AI agent intro

Introduction to AI Agents

AI agents represent a significant shift from AI that merely suggests to AI that acts. They are software systems that use AI to autonomously pursue goals and complete multi-step tasks on behalf of users—with minimal human oversight.

What You Need to Know

To understand and work with agents effectively, you should focus on these five areas:

• Agent basics: How they work and when to use them.
• Workflow design: Breaking tasks into agent-friendly steps.
• Tool integration: Connecting agents to your systems.
• Testing & validation: Ensuring reliable execution.
• Monitoring: Tracking agent performance over time.

The Key Difference

The primary difference is that traditional AI (like ChatGPT) waits for a prompt and responds to one question, while agents take action and work independently to achieve objectives.

Traditional AI (ChatGPT)

• Waits for your prompt
• Responds to one question
• No follow-up action
• Can’t use tools independently
• Requires human for each step

AI Agents

• Initiates tasks autonomously
• Completes multi-step workflows
• Uses tools and APIs
• Makes decisions independently
• Reports back when complete

Real-World Agent Applications & Results

• Customer Support Agent: Handles tickets end-to-end by reading inquiries and searching knowledge bases. Result: 65% reduction in support tickets.
• Sales Research Agent: Researches prospects, finds contact info, and drafts outreach. Result: 4 hours saved per seller weekly.
• Data Analysis Agent: Pulls data from multiple sources, cleans it, and runs analysis.
• HR Onboarding Agent: Creates accounts, sends welcome emails, and tracks completion.

Important Considerations

• Not magic: Agents need clear instructions and guardrails.
• Error handling: Plan for failures and edge cases.
• Human oversight: Critical tasks still need review.
• Start simple: Begin with one workflow, then expand.
• Iteration required: Refine based on real-world use.

source: Tina Huang, https://www.youtube.com/watch?v=sHDCgAWWpNQ