We track Product so you don't have to. Top Podcasts summarised, the latest AI tools, plus research and news in a 5 min digest.

Hey everyone. I'm starting to move the pod shots to standalone web articles because they're getting a bit long for the newsletter and it’s easier to read them like this I think. I’ll start to migrate the backlog in due course, but let me know what you think. Hopefully easier to digest and dig into the ones you want to get back to.

The current backlog link is below. Whether you need a quick refresher, want to preview an episode, or need to get up to speed fast - we've got you covered.

Check it out here

🤖 The Multi-Agent Coding Revolution: How to Build Apps with AI Teams That Work in Parallel

This is a bit of a longer tutorial style this week. It’s based on a recent Peter Yang interview with Kieran Klaassen, founder of Cora AI, who goes deep on the art of managing multiple AI agents to code simultaneously. The less technical of you are definitely going to want to watch the full tutorial on YouTube but others can probably get going with my run-through below. Plus, given the hype and buzz around agents and vibe-coding I thought it a good one to include so you know what’s possible. Let me know if you want more/less!

Here’s the TL;DR:

If you've ever felt overwhelmed by the technical complexity of building software, this might be the breakthrough you've been waiting for. While most of us have been doing "vibe coding"—that back-and-forth collaboration with AI on every line of code—Kieran Klaassen has evolved to something far more powerful: agentic coding.

"It's like I'm a manager and I have like a team of people that are very capable," explains Kieran, founder of Cora, a beautiful AI email assistant. "The work when doing software or building stuff is not code. It's way more. It's research. It's product marketing. It's even understanding what you should build."

Don't worry if you've never coded before or feel intimidated by AI development tools. This approach is actually more accessible than traditional programming because you're managing and directing rather than writing complex code. Think of it like being a project manager who delegates tasks to highly skilled team members—except those team members are AI agents that work 24/7 and never get tired.

In this detailed walkthrough, Kieran demonstrates exactly how to orchestrate multiple AI agents using Claude Code, complete with the specific commands, workflows, and mental models that let you ship production-ready features in minutes instead of weeks.

 🎥Watch the full episode here

📆 Published: July 20th, 2025

🕒 Estimated Reading Time: 4mins. Time saved: 59 mins!🔥

🏗️ The Mental Shift: From Coding Assistant to AI Team Manager

The fundamental difference between vibe coding and agentic coding isn't technical—it's managerial. Instead of micromanaging every line of code, you're delegating entire features to capable agents and reviewing their work like a senior executive reviewing department reports.

What This Means for Non-Technical People

If you've ever managed a team or delegated projects, you already have the core skills for agentic coding:

• Breaking down big problems into smaller, manageable tasks

• Writing clear instructions that others can follow independently

• Reviewing work quality and providing feedback for improvements

• Coordinating multiple workstreams to avoid conflicts and delays

"If your directions are good, if you're clear in your communication about what problem needs to be solved and how to go about it, they can deliver results to you," Kieran notes. "You're still giving feedback, but you're giving feedback on a pull request or on like a larger change."

The Manager's Mindset

This shift requires thinking like a tech lead or engineering manager, but the skills translate from any management experience:

• Delegate outcomes, not processes: Tell agents what you want achieved, not how to achieve it

• Provide context and constraints: Share the bigger picture and any limitations

• Set up quality control systems: Create processes to catch and fix issues

• Orchestrate without micromanaging: Let agents work independently while maintaining oversight

Key Takeaways:
• Management skills transfer directly - If you can manage people, you can manage AI agents
• Clarity enables autonomy - Better specifications lead to better autonomous execution
• Parallel execution scales impact - Multiple agents working simultaneously multiply your output

This is a biggie so to read on more I’ve created a separate article - follow the link below

🛠️ Setting Up Your AI Development Environment (No Coding Experience Required)

The Foundation: Claude Code Installation

Good news: Getting started is surprisingly simple. You don't need to be a programmer to set this up. Kieran uses Warp terminal, but any computer terminal works (think of it like a text-based way to give your computer commands):

