If you like the book version, check here to download: AI Project Management.
Chapter 1: The Day I Became the PM
Chapter 2: Discovering OpenClaw
Chapter 3: Bootstrapping Your First AI Agent
Chapter 4: From Idea to Working Prototype
Chapter 5: When AI PM Got It Wrong
Chapter 6: Dissecting PM Workflows with AI
Chapter 7: Building the Playbook
Chapter 8: Measuring What Matters — Evaluation by Design
Chapter 9: Evaluation as a Daily Practice
Chapter 10: Listening, Learning, Iterating
Chapter 11: Building an AI PM Roadmap
Chapter 12: Getting the Team on Board
Chapter 13: Going Live
Chapter 14: Stories from the Trenches
Chapter 1: The Day I Became the PM Engineering teams without a dedicated project manager face a familiar pattern: task tracking fragments, sprint planning becomes ad hoc, and someone — usually a tech lead — absorbs the PM workload by default. This chapter examines what happens when an engineer inherits project management responsibilities overnight, the specific failure modes that emerge in teams running without PM discipline, and the inflection point where building an AI-powered solution becomes more practical than waiting for a hire. It establishes the problem space and introduces the core thesis: modern AI agent frameworks make it possible to automate the operational backbone of project management — whether you're an engineer filling a gap or a PM looking to multiply your impact.
Chapter 2: Discovering OpenClaw A survey of the AI agent landscape as it applies to project management automation — from chatbot builders and no-code workflow tools to full agent frameworks. This chapter introduces OpenClaw: its architecture, core capabilities, and the design decisions that make it well-suited for PM workflows. Readers will explore the underlying components — LLMs, tool integrations, and agent memory — and learn how OpenClaw maps onto existing team infrastructure, particularly JIRA for task management and Slack for communication. The chapter closes with prerequisites and key decisions to make before starting a build.
Chapter 3: Bootstrapping Your First AI Agent A hands-on walkthrough of setting up the development environment from scratch. Installing and configuring OpenClaw, establishing JIRA API connectivity, and deploying a Slack bot as the agent's communication layer. The chapter covers common setup pitfalls, authentication patterns, and environment configuration. By the end, readers will have a working agent capable of reading a JIRA board and responding to basic queries in Slack — the foundation for everything that follows.
Chapter 4: From Idea to Working Prototype Rapid prototyping in practice: building a minimal AI PM agent that delivers immediate value. Starting with sprint status summarization, then incrementally adding standup collection, blocker detection, and task suggestions. This chapter emphasizes the discipline of shipping the smallest useful thing first, gathering real usage data, and iterating based on what the team actually adopts versus what seemed like a good idea. Practical guidance on scoping prototypes, managing expectations, and recognizing when a prototype is ready to evolve.
Chapter 5: When AI PM Got It Wrong Understanding how AI agents operate — and fail — in PM contexts. This chapter examines the mechanics of prompt processing, context windows, and tool calling, then maps these to real failure scenarios: hallucinated tasks, incorrect status assessments, and misrouted notifications. Readers will learn systematic approaches to debugging agent behavior through log analysis, decision tracing, and output validation. The goal is to build a working mental model of agent limitations so failures become diagnosable rather than mysterious.
Chapter 6: Dissecting PM Workflows with AI A structured teardown of AI PM workflows after several sprints of production use. This chapter presents case studies in automated sprint planning and async standup summarization, analyzing where the agent added genuine value versus where it introduced blind spots. Readers will develop a framework for evaluating AI suitability across PM tasks — a decision model for determining what to automate, what to augment, and what to leave entirely to humans.
Chapter 7: Building the Playbook Proven patterns for AI-assisted project management: task routing, workload balancing, priority negotiation, and dependency tracking. Equally important, this chapter documents the anti-patterns — fully autonomous assignment, unsupervised stakeholder communication, AI-only estimation — and explains why they fail. Readers will learn to compose individual patterns into cohesive workflows covering daily check-ins, sprint ceremonies, and cross-team coordination. Special attention is given to the human-in-the-loop pattern and the criteria for when it applies.
Chapter 8: Measuring What Matters — Evaluation by Design Moving beyond subjective assessment to structured evaluation. This chapter introduces an evaluation framework for AI PM agents, covering the metrics that matter: task accuracy, response relevance, blocker detection rate, and team trust. Readers will learn to define quality criteria specific to their workflows, establish baselines, and design evaluation rubrics that capture both the agent's technical performance and its impact on team effectiveness.
Chapter 9: Evaluation as a Daily Practice Implementing continuous evaluation as an operational practice, not a one-time exercise. This chapter covers automated evaluation pipelines, testing against historical sprint data, and A/B comparison of agent recommendations against human decisions. Readers will set up monitoring dashboards, configure regression alerts, and establish review cadences — turning evaluation from a project milestone into a daily habit embedded in the team's workflow.
Chapter 10: Listening, Learning, Iterating What happens after deployment: capturing production feedback, tracking accuracy over time, and identifying drift in agent performance. This chapter addresses the full feedback loop — collecting team input, analyzing patterns in agent successes and failures, and systematically feeding learnings back into prompts, tool configurations, and behavioral rules. The focus is on the compounding returns of iterative refinement: each cycle producing measurable improvements in agent reliability and team confidence.
Chapter 11: Building an AI PM Roadmap Transitioning an AI PM from a tactical tool to a strategic organizational capability. This chapter covers timing — when to introduce an AI PM and when the conditions aren't right. It addresses positioning (augmentation, not replacement), building executive buy-in, and navigating organizational concerns about automation in people-facing roles. Readers will learn to build a roadmap that evolves the AI PM from a single-team experiment to a scalable platform.
Chapter 12: Getting the Team on Board The human side of AI PM adoption. Engineers, designers, QA leads, and stakeholders each bring different expectations and concerns. This chapter provides practical approaches to cross-functional rollout: managing skepticism, demonstrating value through AI-generated reports and insights, and addressing trust gaps head-on. It covers the cultural shift required to normalize AI-assisted project management — moving from novelty to standard operating procedure.
Chapter 13: Going Live Production-hardening an AI PM agent for daily organizational reliance. This chapter covers security and permissions modeling, data handling boundaries, and reliability engineering — retries, fallbacks, and graceful degradation. It addresses scaling from a single team to multiple teams, establishing monitoring and alerting infrastructure, and defining operational runbooks. The result is an AI PM that teams can depend on with the same confidence as any other production system.
Chapter 14: Stories from the Trenches A candid retrospective on the full journey — what delivered the most value, what wasn't worth the effort, and what produced unexpected results. This chapter examines the capabilities where AI PM agents consistently outperform traditional approaches, the areas where they remain fundamentally limited, and the open questions the field hasn't yet answered. It includes perspectives from different roles — engineers who built their own AI PMs, project managers who used one to scale across multiple teams, and leaders who navigated organizational adoption. It closes with actionable guidance for readers beginning their own implementation: the decisions that matter most, the mistakes that are easy to avoid, and the mindset that makes the difference.
Total: 14 Chapters