Overview
This lecture explains the unique features of AI agents, when to use them compared to other AI approaches, and how to build a simple AI agent for practical tasks like report generation.
What Makes AI Agents Unique
- AI agents build on large language models (LLMs) with additional features: reasoning, planning, memory, system instructions, and tool access.
- Unlike simple chatbots, AI agents can plan, make decisions, use tools, and perform tasks autonomously.
- System instructions specify the agent’s task, role, and workflow details.
- Memory systems allow short-term retention of conversation and potential long-term learning.
- Tool integration enables AI agents to interact with external systems through APIs (e.g., Google Analytics, Google Sheets).
When to Use AI Agents vs. Other Approaches
- Use simple AI chatbots for basic, structured tasks needing high human involvement (e.g., drafting emails).
- Use AI-assisted workflows for complex tasks where you combine multiple tools with human oversight.
- Choose fully automated AI workflows for predictable, structured, low-risk tasks without much need for human supervision.
- Opt for AI agents when tasks are complex, require multiple steps, and you are comfortable allowing higher autonomy and risk.
Example: Monthly Report Generation
- AI chatbot: Needs manual data input and can't access external tools directly.
- AI-assisted workflow: Export data manually, prompt AI to process, high human involvement.
- Automated AI workflow: Predefined steps automate data retrieval, processing, and output, with minimal human input.
- AI agent: Integrates reasoning, memory, and tool use to autonomously retrieve, analyze data, generate reports, and perform follow-up actions.
How to Build a Simple AI Agent
- Choose a no-code platform (e.g., n8n).
- Define workflow with input/output nodes and system instructions.
- Connect an LLM (e.g., Claude), configure memory settings, and set token limits.
- Integrate necessary tools (e.g., Google Analytics, Google Sheets).
- Add modules for output format (e.g., HTML report) and task automation (e.g., FTP upload).
- Test the workflow and adjust system instructions for clarity and scope.
Decision Framework for Using AI Agents
- Assess task complexity: structured/simple vs. multi-step/creative.
- Evaluate control level: human involvement vs. agent autonomy.
- Consider risk tolerance: more autonomy brings scalability but also higher risk.
Key Terms & Definitions
- AI Agent — A software system that can plan, reason, remember, and autonomously perform tasks using tools and data.
- LLM (Large Language Model) — Advanced AI model trained on large text datasets to generate human-like language.
- System Instructions — Guidelines defining the agent’s task, role, and scope.
- Memory — The agent's ability to recall previous interactions (short-term) or save information for future use (long-term).
- Agentic Workflow — A process where AI agents autonomously complete complex, multi-step tasks.
Action Items / Next Steps
- Review your own task requirements using the decision framework to determine if an AI agent is suitable.
- Explore AI agent templates on n8n or similar platforms.
- Consider the risk, complexity, and need for autonomy before building or deploying an AI agent.