Step 1: Install Claude Code

1. Visit the Claude Code website

2. Copy the installation command (it's just one line of text)

3. Paste it into your terminal and press enter

4. Wait for it to install (usually takes 1-2 minutes)

Step 2: Start Your First Session

1. Type claude in your terminal

2. Press enter

3. You'll see a blank space where you can type what you want the AI to do

The interface is intentionally minimal—just a blank slate where you describe what you want built. "This gives me peace because there's just one thing I can do here really, which is type what I want it to do," Kieran explains.

Essential Workflow Optimisations (Optional but Powerful)

Kieran has optimised his setup for maximum efficiency. These are advanced techniques you can add later as you get comfortable:

Speech-to-Text Integration:
Kieran uses Monologue (a speech-to-text tool) because "I stopped typing like more than a year or maybe two years ago whenever whisper launched." This means he literally talks to his AI agents instead of typing—much faster and more natural.

I’ve talked about this before and use Better Dictation. Super Whisper is also more feature rich and generally excellent and I hear good things about Wisperflow. Bottom line is talking to your computer instead of typing is the biggest efficiency unlock I know

Custom Shortcuts:

• WT command: Creates separate workspaces for different projects

• CC command: Launches Claude Code quickly

• Custom aliases: Shortcuts for common tasks

Git Worktrees for Parallel Development:
This is a technical concept, but think of it like having multiple project folders where different AI agents can work simultaneously without interfering with each other. "If there's one changing a file and the other one is changing the same file, they that will be messy."

Key Takeaways:
• Start simple, optimise later - Begin with basic Claude Code, add advanced features as needed
• Voice input is a game-changer - Much faster than typing once you're comfortable
• Separate workspaces prevent conflicts - Like giving each team member their own office

🎯 The Research-First Approach: Planning Before Building

This is where non-technical people often have an advantage over programmers. Traditional coders want to jump straight into writing code. But the best approach is comprehensive planning—something product managers and business leaders excel at.

Step 1: Comprehensive Research Phase

Here's Kieran's exact research prompt structure (notice how it's written in plain English):

Research building an email management system with Gmail integration, AI summarization, and a user interface. Break this into separate components and create detailed specifications for each part that different developers could work on in parallel.  
  
For each component, create a detailed specification document that includes:  
- Problem definition and requirements  
- Technical approach recommendations  
- Implementation steps and considerations  
- Dependencies and integration points  
  
Save each specification as a separate markdown file so I can reference them when directing individual agents.  

What Claude Creates:

• email-storage-spec.md - Complete requirements for database and Gmail integration

• summarization-spec.md - Detailed plan for AI email processing service

• ui-spec.md - User interface requirements and design considerations

Translation for non-technical readers:

• "I want to build an email management system"

• "Please research the best approach and create detailed blueprints"

• "Break it into separate components that different team members can work on"

• "Give me written specifications I can hand to each team member"Here's Kieran's exact research prompt structure (notice how it's written in plain English):

I want to research a feature and use the Gmail client and Ruby LM gem. 

I want to add models - database models for emails that will store the emails from the Gmail client.

Then I want a service that will take an email and summarise the email for me using the Ruby LM gem.

And third, I want a UI that I can read through these emails.

Can you do research on what you would recommend and maybe you can make these three separate issues and you can do them in parallel using sub agents?

Translation for non-technical readers:

• "I want to build an email management system"

• "It should connect to Gmail and store emails in a database"

• "It should automatically summarise emails using AI"

• "It should have a user interface where I can read the summaries"

• "Please research the best way to build this and break it into separate tasks"

Step 2: Use "Magic Words" to Improve AI Output

Kieran uses specific trigger phrases that dramatically improve AI reasoning:

• "Think ultra hard": Makes the AI spend more time analysing complex problems

• "Create todos": Generates step-by-step task lists to keep agents focused

• "Running parallel": Enables multiple AI agents to work simultaneously

These phrases are like management techniques—they guide the AI's approach to problem-solving.

