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This document serves as a guide for developing your agentic product. It is designed to help you systematically define your product's goals, requirements, and implementation strategy. By working through each section, you will refine your product idea, identify potential challenges, and develop a comprehensive plan for building and deploying your solution.

Agents Playbook v2506

This document serves as a guide for developing your agentic product. It is designed to help you systematically define your product's goals, requirements, and implementation strategy. By working through each section, you will refine your product idea, identify potential challenges, and develop a comprehensive plan for building and deploying your solution.

Agents Playbook v2504

Are you interested in building LLM Agent Products, but unsure where to begin, or if you have the necessary background knowledge? We have curated this learning path using 70+ hours of (mostly) free resources that you can use to:<ol><li>Get started on your journey of building LLM Agent Products</li><li>Prepare to get the most of this course or similar ones, and</li><li>Evaluate areas where there's a gap in your knowledge</li></ol> After entering your email, you will be directed to a Miro Board. Contact maven@ai.science if you have any issues with this resource.

LLM AGENTS - Learning Path

Design, develop, deploy, and demo an LLM agent application in 7 weeks!

Founder at Aggregate Intellect

Amir Feizpour, PhD

10+ years in AI and Product Development

Build Multi-Agent Applications - A Bootcamp - LangGraph, Cursor, n8n

Photonics Researcher

Product Manager

Software Developer

Game Developer

Director, Risk Models & Data Science

Manager, Data Scientist

Dev Lead

Researcher

founder of my company

You can book me to talk about this bootcamp or anything else AI / career / startup related:https://calendly.com/amirfzpr/20min-ama 🟢 WHAT WILL I BE ABLE TO BUILD IN THIS BOOTCAMP?You’ll build a functional application powered by LLM agents to automate a workflow of your choice. Along the way, you'll learn to design agent workflows, and integrate required tools. By the end of the program, you'll have a working application ready to showcase in a public demo.You can see this playlist for examples of what previous students built: https://tinyurl.com/llm-agent-demos🟢 HOW WILL YOU HELP ME BUILD APPS LIKE THESE IN JUST 7 WEEKS?This program is all about learning by doing. Specifically:👨🏽‍🏫 1:1 sessions with the us to help you think through bigger picture questions👨‍🎓 Async and sync sessions with a dedicated mentor for constant support, code reviews, and real-time feedback 🟢 I SEE! SO, WHAT SHOULD I EXPECT TO HAPPEN DURING?Think about it like this: This is a 3-sprint product dev + 1 week of prep for the public demo.🖌️ SPRINT 1 - DESIGN (weeks 1 &amp; 2)🔨 SPRINT 2 - DEVELOP (weeks 3 &amp; 4)🚢 SPRINT 3 - DEPLOY (weeks 5 &amp; 6)🎭 PUBLIC DEMO (week 7)

Mentorship Program to Help You Build an Agent-based MVP

How to identify opportunities for building AI agents

Controlling the behavior of your agents often requires coding to include various tools, memory, or orchestration.

Python Coding

If you don't want to code, you can use no-code agent studios. We will provide specific resources but general familiarity is useful.

No-code App Building

If you are planning to code (or vibe code), familiarity with one of the coding assistants (GitHub copilot, cursor, etc), is helpful.

Coding co-pilots

You want to build an agentic MVP to test the feasibility of your startup idea.

You want to build an agentic MVP to explore the new feature you want to add to your existing product

You want to build an agentic MVP to demonstrate your skills for career progression purposes.

Designing ethical AI systems: A hands-on workshop

Amir Feizpour

Founder @ Aggregate Intellect

Lena Shakurova

Founder @ ParsLabs

Learn a structured, repeatable process for mapping ethical risks to system design decisions.

Applied Ethical Frameworks

Identify potential harms in data use, inclusivity, and power imbalance before they reach production.

Spotting Hidden Risks Early

Translate ethical insights into technical and architectural improvements for your current or future projects.

Design Changes for Trustworthy AI

Building AI Systems with DSPy - Beyond 101

Ian Yu

MLE @ Rezolve AI

How DSPy connects prompts, programs, evaluation, and optimization into a unified workflow.

DSPy as a System-Building Tool

Practical patterns for composing DSPy modules into agentic applications.

From Components to Agents

Using metrics and feedback loops to guide system behavior and improvements.

Evaluation-Driven Development

How DSPy’s optimization capabilities reduce manual prompt tuning and iteration cost.

Auto-Optimization in Practice

Build an Agentic App (Part 2/2): LangGraph + Cursor + n8n

Build the backend in n8n and frontend in Cursor

Use AI co-pilots (eg. Cursor) to add code blocks to your app

Test your app to improve its performance using Claude

Build an Agentic App (Part 1/2): Design Using Claude Code

Assess if agentic approach is appropriate for your use case.

Design agentic workflow for your use case.

Implement your agentic workflow using Claude

Agents in the Wild – A Supply Chain Use Case

Waleed Ayoub

Product & Technology Leader @ Daybreak.ai

How time-series forecasting and optimization models anchor agentic decision-making in reality.

Integrating Agents with Classic ML

Lessons from deploying autonomous agents in live supply chain workflow; fail-safes and escalation logic

Operational Reliability in the Wild

Frameworks for determining when agents should act independently versus defer to human operators.

Balancing Autonomy and Oversight

Agents and ROI

Frank Wang

Director @ Ithron Research

Learn how to evaluate whether an agent use case has a clear, measurable business outcome.

From Idea to Impact

Align agent tasks and metrics with existing KPIs to make results visible and defensible.

Designing for Measurable Value

Build a practical decision matrix to compare multiple agent ideas and identify which to pursue first.

Prioritizing Opportunities

Building AI Agents: Bootcamp 2510 Public Demo

What agentic systems can be built in 7 weeks

Skills necessary to build agentic systems

Tool stack involved in building agentic systems

AI Agents As Innovation Catalyst

Jacco Pleijter

Founder/CEO @ Intelligentsia

How agentic systems enable faster iteration, idea generation, and knowledge synthesis.

Rethinking Innovation Workflows

Shifting the role of AI from “assistant” to “co-creator” in innovation processes.

From Automation to Collaboration

Practical frameworks for testing, validating, and scaling AI-assisted innovation ideas.

Designing Agent-Driven Experiments

How to Spot/Validate High-Potential Agentic AI Opportunities

Jason Dea

Head of Product @ Koru

How to ensure your AI idea solves a genuine problem, not just a shiny use case.

Grounded Problem Validation

Why generative AI removes old barriers (e.g., data normalization, rigid APIs) and how that expands viable product ideas.

Recognizing Qualitative Shifts

How to decide if an “agent” really adds value - or if a simpler automation would suffice.

Evaluating Agentic Potential

Personalized Presentations with AI: An Experiment

Discover how AI can adapt presentations to each audience member’s background and interests.

From Generic to Personal Learning

Learn how LLMs can organize, tag, and reassemble content in real time for personalized delivery.

Dynamic Content Management

Understand the balance between automation and presenter control when customizing experiences.

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