Introduction to AI Agents (Datacamp)

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Introduction

Artificial Intelligence (AI) agents are systems designed to think, plan, and act autonomously to achieve specific goals. They combine reasoning, decision-making, and tool use to interact effectively with their environment. The following notes outline the foundations of AI agents, including their design patterns, frameworks, tools, responsibilities, and best practices for building and using them responsibly.

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 Foundations of AI Agents

• What is an AI Agent?
• An agent is a system that leverages on AI model to interact with its environment to achieve a user-defined objective. It combines reasoning, planning, and the execution of actions (after via external tools) to fulfill tasks.
• An AI model capable of reasoning, planning, and acting on a set of actions by interacting with its environment.
•  When to use AI Agents? Criteria
1. Require complex decision-making
2. Require heavy reliance on unstructured data
3. Have difficult to maintain rules
4. Require adaptive problem solving

Agentic Design Patterns and Architectures

• The Thought-Action-Observation Cycle
• Thought: The model decides the next step based on the user prompt. 
• Action: The agent takes an action by calling the tools at their disposal
• Observation: The model reflects on the response from the tool. Feeding into the next set of thoughts and actions.
• ReAct Framework: Reasoning and Acting
• is part of the model's system prompt. System prompt are hidden instructions that tell the model how to behave throughout all conversations.
• ReAct is especially useful on "traditional" language models like the GPT-series of models.
• Newer generation reasoning models are explicitly trained to think step by step, and don't need ReAct prompting. 
• What's in the (Tool) Box?
• Extensions - Tools that connect agents to the outside world.
• Application Programming Interface - A set of rules that lets different software system talks to each other and share data or functionality in a structured way. 
• Model Context Protocol - An open standard that lets AI assistants connect to external data sources and tools through a unified interface. 
• Functions - Tools that lets agents execute a specific set of code.
• Data Stores - Tools that lets agents retrieve information from databases and documents
• Single Agent Systems - A single model equipped with appropriate tools and instructions executes workflows in a loop.
• Multi-Agent Systems - execute workflows across multiple coordinated agents

Building and Using AI Agents Responsibility

• Input Guardrails
• Relevance Classifier
• Safety Classifier
• Moderation
• Rules-Based Projections
• Tool-Based Guardrails
• Tool Safeguards
• Output Guardrails
• PII Filters
• Output Validation
• Agentic Systems in the Real World
• Best Practices Using Off-Shelf Agentic Tools
• Design useful prompts with context
• Effective Prompt Patterns
• Detailed examples of what good looks like
• Context for the task being worked on
• AI-Assisted coding use-case: Updating code
• Detailed examples of what final code should look like
• Context of the original code, and why it needs updating
• Understand the agent's capabilities and limitations
• Always verify your agent's output
• Always be mindful of costs
• Use AI agents responsibly
• Best Practices for Designing and Building AI Agents
• Always design for human intervention
• Do you really need an agent?
• Always be mindful of costs
• Start simple and iterate
  
• Monitor Everything

Conclusion 

AI agents represent a major step forward in intelligent automation, enabling systems to reason, adapt, and collaborate with humans more effectively. Understanding their foundations and responsible design ensures they are used safely and efficiently to enhance real-world applications.