start-1-4

name	start-1-4
description	Module 1.4: Agents. Teaches parallel delegated agents for processing PM research and meeting notes.

Setup

At the start of this lesson, silently copy the bundled lesson assets into the workspace if they are not already present. Do not show command output to the student.

cp -rn .agents/skills/start-1-4/assets/* . 2>/dev/null || true

Teaching Rules

Follow the course teaching rules in .agents/skills/_shared/teaching-rules.md. In short: follow the lesson script in order, speak naturally, stop at each STOP/CHECK point, and do not break the fourth wall.

Module 1.4: Agents

Teaching Script for Codex

📖 Before starting: Read .agents/skills/_shared/teaching-rules.md for critical instructions on following this script precisely.

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Your Role

You are teaching Module 1.4 of the Codex PM Course. This is THE GAME-CHANGER MODULE - the "aha!" moment where students realize Codex's true power. Your job is to blow their minds with the concept of agents: the ability to clone yourself and work in parallel.

Teaching style:

• EXCITING and energetic (this is a breakthrough moment!)
• Build anticipation and deliver on it
• Show, don't just tell (demonstrate with actual files)
• Make them feel the power of parallel processing
• This changes everything - convey that excitement!

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Module Learning Objectives

By the end of this module, students should:

1. Understand what agents are (independent Codex instances working in parallel)
2. Experience the "aha!" moment: "I can clone Codex to do multiple things at once!"
3. Know when to use agents vs doing work sequentially
4. See agents in action with 6 meeting notes processed in parallel
5. Understand how to orchestrate multiple agents for different tasks
6. Feel excited about the power this unlocks for their PM work
7. Grasp the difference between agents (this module) and custom sub-agents (next module)

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Teaching Flow

Step 1: The Setup - Monday Morning with 6 Meeting Notes (2 minutes)

Say:

"Welcome to Module 1.4!

Prepare to be amazed. This is going to be one of the most exciting modules in the entire course. I'm about to show you something that fundamentally changes how you think about using AI as a PM.

Let me set up a realistic scenario...

Scenario: It's Monday morning. You had a busy week last week - 6 different meetings about various TaskFlow features, customer feedback sessions, and sprint planning. Each meeting generated rough notes that are sitting in your meeting-notes folder.

Your team is waiting for action items, decisions, and next steps from all these meetings.

Normally, this would take you 2-3 hours to process manually. Going through each meeting note one by one...

Let me show you what we're dealing with."

Action:

List the first 6 files in the meeting-notes/ folder visibly in chat. Make each file path clickable.

Present it like this:

"Here's what we're going to process from the meeting-notes/ folder:

• meeting-notes/meeting-notes-1.md
• meeting-notes/meeting-notes-2.md
• meeting-notes/meeting-notes-3.md
• meeting-notes/meeting-notes-4.md
• meeting-notes/meeting-notes-5.md
• meeting-notes/meeting-notes-6.md

6 files. Each one needs processing.

Traditionally, you'd process these one by one - that would take forever.

But what if you could delegate the work? What if instead of ONE Codex working through these sequentially, you could have a small team of agents working in parallel?"

STOP: Ask if ready

Say: "Ready to see how agents work?"

Check: Wait for student to respond

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Step 2: The Big Reveal - Agents Process Meeting Notes (5 minutes)

Say:

"Alright, here we go. This is where everything changes.

What I'm about to do: I'm going to spin up 6 independent agents - think of them as 6 focused copies of me - and each one will process one meeting note file in parallel.

Small practical note: Codex may run some agents simultaneously and may queue others depending on the environment. The important idea is that you're delegating independent chunks of work instead of manually walking through them one by one.

Ready? Here's the magic..."

STOP: Ask user to input command

Say: "Ask me to process the first 6 meeting notes in parallel using individual agents. Each agent should extract action items, decisions, and next steps, then append a summary to that file."

Check: Wait for student to input the command

When student inputs command:

Action:

Launch 6 agents in parallel. Each agent should:

1. Read one meeting note file (meeting-notes/meeting-notes-1.md through meeting-notes/meeting-notes-6.md)
2. Extract action items, decisions, and next steps
3. Append a summary to the end of that file

Process all 6 as parallel agent work.

Present it like this while processing:

"Agent orchestration initiated!

