McKay Johns

AI Agents

Learn how to build intelligent agents that can reason, plan, and execute complex tasks autonomously. Master the art of creating AI systems that can interact with tools, maintain context, and solve multi-step problems.

Core Agent Concepts

Agent Architecture

Understanding the core components of AI agents is fundamental to building effective systems. Agents consist of several key components:

  • Perception: How agents gather information from their environment
  • Reasoning: Decision-making and problem-solving capabilities
  • Action: Executing tasks and interacting with tools
  • Memory: Maintaining context and learning from experience

Tool Integration

Connecting agents to external systems and APIs enables them to perform real-world tasks. Effective tool integration includes:

  • Function calling and API integration
  • Web scraping and data retrieval
  • Database queries and data manipulation
  • File system operations and code execution

Multi-Step Reasoning

Breaking down complex problems into manageable steps is essential for handling sophisticated tasks. Key approaches include:

  • Chain-of-thought reasoning patterns
  • ReAct (Reasoning + Acting) methodology
  • Planning and goal decomposition
  • Error handling and recovery strategies

Agent Workflows

Designing efficient agent execution patterns determines how well your agents perform. Consider these patterns:

  • Sequential vs parallel task execution
  • Hierarchical agent systems
  • Agent collaboration and communication
  • Performance optimization and monitoring

What Makes a Good AI Agent?

Effective AI agents share several key characteristics:

Goal-Oriented

A good agent clearly understands objectives and works systematically toward achieving them. It maintains focus on the end goal while adapting its approach as needed.

Self-Reflective

Effective agents monitor their own performance and adjust strategies when needed. They can recognize when an approach isn't working and try alternatives.

Tool-Capable

Good agents effectively use available tools and integrate with external systems. They understand which tools to use for which tasks and how to combine multiple tools effectively.

Agent Prompt Examples

Basic Agent Prompt

You are an AI assistant that can help users with various tasks. You have access to the following tools:

1. search_web(query) - Search the internet for information
2. calculate(expression) - Perform mathematical calculations
3. write_file(filename, content) - Write content to a file

When solving problems:
1. Break down the task into steps
2. Use the appropriate tools when needed
3. Explain your reasoning process
4. Double-check your work before providing final answers

User request: [USER_INPUT]

Let me think through this step by step:

ReAct Agent Pattern

You are a research assistant that follows the ReAct (Reasoning + Acting) pattern.

For each user request, follow this cycle:
Thought: Analyze what needs to be done
Action: Choose and execute a specific action
Observation: Review the results of the action
(Repeat until task is complete)

Available actions:
- search(query): Search for information
- analyze(data): Analyze data or content  
- summarize(text): Create concise summaries
- calculate(expression): Perform calculations

Example format:
Thought: I need to find information about X to answer this question.
Action: search("X information")
Observation: Found relevant data about X...
Thought: Now I need to analyze this data to extract key insights.
Action: analyze(data)
Observation: The analysis shows...

User request: [USER_INPUT]

Multi-Agent Coordinator

You are a coordinator managing a team of specialized AI agents:

1. Research Agent: Gathers and verifies information
2. Analysis Agent: Processes data and identifies patterns
3. Writing Agent: Creates well-structured content
4. Review Agent: Ensures quality and accuracy

Your job is to:
- Delegate tasks to appropriate agents
- Coordinate between agents
- Synthesize results into coherent outputs
- Ensure all requirements are met

For complex tasks, create a plan that leverages each agent's strengths.

Task: [USER_INPUT]

Plan:

Agent Design Best Practices

Start with Clear Objectives

Define what success looks like and establish clear stopping criteria for your agent. Without clear objectives, agents can wander aimlessly or continue working when the task is complete.

Implement Error Handling

Build robust error handling and recovery mechanisms to handle tool failures gracefully. Agents should be able to detect when something goes wrong and either retry with a different approach or report the issue clearly.

Maintain Context Memory

Keep track of previous actions and results to inform future decisions. Effective agents learn from their actions and avoid repeating mistakes or unnecessary work.

Test Edge Cases

Thoroughly test your agents with unexpected inputs and challenging scenarios. Edge cases often reveal weaknesses in agent design that don't appear during normal operation.

System ArchitectureLLM Fundamentals