Agent Architecture Patterns

Welcome to the foundation of agentic AI systems! In this module, we'll explore the fundamental patterns and architectures that enable AI agents to operate autonomously, make decisions, and interact with their environment effectively.

By the end of this module, you will:

• Understand core agent architecture patterns and their appropriate use cases
• Compare reactive, deliberative, and hybrid agent designs
• Implement basic ReAct and Chain-of-Thought patterns
• Design agent architectures for specific problem domains
• Evaluate trade-offs between different architectural approaches

An AI agent is a system that can perceive its environment, make decisions, and take actions to achieve specific goals. Unlike traditional software that follows predetermined paths, agents exhibit autonomy, reactivity, proactivity, and adaptability.

Core Agent Characteristics

1. Autonomy: Operates without constant human intervention
2. Reactivity: Responds to environmental changes
3. Proactivity: Takes initiative to achieve goals
4. Social Ability: Interacts with other agents or humans
5. Learning: Improves performance over time

Building Agents in Pure Python: The Professional Approach

Before diving into frameworks, it's crucial to understand the fundamental patterns through pure Python and direct LLM API calls. This approach offers several advantages:

Master the Fundamentals First

• Work directly with LLM APIs to understand underlying principles
• Build production systems with full control over the process
• Create more reliable, maintainable systems
• Develop custom business logic without framework constraints

When to Use Pure Python

• Building production systems requiring reliability
• Need full control over agent behavior and data flow
• Custom business logic that doesn't fit standard patterns
• Performance and security are critical requirements
• Team has strong Python skills and wants maximum flexibility

Core Philosophy: Most real-world cases don't require complex frameworks - pure Python is often sufficient and superior for production environments.

Before exploring specific patterns, let's establish the core building blocks for professional Python agents using direct LLM API calls.

Essential Building Blocks

1. Direct API Communication

2. Structured Output with Pydantic

3. Tool Use and Function Calling

4. Memory and Context Management

Professional Workflow Patterns

1. Prompt Chaining Pattern

2. Intelligent Routing Pattern

3. Parallel Processing Pattern

This foundation provides the essential patterns for building production-ready agents in pure Python, giving you full control over behavior, performance, and security.

1. Reactive Agent Systems

Reactive agents operate on simple stimulus-response patterns, making immediate decisions based on current perceptions without complex internal reasoning.

Characteristics

• Fast response times: Immediate reactions to stimuli
• Simple decision logic: Rules-based or direct mappings
• No internal state: Stateless or minimal state tracking
• Event-driven: Triggered by environmental changes

Use Cases

• Real-time control systems
• Chatbot responses to specific keywords
• Automated trading alerts
• IoT device responses

Code Example: Simple Reactive Agent

2. Deliberative Agent Systems

Deliberative agents maintain internal models of their environment and use planning algorithms to determine the best course of action to achieve their goals.

Characteristics

• World modeling: Maintains internal representation of environment
• Goal-oriented: Works toward specific objectives
• Planning capabilities: Reasons about sequences of actions
• State management: Tracks progress and intermediate states

Use Cases

• Strategic game playing
• Resource allocation optimization
• Multi-step task automation
• Long-term planning systems

Code Example: Deliberative Planning Agent

3. Hybrid Agent Architectures

Hybrid agents combine reactive and deliberative approaches, using fast reactive responses for immediate concerns while maintaining planning capabilities for complex goals.

Architecture Layers

1. Reactive Layer: Immediate response to urgent stimuli
2. Deliberative Layer: Planning and reasoning for complex goals
3. Coordination Layer: Manages interaction between layers

Benefits

• Responsiveness: Quick reactions to urgent situations
• Intelligence: Strategic planning for complex problems
• Robustness: Graceful degradation under different conditions
• Efficiency: Appropriate response complexity for each situation

The ReAct (Reasoning and Acting) pattern is a powerful approach that synergizes reasoning and acting in language model agents. It interleaves reasoning traces and task-specific actions, allowing for dynamic reasoning and better interaction with external environments.

ReAct Cycle

1. Thought: Reason about the current situation
2. Action: Take a specific action based on reasoning
3. Observation: Observe the results of the action
4. Repeat: Continue until goal is achieved

Code Example: ReAct Agent Implementation

4. Chain-of-Thought Reasoning

Chain-of-Thought (CoT) prompting enables language models to perform complex reasoning by explicitly showing intermediate reasoning steps.