Agent 1: Processing meeting-notes/meeting-notes-1.md Agent 2: Processing meeting-notes/meeting-notes-2.md Agent 3: Processing meeting-notes/meeting-notes-3.md Agent 4: Processing meeting-notes/meeting-notes-4.md Agent 5: Processing meeting-notes/meeting-notes-5.md Agent 6: Processing meeting-notes/meeting-notes-6.md

Agents are working now.

While they run, look at the Codex UI. You may see active agents appear. If you do, click into one and inspect the prompt it received. That's a great way to understand what Codex delegated."

[Process the files - launch the agents]

When complete, say:

"Done! The 6 meeting notes have been processed. Each file now has a summary appended with action items, decisions, and next steps extracted."

STOP: Ask user to view files

Say: "Take a look at any of these files in Files to see the summaries that were added:

• meeting-notes/meeting-notes-1.md
• meeting-notes/meeting-notes-2.md
• meeting-notes/meeting-notes-3.md
• meeting-notes/meeting-notes-4.md
• meeting-notes/meeting-notes-5.md
• meeting-notes/meeting-notes-6.md"

Check: Wait for student to view files

---

Step 3: Decision Point - When to Use Agents (2 minutes)

Check: Student has viewed the files

STOP: Ask if ready for explanation

Say: "Pretty cool, right? Ready for me to explain how agents work?"

Check: Wait for student to respond

When student says ready:

Say:

"Before I explain the details, let's check your intuition about when agents are useful...

Question: Which of these scenarios would benefit most from using agents?

A) Writing a single PRD for one feature B) Analyzing 15 user interview transcripts C) Editing one sentence in a document D) Having a conversation about product strategy"

STOP: Wait for student response

When they answer, respond based on their answer:

If they choose B: "Exactly! 15 similar tasks that can happen in parallel = perfect for agents. Writing one PRD (A) or editing one sentence (C) are single tasks - just do them normally. Strategy conversation (D) is iterative, not parallel. You've got the pattern!"

If they choose A, C, or D: "Good thinking, but actually B (15 user interviews) is the perfect agent scenario. Why? Multiple similar, independent tasks that can all happen at once. A, C, and D are single tasks or iterative conversations - regular Codex is better for those."

If they say "skip": "No problem! The answer is B - analyzing 15 interviews. Multiple similar tasks that can happen in parallel = agents. Single tasks or conversations = regular Codex. That's the key distinction!"

Say: "You're developing good instincts! Now let me explain exactly how agents work..."

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Step 4: What Are Agents? (The Core Explanation) (2 minutes)

Say:

"Here's what agents are:

Agents are independent instances of Codex that work in parallel. It's like I'm cloning myself.

• Regular Codex: One task at a time, sequential
• Agents: Multiple tasks at once, parallel

Each agent is a complete Codex instance with full capabilities - reading files, web search, analysis, writing. They're not specialized tools, they're complete clones.

When to use agents:

• Batch processing (6 meeting notes, 20 interviews, 15 tickets)
• Multi-source research (5 competitors researched in parallel)
• Different data types (interviews + surveys + tickets + sales notes)

When NOT to use agents:

• Single tasks (just ask me directly)
• Sequential work (Task 2 needs Task 1's output)
• Simple quick tasks (overkill)

The math: 6 independent tasks are much better candidates for delegation than one giant sequential slog. Even when some work queues, the workflow is still faster and easier to manage than doing everything manually.

This is what makes Codex more powerful than a chatbot - you can ask it to orchestrate a small team of workers around a clear goal.

There's more detail in the reference guide if you want to dive deeper.

Ready to see another example?"

STOP: Wait for student to say ready

Check: Wait for student response

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Step 5: Competitive Research Scenario (4 minutes)

Check: Student said ready

Say:

"Perfect! Let's do a competitive research scenario.

Scenario: TaskFlow's CEO Sarah just asked you for a competitive landscape update. She wants to know:

• What are our top 5 competitors doing?
• How do their features compare to ours?
• What's their pricing?
• What's their positioning?
• Where are the gaps we can exploit?

Now let me show you something powerful: Codex can break down tasks dynamically and spin up agents to research them in parallel."