Step 3: Document Everything as Project Specifications

The research phase produces detailed documents that serve as blueprints:

• Problem definition and context

• Technical approach and architecture decisions

• Implementation steps and considerations

• Dependencies and integration points

"I always like document things like this and use them then to kick off another agent," Kieran explains. This creates a paper trail of decisions and ensures consistency across your project.

Key Takeaways:
• Planning prevents problems - Comprehensive research saves time and prevents costly mistakes
• Use "magic words" strategically - Specific phrases unlock better AI performance
• Document everything - Creates reusable blueprints and maintains project context

⚡ Parallel Execution: Running Multiple AI Agents Simultaneously

This is where the magic happens. Instead of building features one at a time (which could take significant time), you can have multiple AI agents building different components simultaneously. It's like having a development team that works 24/7 without breaks.

Understanding Parallel Work (Non-Technical Explanation)

Imagine you're renovating a house:

• Traditional approach: Finish the plumbing completely, then do electrical, then painting

• Parallel approach: Have the plumber, electrician, and painter work simultaneously in different rooms

The parallel approach is much faster, but requires coordination to avoid conflicts (like the painter working in a room where the electrician is still installing outlets).

Setting Up Parallel Workspaces

For each feature, you create a separate workspace (these are Kieran’s "git worktrees," just think of them as project folders):

Example: Building an Email Management System

Workspace 1: Database Setup

• Agent focuses on storing and organising emails

• Works independently without interfering with other agents

Workspace 2: Email Processing Service

• Agent builds the AI summary feature

• Can work simultaneously with Workspace 1

Workspace 3: User Interface

• Agent creates the screens users will interact with

• Develops in parallel with both other workspaces

Giving Instructions to Each Agent

In each workspace, you provide the agent with:

1. The project specification: "Here's what this feature should do"

2. Clear implementation instructions: "Build this specific component"

3. Quality directives: "Think carefully about all the steps needed"

Example instruction for the UI agent:

I want you to implement the user interface for reading emails. Currently the database and email processing are being worked on by other team members, so just create mock data for now. I want to see the UI working even though the backend isn't ready yet.

Reference: @ui-specification.md

Managing Dependencies Between Features

When features depend on each other, use this approach:

• Independent features: Can run completely in parallel

• Dependent features: Tell agents to "assume this exists" and create temporary mock data

• Integration phase: Combine completed features and connect them properly

"The beauty is we had research done as a holistic thing. So you can say, 'Hey, like assume this exists,'" Kieran notes.

Key Takeaways:
• Parallel work multiplies productivity - Three agents working simultaneously = 3x faster development
• Mock dependencies to avoid blocking - Don't let missing pieces stop progress
• Integration happens after individual completion - Combine and connect features in a final phase

🔍 Advanced Techniques: Custom Commands and Quality Control

Creating Reusable Workflows (Slash Commands)

As you get more experienced, you can create shortcuts for common tasks. Kieran has built a library of "slash commands" that automate complex workflows:

# Research and planning
/research-feature [description] 

# Code quality checks
/review-security
/review-performance  
/review-best-practices

# Feature implementation
/implement-feature [specification-file]

Think of these like templates or macros - they contain sophisticated instructions that you can reuse across projects. Each command includes:

• Specific prompts that work well for that type of task

• Quality criteria and best practices

• Output formatting instructions

• Context gathering from your existing project

The Power of Context and Examples

One of the biggest advantages for non-technical people is that you can provide business context that programmers often miss. Kieran emphasises grounding AI decisions in real examples:

"The best research done is... really grounding it in things and things you can use is like reference blog posts where you think I really like this style or this pattern or like what are best practices."

