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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

Product managers who drive strategy, development, and launch of products.

Engineers and engineering leaders who build, deploy, and scale software systems.

Startup founders building new businesses, from MVP to scale.

Applying AI tools and techniques to professional workflows — assign alongside more specific AI topics.

Building autonomous AI agent systems that take actions, use tools, and orchestrate multi-step workflows.

Using Claude Code to build and ship software through an AI-powered command-line coding workflow.

This isn't just another course. You'll walk away with tangible assets that advance your career and concrete skills that set you apart.

Founder at Aggregate Intellect

Amir Feizpour, PhD

10+ years in AI and Product Development

Agentic Buildcamp - A Cognitive Gym for Building AI Agents — using AI Agents

Sr Data Engineer

Photonics Researcher

Product Manager

Software Developer

Game Developer

Director, Risk Models & Data Science

Manager, Data Scientist

Dev Lead

Researcher

You're already trying to build something. Agentic Buildcamp is where pragmatic builders - developers, founders, and operators - ship a real AI agent using the KnowledgeOps methodology: 12+ guided workflows that take you from problem framing to production, delivered through Sherpa-B, an MCP server that connects to your CLI coding agent (eg. Claude Code). You learn by building, using the same agentic process you're learning to build with.Note 1: Join my next <a target="_blank" rel="noopener noreferrer nofollow" href="https://calendly.com/amirfzpr/agents-office-hour">Agents Office Hour</a> and ask about this program.Note 2: If you are interested in a mentorship version of this program, reach out to <a target="_blank" rel="noopener noreferrer nofollow" href="https://mailto:bootcamp@ai.science">bootcamp@ai.science</a>🟢 WHAT WILL I BE ABLE TO BUILD?You’ll learn to design, build, evaluate, and deploy agentic systems, and integrate required tools. By the end of the program, you'll have a working application ready to showcase in a public demo.▶️ <a target="_blank" rel="noopener noreferrer nofollow" href="https://tinyurl.com/llm-agent-demos">Playlist of previous public demos</a> 🟢 WHAT SHOULD I EXPECT TO HAPPEN DURING?This is a 3-sprint product development + 1 week of prep for the public demo.🖌️ SPRINT 1 - DESIGN (wk 1 &amp; 2)🔨 SPRINT 2 - DEVELOP (wk 3 &amp; 4)🚢 SPRINT 3 - DEPLOY (wk 5 &amp; 6)🎭 PUBLIC DEMO (wk 7)Sprint demos will be live and interactive.

Self-directed program where builders ship real AI agents in 7 wks in Claude Code

Ship a Real Agentic AI Application

Experience Learning via Building with KnowledgeOps Methodology

Obtain Assets and Artifacts that Will Super-charge your Building

Most participants fail because they're not there to build. Outside of that we don't expect you to bring much prior experience 

Lots of enthusiasm to build

You will use a CLI tool (eg. Claude Code) as the primary way to learn and build (we provide resources for this)

Claude Code

Since we use coding agents heavily, you need good version control set up (we provide resources for this)

Biz or Tech Entrepreneurs - You want to build an agentic MVP to test the feasibility of your startup idea.

Hands-on Product or Operations Managers - You want to build an agentic MVP to explore a new feature in your existing product / process

Developers, Data Engineers, Data Scientists - You want to build an agentic MVP to demonstrate your skills for career progression purposes.

Amir Feizpour

Building AI Agents: Bootcamp 2608 Public Demo

Build Agentic Apps Using Claude Code & Bootcamp Info Session

Building AI Agents: Bootcamp 2606 Public Demo

Mohsin Iqbal

Harness Engineering: How Claude Code/ Codex/ Gemini CLI work

Hugo Mailhot

Building a Shared Human–AI Memory System

Building AI Agents: Bootcamp 2605 Public Demo

Founder @ Aggregate Intellect

What agentic systems can be built in 7 weeks

Skills necessary to build agentic systems

Tool stack involved in building agentic systems

How our 7-week program helps you build an agentic system

How you can go from a vague idea to a working MVP

Assess if agentic approach is appropriate for your use case.

Design agentic workflow for your use case.

Implement your agentic workflow using Claude

Leave with some workflow files you can immediately use

Learn more about our 7-week program to help you build

The Arc of Personal Automation: From Claw Agents to Durable

Sahand Sojoodi

VP of Engineering at 8090

How agents can turn vague natural-language goals into useful first versions of automations.

Start with Intent

Why repeated agent behavior should eventually become explicit, observable infrastructure.

Recognize When Workflows Stabilize

How to reserve AI usage for interpretation while making routine steps deterministic.

Separate Intelligence from Execution

Practical patterns for scheduling, progress tracking, observability, and minimizing token use.

Design for Reliability and Cost

How to move from flexible agents to durable, repeatable systems without losing usefulness.

A Reference Pattern for Personal Automation

Automating professional workflows with AI tools — productivity, operations, and task automation.

Sr. MLE @ Alethia AI

The difference between agent tools (e.g., Claude Code) and the model that powers them.

What a “Harness” Really Means

Inputs, execution loops, tool use, logging, and iteration cycles.

Core Harness Components

A minimal, notebook-based approach to structuring AI workflows.

Building Your Own Harness

CoreNLP Lead @ Coveo

Why most AI workflows break down over time and how memory changes the interaction model.

From Stateless to Stateful AI

How to structure notes so they are both human-readable and usable as machine context.

Designing a Memory Backbone

Turning personal notes into reusable context for research, projects, and collaboration.

Dual-Use Knowledge Systems

Applying shared memory to research synthesis and mapping complex professional ecosystems.

Practical Use Cases

Strategies for keeping memory systems accurate, relevant, and useful over time.

Maintaining and Evolving Memory

Broad professional audience — content relevant across many roles and functions.

Data scientists who apply statistical and ML techniques to extract insights from data.

Designing and optimizing the context window for LLM applications — prompt structure, memory, and information retrieval.

Building retrieval-augmented generation and search systems — vector databases, embeddings, ranking, and retrieval pipelines.

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