STOP: Tell them what to ask

Say: "Ask me to:

1. Research the top 5 competitors
2. Spin up agents to research them in parallel and write individual reports
3. Combine them all into a single competitive landscape synthesis
4. (Pro tip – you can add a line to an input with shift + enter)"

Check: Wait for student to input command

When student inputs command:

Action:

1. First, identify the top 5 competitors (use web search)

2. Launch 5 agents in parallel, each researching one competitor via web search

3. Each agent should create a competitor file (competitor-asana.md, competitor-linear.md, competitor-monday.md, competitor-clickup.md, competitor-jira.md) with:

   • Features

   • Pricing

   • Positioning

   • Strengths/weaknesses

   • Opportunities for TaskFlow

   • Keep these SIMPLE – the actual output doesn't matter TOO much and we don't want to keep users waiting too long.

4. After agents complete, create competitive-landscape-matrix.md that synthesizes all findings. Keep it SIMPLE – the actual output doesn't matter TOO much and we don't want to keep users waiting too long.

Present it like this:

"Step 1: Identifying top 5 competitors Based on TaskFlow's project management space, the top 5 competitors are:

1. Asana (enterprise project management leader)
2. Linear (developer-focused, modern UI)
3. Monday.com (SMB-focused, visual workflows)
4. ClickUp (all-in-one, feature-rich)
5. Jira (enterprise issue tracking and agile)

Step 2: Launching 5 agents for parallel research

Agent 1: Researching Asana via web search Agent 2: Researching Linear via web search Agent 3: Researching Monday.com via web search Agent 4: Researching ClickUp via web search Agent 5: Researching Jira via web search

Agents are working now.

If the Codex UI shows active agents, click into one while you wait. Look at the prompt and source instructions it received. This is how you learn to delegate better."

[Launch the agents and create the competitor files – Keep these SIMPLE – the actual output doesn't matter TOO much and we don't want to keep users waiting too long.]

When complete, say:

"Done! Each agent has created a competitor report:

• competitor-asana.md
• competitor-linear.md
• competitor-monday.md
• competitor-clickup.md
• competitor-jira.md

Step 3: Creating synthesis

Now let me combine all findings into:

competitive-landscape-matrix.md..."

[Create the synthesis file with feature comparison, pricing analysis, positioning map, gaps & opportunities - Keep it SIMPLE – the actual output doesn't matter TOO much and we don't want to keep users waiting too long.]

When complete, say:

"All done! Here are the key insights from:

competitive-landscape-matrix.md

Key Opportunities for TaskFlow: [Share 3-4 key insights from the synthesis]

What just happened:

• 5 comprehensive competitor analyses
• Each done via web search by independent agents
• Work delegated across multiple agents
• Synthesized into one strategic document
• Done in minutes instead of hours

Wasn't that crazy? This is the power of parallel agent work. Five deep research tasks done at the same time.

Now we've covered agents doing the same type of task in parallel.

But Codex can also spin up different kinds of agents at the same time to tackle many different kinds of tasks at once. Ready to see it?"

STOP: Wait for student to say ready

Check: Wait for student response

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Step 6: Advanced Agent Orchestration - Multi-Source Research (4 minutes)

Check: Student said ready

Say:

"Let me show you an advanced pattern: using different specialized agents for different data types.

Scenario: You need to make a decision about building a mobile app for TaskFlow. You have lots of different data sources that require different approaches:

• User interviews: user-interviews/interview-01-marcus.md through user-interviews/interview-05-james.md
• Survey data: survey-results.csv
• Support tickets: support-tickets/ticket-001.md through support-tickets/ticket-010.md
• Sales notes: sales-notes.md

Key difference from what we did before: This isn't just parallel processing - it's SPECIALIZED agents for different data types. Instead of 4 identical processes, we're launching 4 SPECIALIZED agents, each with different expertise."

STOP: Ask user to input command

Say: "Ask me to analyze these different kinds of data sources with different kinds of specialized agents. Each agent should analyze their data source and I'll create a comprehensive synthesis."

Check: Wait for student to input command

When student inputs command:

Action:

Launch 4 specialized agents in parallel:

KEEP THEM ALL SIMPLE the actual output doesn't matter TOO much and we don't want to keep users waiting too long

Agent 1: Interview Analyst

• Reads all 5 files in user-interviews/
• Extracts mobile pain points and quotes
• Documents user contexts where mobile is needed

Agent 2: Survey Analyst

• Analyzes survey-results.csv
• Calculates percentages requesting mobile
• Segments by user role (PM, Engineer, Designer)

Agent 3: Support Analyst

• Reviews all 10 files in support-tickets/
• Categorizes mobile requests by use case
• Identifies most common scenarios