Essential Context Sources:

• Existing products you admire: "Build something like Slack's notification system"

• Your company's style and voice: "Use our brand guidelines and tone"

• User feedback and requirements: "Our customers specifically asked for this workflow"

• Business constraints: "This needs to work on mobile and load quickly"

Quality Control Through Multiple Reviews

Kieran runs multiple review passes on generated code, each with a different focus:

# Security-focused review
/fix-critical-issues

# User experience review
/review-usability

# Business requirements check
/verify-requirements

Each review uses a different "perspective" or "hat":

• Security expert mindset: "What could go wrong?"

• User experience designer: "Is this intuitive and easy to use?"

• Business stakeholder: "Does this solve the actual problem?"

• Performance optimiser: "Will this be fast enough for real users?"

Key Takeaways:
• Build reusable workflows - Create templates for tasks you do repeatedly
• Provide business context - Your domain knowledge is invaluable for AI agents
• Multiple review perspectives catch more issues - Different viewpoints find different problems

🚀 Real-World Example: Building an Email Management System

Let's walk through Kieran's actual demo, building a simplified version of his Cora email assistant with three parallel features. This example shows how someone with no coding background can orchestrate a complex software project.

The Project: AI-Powered Email Assistant

Business Goal: Build a system that connects to Gmail, automatically summarises emails using AI, and provides a clean interface for reading and managing them.

Why This Project Works Well for Parallel Development:

• Database storage (technical foundation)

• AI summary (business logic)

• User interface (user experience)

These three components can be built simultaneously because they have clear boundaries and minimal overlap.

The Complete Workflow: From Research to Implementation

Step 1: Initial Research Prompt

Research building an email management system with Gmail integration, AI summary, and a user interface. Break this into separate components and create detailed specifications for each part that different developers could work on in parallel.  
  
Create three specification documents:  
1. Email storage and Gmail integration requirements  
2. AI summary service requirements    
3. User interface requirements  
  
For each spec, include problem definition, technical approach, implementation steps, and integration considerations.  

Step 2: Claude Creates Specification Documents
After running the research prompt, Claude generates three detailed specification files that serve as blueprints for each component.

Step 3: Parallel Implementation Using the Specs

Feature 1: Email Database Storage

What You Tell the Agent (referencing the generated spec):

I need you to implement the email storage system. Here's the detailed specification we researched:  
  
[Claude would have the full specification from the research phase]  
  
The system should:  
- Connect to Gmail and download emails  
- Store them with sender, subject, body, timestamp, and other important details  
- Organise them efficiently for quick searching  
- Handle large volumes of emails without slowing down  
  
Think ultra hard about all the steps needed to build this properly.  

Feature 2: AI Email summary Service

What You Tell the Agent:

Build the email summary service based on our research specification:  
  
[Claude references the summary spec from research phase]  
  
The database models are being worked on by another team member, so create mock data for testing.  
  
The service should:  
- Take a full email and generate a 2-3 sentence summary  
- Identify the main action items or requests  
- Categorise emails by type (meeting request, information, urgent, etc.)  
- Handle errors gracefully if the AI service is unavailable  

Feature 3: User Interface

What You Tell the Agent:

Create the user interface based on our UI specification:  
  
[Claude references the UI spec from research phase]  
  
The database and summary service are being built by other team members, so use mock data to show how it will work.  
  
The interface should:  
- Show a list of emails with their AI-generated summaries  
- Allow users to read full emails when needed  
- Provide options to archive, delete, or mark emails as important  
- Work well on both desktop and mobile devices  
- Be clean and easy to use  

What the Agent Builds:

• Email list view with summaries prominently displayed

• Responsive design that works on different screen sizes

• Mock data that demonstrates the full user experience

• Basic interactions (clicking, archiving, marking as read)

Integration and Review Phase

After all three agents complete their work:

1. Review Each Component: Check that each piece works as intended

2. Test Integration Points: Ensure the database, summary, and UI work together

3. Quality Assurance: Run automated reviews for security, performance, and usability

4. User Testing: Try the complete system with real email data

The Result: A working email management system built in a fraction of the time it would take using traditional development approaches.

