)

Benefits:

• Transparent reasoning process
• Error correction through observation
• Flexible tool usage
• Debuggable execution

🌲 Tree of Thoughts Pattern

Core Concept: Explores multiple reasoning paths simultaneously

Process:

1. Generate multiple initial approaches
2. Evaluate each approach's potential
3. Expand promising paths further
4. Prune unsuccessful branches
5. Select optimal solution path

Use Cases:

• Complex problem solving
• Strategic planning
• Creative tasks
• Optimization problems

🎯 Goal-Oriented Pattern

Architecture Components:

• Goal Stack: Hierarchical objective management
• Planner: Action sequence generation
• Executor: Action implementation
• Monitor: Progress tracking

Goal Types:

• Achievement goals (reach specific state)
• Maintenance goals (preserve conditions)
• Performance goals (optimize metrics)
• Avoidance goals (prevent outcomes)

🏭 Agent Factory Pattern

Purpose: Standardized agent creation and management

Components:

• Agent templates and configurations
• Resource allocation mechanisms
• Lifecycle management
• Performance monitoring

Benefits:

• Consistent agent deployment
• Efficient resource utilization
• Scalable agent provisioning
• Centralized configuration

🔄 Feedback Loop Pattern

Learning Cycle:

1. Action: Perform task
2. Measurement: Collect metrics
3. Analysis: Evaluate performance
4. Adjustment: Modify behavior
5. Iteration: Apply improvements

Learning Mechanisms:

• Reinforcement learning from rewards
• Supervised learning from examples
• Unsupervised pattern discovery
• Meta-learning (learning to learn)

Agent Architecture Patterns

🏛️ Layered Architecture

Layers:

1. Presentation Layer: User interfaces and APIs
2. Application Layer: Business logic and workflows
3. Domain Layer: Core reasoning and knowledge
4. Infrastructure Layer: Tools, databases, external services

Benefits:

• Clear separation of concerns
• Modular design and testing
• Reusable components
• Maintainable codebase

🎪 Orchestrator Pattern

Components:

• Central Orchestrator: Workflow coordination
• Agent Registry: Available agent catalog
• Task Queue: Work distribution
• Result Aggregator: Output combination

Workflow Types:

• Sequential execution chains
• Parallel processing branches
• Conditional logic flows
• Loop-based iterations

🔀 State Machine Pattern

Elements:

• States: Discrete behavioral modes (idle, processing, learning)
• Transitions: State change rules
• Events: Transition triggers
• Actions: State-specific behaviors

Example State Flow:

Idle → (task received) → Processing
Processing → (task complete) → Learning
Learning → (update complete) → Idle
Any State → (error) → Error
Error → (recovery) → Idle

Framework Implementations

LangChain Agent Types

CrewAI Patterns

Role-Based Teams:

crew = Crew(
    agents=[
        researcher_agent,
        analyst_agent,
        writer_agent
    ],
    tasks=[
        research_task,
        analysis_task,
        writing_task
    ],
    process=Process.sequential
)

Multi-Agent Coordination

Hierarchical Structure:

Selecting the Right Pattern

Decision Framework

Consider These Factors:

1. Task Complexity: Simple vs. multi-step workflows
2. Autonomy Requirements: Human oversight vs. independent operation
3. Integration Needs: Number and complexity of external tools
4. Memory Requirements: Stateless vs. persistent context
5. Scalability: Single instance vs. distributed systems
6. Real-time Constraints: Response time requirements

Pattern Selection Guide

Performance Considerations

Optimization Strategies

Response Time:

• Cache common operations
• Parallel tool execution
• Lazy loading of resources
• Efficient memory retrieval

Resource Usage:

• Agent pooling and reuse
• Memory cleanup strategies
• Tool connection management
• Graceful degradation

Reliability:

• Error handling and recovery
• Retry mechanisms with backoff
• Circuit breaker patterns
• Monitoring and alerting

Best Practices

Development Guidelines

1. Start Simple: Begin with reactive agents, add complexity gradually
2. Clear Interfaces: Define explicit agent APIs and contracts
3. Comprehensive Testing: Test each agent type thoroughly
4. Monitor Performance: Track metrics and user satisfaction
5. Plan for Scale: Design with growth in mind

Deployment Considerations

1. Security: Implement proper authentication and authorization
2. Monitoring: Real-time performance and error tracking
3. Versioning: Manage agent updates and rollbacks
4. Documentation: Maintain clear usage guides and examples

What's Next?

In the next chapter, we'll explore Human-AI Collaboration and learn how to design systems where humans and agents work together effectively.