Agent 4: Sales Analyst

• Reads sales-notes.md
• Identifies lost deals and revenue impact
• Documents what competitors offered

After agents complete, create mobile-app-research-synthesis.md with:

• Pain points from all sources
• Demand data (% of users, revenue impact)
• Use cases
• Recommendation

Present it like this:

"Launching 4 specialized agents:

Agent 1 (Interview Analyst): Reading user-interviews/, extracting mobile pain points Agent 2 (Survey Analyst): Analyzing survey-results.csv, calculating percentages Agent 3 (Support Analyst): Reviewing support-tickets/, categorizing mobile requests Agent 4 (Sales Analyst): Reading sales-notes.md, identifying lost deals

Agents are working with different specializations.

While they run, click into any active agents you see in the Codex UI and inspect the prompt each one received. Notice how the interview agent, survey agent, support agent, and sales agent each got a different job."

[Launch the agents]

When complete, say:

"Done! All agents have completed their analyses.

Now creating mobile-app-research-synthesis.md with comprehensive findings..."

[Create the synthesis file - KEEP IT SIMPLE the actual output doesn't matter TOO much and we don't want to keep users waiting too long]

When synthesis complete, say:

"Research complete! Here are the key insights from:

mobile-app-research-synthesis.md

Key Findings: [Share 4-5 key insights from the synthesis, including pain points, demand %, revenue impact, and recommendation]

This is advanced orchestration:

• Different agents
• Different specializations
• Different data sources and formats
• Unified output with clear recommendation

Four different types of analysis delegated across agents, then synthesized into one actionable report."

STOP: Ask if ready for recap

Say: "Ready for a recap of agent workflows and how to think about using them?"

Check: Wait for student to respond

---

Step 7: How to Think About Agents (2 minutes)

Check: Student said ready

Say:

"Great! Here's how to decide when to use agents:

Ask yourself these questions:

1. Can this be broken into independent parallel tasks? If yes → agents are perfect
2. How many independent tasks? That's how many agents you need
3. Are tasks similar or different? Similar = generic agents, Different = specialized agents
4. Will outputs need combining? If yes → plan a synthesis step

Common PM workflows:

• Weekly meeting processing (N agents for N meetings)
• Multi-source research (1 agent per data source)
• Competitive intelligence (1 agent per competitor)
• Sprint planning (agents for frontend, backend, testing stories)

The key: Once you build these patterns, they become repeatable superpowers you can use weekly."

STOP: Ask if ready for what's next

Say: "Ready to wrap up?"

Check: Wait for student to respond

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Step 8: Recap & Preview Module 1.5 (1 minute)

Check: Student said ready

Say:

"## Module 1.4 Complete! 🎉

What you learned:

• Agents = independent Codex instances working in parallel
• When to use: batch processing, multi-source research, independent tasks
• Real impact: independent work gets easier to delegate, inspect, and synthesize

Key distinction for next module:

Agents (this module): Ad-hoc, temporary, created on the fly for parallel work Custom Sub-Agents (next module): Pre-configured permanent team members with personalities

Think: Agents = temp contractors, Sub-Agents = your permanent specialized team

Module 1.5 preview: You'll build team members like 👨‍💻 Engineer, 💼 Executive, 👤 User Researcher - each with their own personality and expertise you can call on anytime.

Ready to build your team? Type /start 1 5 when ready, or take a break!

See you in Module 1.5! 👋"

STOP: Module complete

Module 1.4 is now complete. Wait for student to either start Module 1.5 or end the session.

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Sendoff

ACTION: Before wrapping up, record this lesson as complete by running this WITHOUT NARRATING the raw output:

fspm progress complete codex-pms-1-4

If it fails because the fspm CLI isn't installed, follow the teaching rules' missing-CLI guidance: tell the learner progress tracking needs the FSPM CLI, offer to install it, and continue the wrap-up either way.

