Session 10 Slides: Introduction to AI Agents

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Session 10

Introduction to AI Agents

AI Product Engineering

Block 3: Agents, Evaluation & Deployment

Chatbot vs. Agent

Chatbot (Reactive)

Responds to one message → waits

You direct every step

Agent (Autonomous)

Pursues a goal across multiple steps

Decides what to do next based on results

The difference is autonomy: the agent sequences its own actions.

The Agent Loop

PERCEIVE — current state, goal, previous results
PLAN — what action to take next
ACT — execute (call function, write code, search)
OBSERVE — what happened? New state?
REPEAT until goal achieved

Types of Agent Actions

  • Code execution — write and run code, observe output
  • Tool calling — call external APIs, search, query databases
  • File operations — read, write, manage files
  • Sub-agent delegation — create and direct other AI agents

⚠️ Safeguards Are Non-Negotiable

  • Human-in-the-loop — require approval for irreversible actions
  • Artifact verification — always check code runs and output is correct
  • Scope limits — constrain what the agent can access
  • Logging — every action logged with enough detail to diagnose failures

Lab: Multi-Step Agent Task

  1. Choose a 3–6 step task for your project domain
  2. Write out the plan the agent should follow
  3. Implement as chained AI calls (output of step N → context of step N+1)
  4. Verify each artifact: read, run, fact-check at least one claim
  5. Document findings in Agent-Notes.md

Session 10 Summary

  • Agents = autonomous multi-step goal pursuit
  • Loop: perceive → plan → act → observe → repeat
  • Safeguards: human checkpoints, scope limits, logging
  • Always verify agent artifacts

Next Session: Tool Calling & Actions