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# How AI Agents Plan, Remember, and Collaborate

## Learning goals

- Define an AI agent in plain language
- See the agent loop
  Plan → Act → Observe → Update
- Spot planning + feedback
  with a number-guessing demo
- See multi-agent collaboration
  with a comedy duo demo

## What is an AI agent?

- An entity that pursues a goal and decides what to do next
- Core loop
  Plan, Act, Observe, Update (with memory)
- Common ingredients
  Short-term memory (state)
  Optional tools (e.g., search, code, APIs)
- More than a chatbot

## Demo 1 — Number guessing: planning + feedback

- Setup
  Agent A holds a secret number (1–100)
  Agent B guesses; feedback is 'too high/low'
- Agentic view
  Plan: pick midpoint
  Memory: track [low, high]
  Observe: feedback
  Update: shrink interval

## Failure mode: noisy feedback

- Flip a hint (say 'too high' once when it's not)
- Effect
  Inconsistent interval; plan becomes brittle
  Agent may recover slowly or fail
- Lesson
  Agents inherit the reliability of their signals

## Demo 2 — Comedy duo: coordination + roles

- Setup
  Two agents alternate with a theme (e.g., coffee)
  Short-term memory: build on prior line
- Simple strategy
  Turn-taking + style guardrails = coherent output
- Agentic view
  Roles steer behavior
  State = last few lines
  Coordination emerges without hard-coding jokes

## From toy to real workflows

- Same loop scales to
  research helpers
  data pipelines
  classroom tutors, graders, lab partners
- Design levers
  Explicit roles and goals
  Tight feedback signals
  State logging and resets

## Responsible use & guardrails

- Validate feedback channels; detect contradictions
- Constrain roles, tone, topics (age-appropriate themes)
- Log interactions; reset state between runs

## Try it yourself

- Web app
  Number guessing + comedy duo