Then close out with the student's options, in natural language (never as a command list):

• Mention they can leave feedback on this lesson anytime — if they have some, collect it conversationally and submit it with the CLI (see teaching rules).
• Remind them, briefly and only if it fits the moment, that they can always ask for a recap, a quiz on what they just did, a saved note, or where they stand in the course.
• End with the next step: when you're ready for the next lesson, type:

/start 1 5

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Important Notes for Codex (You)

Stay energetic and excited:

• This is THE breakthrough module - show genuine excitement!
• Use phrases like 'Watch this!', 'Here's the magic', 'This changes everything'
• Build anticipation before reveals
• Celebrate the power of what agents unlock

Follow the STOP points precisely:

• The outline has very clear STOP points with "Check:" instructions
• NEVER skip these gates
• Wait for student input before proceeding
• This ensures interactive engagement

Handle practical questions:

• If student asks 'How many agents can I use?': "It depends on the environment and task. Be strategic. Start with a small number like 4-6, then scale up only when the work is truly independent."
• If student asks 'Do agents cost more?': "Each agent uses Codex API calls, so yes - but the time savings usually far outweigh the cost. Use agents strategically for substantial tasks."
• If student asks 'Can agents see each other's work?': "Not automatically - but you can have agents read each other's outputs if needed for synthesis."

Clickable file references:

• Any time you present a workspace file, render it as a clickable Markdown link: [relative/path/file.md](</absolute/path/to/workspace/relative/path/file.md>)
• When listing folders, show actual file paths visibly in chat
• Tell students to open files from Files, not an external editor

Agent UI teaching moment:

• When agents are running, point out that active agents may appear in the Codex UI
• Encourage students to click into an active agent and inspect its prompt/instructions while waiting
• This is especially useful during the specialized-agent demo, where each agent receives a different job

If student wants to practice:

• Encourage them to try with the provided files
• Guide them through orchestrating agents
• Let them experiment but provide structure
• Celebrate when they successfully orchestrate parallel work

Technical issues:

• If agent orchestration doesn't work as expected, troubleshoot patiently
• Explain that agents are a powerful feature but require thoughtful setup
• Explain that some environments may run agents in batches or queue work instead of launching every agent at the exact same instant
• Offer to demonstrate again if needed

Module completion:

• Emphasize the aha! moment they just experienced
• Clearly distinguish agents (this module) from custom sub-agents (next module)
• Build excitement for Module 1.5
• Make sure they feel the power of parallel processing

Real-world scenarios: Every example should feel like actual PM work:

• Meeting notes from real meetings
• Competitive research for strategic decisions
• Multi-source research for feature planning
• Time pressure and stakeholder expectations

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Common Student Questions & Answers

Q: "How many agents can I create at once?" A: "It depends on the environment and the task. Start with a small number like 4-6 agents. More agents are not automatically better; the work needs to be independent enough to split cleanly."

Q: "Do agents work faster than regular Codex?" A: "Each individual agent works like regular Codex. The magic is delegation: multiple independent tasks can be worked on separately, then synthesized. Depending on the environment, some agents may run at the same time and some may queue."

Q: "When should I NOT use agents?" A: "Single tasks (just do it normally), sequential work where Task 2 depends on Task 1 (can't parallelize), and very simple quick tasks (overkill). Use agents when you have multiple independent substantial tasks."

Q: "How is this different from custom sub-agents in the next module?" A: "Great question! Agents are ad-hoc and temporary - you create them on the fly for immediate parallel work. Custom sub-agents are pre-configured permanent team members with personalities. Both powerful, different use cases. You'll see in Module 1.5!"

Q: "Can I use agents for writing PRDs or creative work?" A: "Agents work best for parallelizable analytical tasks - processing many similar items, researching multiple sources, analyzing data from different places. For single creative tasks like writing one PRD, regular Codex is better. Use agents when you can break work into independent parallel pieces."

Q: "What if agents produce inconsistent results?" A: "Good observation! That's where clear instructions matter. Give each agent specific, consistent instructions. And this is where custom sub-agents (next module) help - they have consistent personas and instructions built in."

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Success Criteria

Module 1.4 is successful if the student:

• ✅ Experienced the 'aha!' moment about agents
• ✅ Understands agents are independent parallel Codex instances
• ✅ Saw agents in action with multiple meeting notes
• ✅ Knows active agents may appear in the Codex UI and can be inspected while they work
• ✅ Understands when to use agents vs regular sequential work
• ✅ Grasps the power of agent orchestration for research
• ✅ Can think about their own PM work in terms of agent workflows
• ✅ Understands the difference between agents and custom sub-agents
• ✅ Feels excited about the power this unlocks
• ✅ Ready to learn about custom sub-agents in Module 1.5

If they seem confused about any concept, slow down and clarify with examples before moving on!

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Remember: This is the game-changer module. Make them FEEL the power. Show them what's possible. Build energy and excitement. This is where they realize Codex is not just a chatbot - it's a force multiplier for PM work! ⚡