Key Takeaways:
• Clear separation of concerns - Database, business logic, and UI can develop independently
• Mock data enables parallel work - Don't let missing pieces block progress
• Integration is the final step - Combine and connect completed components

🎭 The Human Element: When and How to Intervene

Agentic coding doesn't eliminate the need for human judgment—it amplifies it. Even if you're not technical, there are crucial moments where your input makes the difference between a mediocre product and a great one.

When AI Agents Need Human Guidance

Business Logic Decisions: AI agents excel at implementation but need human input on product decisions and trade-offs.

Example: "Should we prioritise email loading speed or summary accuracy?" This requires understanding your users and business goals.

User Experience Choices: Agents can build functional interfaces, but humans understand what feels intuitive and delightful.

Example: "Should the summary appear above or below the email preview?" This requires empathy for how users actually work.

Integration Complexity: When features need to work together, human oversight ensures they create a cohesive experience.

Example: "How should the UI handle emails that fail to summarise?" This requires thinking through edge cases and error scenarios.

The Review Process (Non-Technical Approach)

You don't need to understand code to provide valuable feedback. Focus on:

1. Does It Solve the Right Problem?

• Test the system with real scenarios from your work

• Check that it addresses the original business need

• Verify that the user experience makes sense

2. Is the Quality Good Enough?

• Try to break the system with unusual inputs

• Test on different devices and browsers

• Check that error messages are helpful and clear

3. Does It Match Your Vision?

• Compare the result to your original specifications

• Ensure the style and tone match your brand

• Verify that the functionality is complete

Example Review Process:

I tested the email summary feature with 20 real emails from my inbox. Here's what I found:

✅ Summaries are accurate and helpful for 18/20 emails
❌ Two emails with lots of technical jargon were summarised poorly
✅ The interface is clean and easy to navigate
❌ The mobile version is hard to use on small screens
✅ Loading speed is acceptable for my email volume

Next steps: Fix the technical jargon issue and improve mobile layout.

Maintaining Quality Without Technical Knowledge

Use Real-World Testing: The best quality assurance comes from using the system for actual work, not theoretical testing.

Get Multiple Perspectives: Have colleagues or potential users try the system and provide feedback.

Focus on User Outcomes: Instead of worrying about technical details, focus on whether the system helps users accomplish their goals.

Document What Works: When you find patterns or approaches that work well, save them for future projects.

Key Takeaways:
• Business judgment is irreplaceable - AI agents implement, humans decide on strategy and user experience
• Real-world testing beats theoretical analysis - Use the system for actual work to find issues
• Multiple perspectives improve quality - Get feedback from different types of users

🔄 Beyond Coding: The Full Product Development Workflow

Here's where agentic coding becomes truly powerful for non-technical people. The same approach that works for managing coding agents works for every aspect of product development.

Product Management Tasks

Feature Research and Competitive Analysis:

Research email management tools and analyse what features users love most. Look at Gmail, Outlook, Superhuman, and Hey. Create a competitive analysis document with:
- Key features and how they work
- User reviews and common complaints  
- Pricing and business models
- Opportunities for differentiation

Think ultra hard about what would make our email tool 10x better than existing options.

User Story Creation and Prioritisation:

Based on our user interviews and competitive research, create detailed user stories for our email management tool. For each story, include:
- User persona and context
- Specific problem being solved
- Acceptance criteria for completion
- Priority level and reasoning

Focus on the most impactful features that solve real user pain points.

Marketing and Communication

Feature Announcements:

Write a feature announcement for our new AI email summary feature. The announcement should:
- Explain the problem this solves for busy professionals
- Highlight the key benefits and time savings
- Include specific examples of how it works
- Match our company's friendly but professional tone
- Be suitable for email newsletter, blog post, and social media

Reference our previous announcements for tone and style.