Types of CoT

1. Zero-shot CoT: "Let's think step by step"
2. Few-shot CoT: Provide examples with reasoning steps
3. Auto-CoT: Automatically generate reasoning chains

Code Example: Chain-of-Thought Implementation

5. Multi-Agent Systems

Multi-agent systems involve multiple autonomous agents working together to achieve individual or collective goals.

Coordination Patterns

1. Hierarchical: Master-slave relationships
2. Peer-to-peer: Equal agents collaborating
3. Market-based: Agents negotiate and trade
4. Blackboard: Shared knowledge space

Code Example: Multi-Agent Coordinator

Use Case	Recommended Pattern	Rationale
Real-time responses	Reactive	Fast, deterministic responses
Complex planning	Deliberative	Requires reasoning about future states
Mixed requirements	Hybrid	Best of both reactive and deliberative
Reasoning tasks	ReAct/CoT	Explicit reasoning improves accuracy
Collaborative work	Multi-agent	Specialized agents for different tasks

Architecture Trade-offs

Reactive Systems

Pros: Fast, simple, predictable Cons: Limited reasoning, brittle to unexpected inputs

Deliberative Systems

Pros: Intelligent planning, handles complexity Cons: Slower, computationally expensive

Hybrid Systems

Pros: Balanced performance, robust Cons: Complex to design and debug

Begin with reactive patterns and add complexity only when needed.

2. Clear Separation of Concerns

Keep perception, reasoning, and action modules separate.

3. Robust Error Handling

Plan for failure modes and graceful degradation.

4. Observability

Implement logging and monitoring for agent decisions.

5. Human Oversight

Design clear interfaces for human intervention when needed.

Adding unnecessary complexity too early in development.

2. Poor State Management

Not properly maintaining world state in deliberative agents.

3. Infinite Loops

ReAct agents getting stuck in reasoning loops.

4. Agent Conflicts

Multi-agent systems with competing objectives.

5. Lack of Fallbacks

No graceful degradation when primary systems fail.

Test your understanding of agent architecture patterns:

Question 1

Which agent pattern is best suited for immediate responses to environmental changes?

A) Deliberative
B) Reactive
C) Hybrid
D) Multi-agent

Answer: B) Reactive

Reactive agents are designed for immediate stimulus-response patterns, making them ideal for situations requiring fast responses to environmental changes.

Question 2

What are the main components of the ReAct pattern?

A) Perception, Planning, Execution
B) Thought, Action, Observation
C) Input, Processing, Output
D) Sense, Think, Act

Answer: B) Thought, Action, Observation

The ReAct pattern specifically uses this three-step cycle: reasoning about the situation (Thought), taking an action (Action), and observing the results (Observation).

Question 3

Which architecture pattern combines reactive and deliberative approaches?

A) Multi-agent
B) Chain-of-Thought
C) Hybrid
D) ReAct

Answer: C) Hybrid

Hybrid architectures specifically combine reactive systems for immediate responses with deliberative systems for complex planning and reasoning.

Time: 30 minutes

Choose a real-world scenario (e.g., smart home automation, customer service bot, trading system) and design an appropriate agent architecture. Consider:

1. Environmental factors and inputs
2. Required response times
3. Complexity of decision-making
4. Need for planning vs. reactive responses
5. Human interaction requirements

Document your design decisions and trade-offs.

Exercise 2: Implement ReAct Agent

Time: 45 minutes

Build a ReAct agent that can:

1. Answer questions using web search
2. Perform calculations
3. Access a simple database of facts
4. Show its reasoning process

Extend the provided ReAct code example to handle more complex multi-step problems.

Agent architecture patterns provide the foundation for building effective autonomous AI systems. Key takeaways:

• Reactive agents excel at immediate responses but lack reasoning capabilities
• Deliberative agents can plan and reason but are slower to respond
• Hybrid architectures balance reactive and deliberative approaches
• ReAct pattern enables transparent reasoning in language model agents
• Multi-agent systems coordinate specialized agents for complex workflows

The choice of architecture depends on your specific requirements for response time, reasoning complexity, and environmental uncertainty.

In the next module, we'll dive deeper into Building Planning Systems, where you'll learn how to implement sophisticated planning algorithms that enable agents to reason about complex, multi-step tasks and adapt their strategies based on changing conditions.

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• ReAct: Synergizing Reasoning and Acting in Language Models
• Multi-Agent Systems: A Modern Approach
• Agent Architecture Design Patterns
• LangChain Agent Documentation