Agents

What is an Agent?

Agents are intelligent programs that can autonomously solve complex problems by combining reasoning, memory, and tool usage. Unlike traditional Large Language Models (LLMs) that generate responses based on prompts, agents can take actions, make decisions, and adapt their approach based on changing contexts.

LLMs vs Agents: Key Differences

Large Language Models (LLMs)

• Purpose: Generate text based on input prompts
• Interaction: Single request-response cycle
• Capabilities: Text generation, analysis, summarization
• Memory: No persistent memory between conversations
• Tools: Cannot use external tools or APIs
• Decision Making: Limited to generating appropriate responses
• Autonomy: Requires human guidance for each step

Agents

• Purpose: Autonomously solve complex, multi-step problems
• Interaction: Continuous, iterative problem-solving process
• Capabilities: Planning, reasoning, tool usage, task execution
• Memory: Persistent memory across interactions and sessions
• Tools: Can use APIs, databases, web search, code execution, etc.
• Decision Making: Can plan, execute, evaluate, and adapt strategies
• Autonomy: Can work independently with minimal human intervention

Levels of Agent Autonomy

Instead of a binary definition, agents exist on a spectrum of autonomy and capability:

Level 0: Basic LLM (No Agency)

• Simple prompt-response interaction
• No tools or persistent memory
• Example: Basic chatbot for answering questions

Level 1: Tool-Enabled Agents

• Can use external tools and APIs
• Basic autonomous task execution
• Limited memory and planning

Level 2: Knowledge Agents

• Persistent memory and learning
• Domain-specific knowledge integration
• Advanced reasoning and context awareness

Level 3: Multi-Agent Systems

• Teams of specialized agents
• Complex workflow orchestration
• Collaborative problem-solving

Agent Design Patterns

1. ReAct Pattern (Reasoning + Acting)

Alternates between reasoning about the problem and taking actions.

def react_loop(agent, task: str) -> str:
    """ReAct pattern implementation"""
    state = {"task": task, "completed": False}
    
    while not state["completed"]:
        # Reason about current state
        thought = agent.reason(state)
        
        # Decide on action
        action = agent.plan_action(thought, state)
        
        # Execute action
        observation = agent.execute_action(action)
        
        # Update state
        state = agent.update_state(state, thought, action, observation)
    
    return state["result"]

2. Plan-Execute Pattern

Creates a comprehensive plan before execution.

def plan_execute(agent, goal: str) -> str:
    """Plan-Execute pattern implementation"""
    plan = agent.create_detailed_plan(goal)
    
    for step in plan.steps:
        result = agent.execute_step(step)
        if not result.success:
            plan = agent.replan(plan, result.error)
    
    return agent.compile_final_result(plan)

3. Reflection Pattern

Continuously evaluates and improves performance.

def reflection_loop(agent, task: str) -> str:
    """Reflection pattern implementation"""
    attempt = 1
    max_attempts = 3
    
    while attempt <= max_attempts:
        result = agent.attempt_task(task)
        evaluation = agent.self_evaluate(result, task)
        
        if evaluation.meets_criteria():
            return result
        
        feedback = agent.generate_self_feedback(evaluation)
        agent.incorporate_feedback(feedback)
        attempt += 1
    
    return result  # Return best attempt

Building Effective Agents

Key Principles

1. Clear Goal Definition: Agents need well-defined objectives and success criteria
2. Robust Error Handling: Agents must gracefully handle failures and adapt
3. Efficient Tool Usage: Strategic selection and usage of available tools
4. Memory Management: Effective storage and retrieval of relevant information
5. Continuous Learning: Ability to improve performance over time

Common Challenges

1. Tool Selection: Choosing the right tool for each subtask
2. Context Management: Maintaining relevant context across long interactions
3. Error Recovery: Handling failures and finding alternative approaches
4. Performance Optimization: Balancing thoroughness with efficiency
5. Safety and Reliability: Ensuring agents behave predictably and safely

Implementation Frameworks

Popular frameworks for building agents include:

Coding Frameworks

• LangChain / LangGraph: Comprehensive framework with extensive tool integrations
  • LangChain Docs
  • LangGraph Docs
• CrewAI: Multi-agent collaboration framework
  • CrewAI Docs
• Agno: Reasoning Agents, Multimodal Agents, Teams of Agents and Agentic Workflows
  • Agno Docs
• PydanticAI: Build agents with Pydantic models
  • PydanticAI Docs

Low Code Frameworks

• Langflow: Build agents with a visual interface
  • Langflow Docs
• N8N: Build agents with a visual interface
  • N8N Docs
• Make.com: Build agents with a visual interface
  • Make.com Docs

Other Frameworks

• AutoGPT: Autonomous agent framework with goal-driven execution
  • AutoGPT Docs