Customer Support Documentation:

Create help documentation for users who are new to AI email summaries. Include:
- Step-by-step setup instructions with screenshots
- Common questions and troubleshooting tips
- Best practices for getting the most value
- Contact information for additional support

Write for users who may be skeptical or intimidated by AI features.

Operations and Maintenance

Performance Monitoring:

Create a monitoring dashboard that tracks:
- Email processing speed and accuracy
- User engagement with summarized emails
- System uptime and error rates
- User feedback and satisfaction scores

Alert me when any metrics fall below acceptable thresholds.

User Feedback Analysis:

Analyze the last 100 customer support tickets and user feedback emails. Identify:
- Most common user complaints and requests
- Patterns in user confusion or difficulty
- Opportunities for product improvements
- Success stories and positive feedback themes

Create actionable recommendations for product and support team.

Example: Automated Business Process

Kieran uses Claude Code for non-coding tasks like email management:

/triage-podcasts
# Analyses incoming podcast requests
# Scores them based on relevance and quality
# Creates calendar events for promising opportunities
# Drafts response templates

Translation for business context:

• Automatically reviews incoming partnership requests

• Evaluates them against your business criteria

• Schedules follow-up meetings for promising opportunities

• Prepares personalized response templates

Key Takeaways:
• Every business process can be "agentified" - Research, writing, analysis, and operations
• Compound your efforts - Every workflow you create makes future work faster
• Think systems, not tasks - Build repeatable processes, not one-off solutions

🎯 Getting Started: Your First Multi-Agent Project (Beginner-Friendly)

Week 1: Foundation and Comfort Building

Day 1-2: Environment Setup (No Coding Required)

• Install Claude Code following the simple instructions

• Practice basic conversations with the AI

• Try asking it to research a topic you're interested in

• Get comfortable with the interface and basic commands

Day 3-4: Single Agent Practice

• Choose a simple business task (like writing a competitive analysis)

• Practice the research → specification → implementation workflow

• Experiment with "magic phrases" ("think ultra hard", "create todos")

• Learn to give clear, detailed instructions

Day 5-7: Understanding Project Structure

• Learn about organizing work into separate projects/folders

• Practice breaking big problems into smaller tasks

• Understand how to manage dependencies between tasks

• Set up your preferred review and feedback process

Week 2: Two-Agent Coordination

Day 1-3: Simple Parallel Projects

• Choose a project with two independent components (like a blog post + social media campaign)

• Practice running two agents on related but separate tasks

• Learn to manage timing and handoffs between agents

• Develop your quality review workflow

Day 4-5: Adding Complexity

• Try a project where one task depends on another

• Practice the "assume this exists" approach for dependencies

• Refine your integration and review processes

• Start building templates for common task types

Day 6-7: Quality and Optimisation

• Implement multiple review perspectives on your work

• Create checklists for different types of projects

• Optimize your workflow for speed and quality

• Document what works well for future reference

Week 3: Advanced Multi-Agent Orchestration

Day 1-3: Three-Agent Projects

• Choose a complex project (like launching a new product feature)

• Break it into research, implementation, and marketing components

• Practice coordinating multiple agents with interdependencies

• Develop advanced quality control processes

Day 4-5: Custom Workflow Development

• Build templates for your most common project types

• Create reusable instruction sets for different kinds of work

• Develop domain-specific review criteria

• Set up integration with your existing business tools

Day 6-7: Scale and Systematise

• Document your complete workflow for team training

• Create templates for onboarding others to your approach

• Plan how to apply this to larger business processes

• Identify your next level of automation opportunities

Success Metrics for Non-Technical Users

Productivity Metrics:

• Time from idea to working prototype/deliverable

• Number of projects you can manage simultaneously

• Quality scores from stakeholder reviews

• Reduction in manual, repetitive work

Learning Metrics:

• Comfort level with giving AI agents complex instructions

• Ability to break down business problems into manageable tasks

• Skill at coordinating multiple workstreams

• Growth in reusable template and workflow library

Business Impact Metrics:

• Faster delivery of marketing campaigns, product features, or business initiatives

• Increased experimentation and iteration speed

• Better outcomes through more thorough research and planning

• Reduced costs per project or initiative

Recommended First Projects by Role

Product Managers:

• Competitive analysis + feature specification + user story creation

• User research analysis + product roadmap + stakeholder communication

Marketing Professionals:

• Content strategy + content creation + distribution planning

• Campaign research + creative development + performance tracking

Business Operations:

• Process documentation + workflow optimization + training material creation

• Data analysis + reporting + recommendation development

Entrepreneurs:

• Market research + business plan + pitch deck creation

• Product development + marketing strategy + operational planning

Key Takeaways:
• Start with familiar business tasks - Apply agentic approaches to work you already understand
• Focus on coordination skills - The technical details matter less than project management
• Build complexity gradually - Master simple coordination before attempting complex orchestration

🚀 The Future of Work: What This Means for Non-Technical Professionals

Kieran's approach represents a fundamental shift in how work gets done. We're moving from individual contributors doing tasks to managers orchestrating AI teams. This has profound implications for every professional, not just programmers.

For Individual Contributors

• Skill evolution: From task execution to specification writing and quality management

• Productivity multiplication: 10x output through parallel AI agent orchestration

• Focus shift: More time on strategy and creative problem-solving, less on routine execution

For Teams and Organizations

• Role redefinition: Senior professionals become AI team orchestrators

• Quality processes: New review workflows that combine AI capability with human judgment

• Collaboration patterns: Async work with AI agents, sync work with human colleagues

For Business Strategy

• Faster iteration cycles: Ideas to working prototypes in hours, not weeks

• Lower experimentation costs: Try more approaches with less resource commitment

• Better market responsiveness: Rapid testing and iteration based on customer feedback

The Compound Effect for Business

"Make sure everything you do is like compounding because it's better," Kieran advises. Every workflow you create, every template you build, every process you establish makes future work faster and better.

This isn't just about working faster—it's about thinking at a higher level of abstraction where you orchestrate systems instead of executing individual tasks.

Real-World Business Applications

Marketing Teams: Instead of one person writing a blog post, have three agents simultaneously create the content, design the graphics, and plan the distribution strategy.

Product Teams: Instead of sequential research → specification → design phases, run all three in parallel with different agents, then integrate the results.

Operations Teams: Instead of manually analysing data and creating reports, have agents continuously monitor metrics, identify trends, and draft executive summaries.

Sales Teams: Instead of individually researching prospects, have agents simultaneously research companies, identify decision-makers, and draft personalised outreach sequences.

The Competitive Advantage

The professionals who master this approach first will have an enormous competitive advantage. They'll:

• Ship projects faster than competitors

• Iterate more quickly based on market feedback

• Solve bigger, more complex problems

• Free up time for high-value strategic thinking

The question isn't whether this future is coming—it's whether you'll be ready for it.

Getting Started: Advice for the Intimidated

Kieran's advice for non-engineers feeling intimidated by agentic coding: "Just do it. Don't feel intimidated... Just do one simple thing and see if it can do that and then build on top of that."

Start with what you know:

• If you're in marketing, begin with content creation and campaign planning

• If you're in product, start with research and competitive analysis

• If you're in operations, begin with process documentation and data analysis

• If you're an entrepreneur, start with market research and business planning

Remember: You don't need to become a programmer. You need to become a better manager and communicator. The AI agents handle the technical execution—you provide the vision, context, and quality control.

Ready to start your multi-agent journey? Begin with Claude Code, practice the workflows Kieran demonstrated, and remember: the future belongs to those who can effectively orchestrate AI teams, not just use AI tools.

Connect with Kieran:

• X: @kieranklaassen

• Website: cora.computer

• Watch the full tutorial: YouTube

• Get the takeaways: creatoreconomy.so

 🎥Watch the full episode here