Why AGENTIFY?
Agents 1.0 is over. If you are not preparing for Next-Generation Agents, you will be left behind.
Smart founders, investors, and builders are already pivoting. The AI agents that will be available in 2027 and 2028 will greatly outstrip the adaptability and capability of what is currently in your feed calling itself an “AI Agent.” The vast majority who have “built an agent” already, have in fact built a very simple, often single LLM, single loop, semi-agentic workflow. These 2025 “agents” can “complete tasks,” but they have no initiative, and they do not think for themselves. In short, they have no agency.
What is coming shortly, in one to two years, will make the early 1.0 agents we see today feel like they hardly deserve the name “AI agent” at all.
This spells real opportunity for those who have yet to build anything, or are holding a job in an industry that is gradually adopting AI for the “obvious low-hanging” use cases. It also spells a certain amount of career and job risk for those who sit still, and assume their positions are secure even if they do nothing.
The good news? You can still catch up. You can start today, and be part of what is barely on the horizon as we close out 2025. But to do so requires more than watching a YouTube video, or instantiating “class agent” in your favorite Python framework, or building a “drag and drop” agent workflow in any of the myriad “no code automation builders.” Those are already commodities.
To tap into the huge opportunity that sits before builders and investors today, you will have to go deeper.
I want to explain what motivated the writing of my just-released book AGENTIFY, which aims to lay out next-generation agents from a product, research, and engineering perspective, and give you context to decide if this book is right for you.
When I set out to write, I was already talking to many friends and former colleagues who were facing challenges with the shifting job market. They were worried, and I hoped if I could share what I had learned from my own AI engineering and research journey, it might help them.
That was 10 months ago. Today, people are even more worried. The job market is tougher than ever.
Additionally, in my work I advise large enterprises and startups on AI strategy, on engineering, and do research into specific problems that can help these companies grow. Just as I saw individuals struggling with the pace of change, I see the same in companies large and small.
Too many builders are either building for something that OpenAI or Anthropic or Google will launch next week as a feature, or they are stuck waiting to “follow a leader”, unable to disrupt their own businesses for lack of leadership and creativity, and due to a fear of making mistakes. They are not looking at what comes next. Therefore they are largely not building an approach that is defensible. AGENTIFY can help leaders and builders address this gap with a research-backed, detailed guide to what next-generation agents will be capable of and, even more importantly, how to build them.
I was pretty naive about how long and hard writing an in-depth non-fiction book would be. But if anything, despite blowing way past all the deadlines I originally set for myself, the motivation has gotten stronger and stronger. I’ve had many friends also come to me about their college-age or recently graduated kids, asking if I can help with job searches, or make referrals. I’ve been happy to, and have been telling all of them about the changes I see coming, and what I think AI agents and robotics will mean over the next several years, and that now is the time to get prepared.
I get it. Busy people in busy jobs are struggling to keep pace. Many people I speak with know they need to “catch up on AI", or “go deeper on AI”, but they are not sure exactly how? Even if you are not building agents, but perhaps thinking about a future job interview or career move, I do sincerely hope and believe that AGENTIFY can help. A ~380 page book is very different than a YouTube podcast, or a blog post, or a chat or summary with your favorite LLM. A book is a chance to see many diverse strands and facts synthesized together, follow a coherent narrative, and form a deeper perspective, more grounded and not based on hype or hyperbole. In AGENTIFY I did my best to keep the theory, engineering, and product principles accessible, and keep the narrative moving along with stories and occasional dashes of humor.
I hope that the months of work put into AGENTIFY can save you many, many hours of search, and can help you feel more ready to face the road ahead. I welcome all feedback.
The simplest way I could think of to help you see what is in the book was to give you the full Table of Contents, followed by the full Preface, exactly as they appear in the book. I will be releasing details from chapters in future posts, but if you want to dig in now, please just visit the launch page:
AGENTIFY Table of Contents
PART I: From Tools To Agents
1. Software as Colleague & Teammate
2. The Evolution of Software Products
3. Where We Are Today
4. What Is True Agency?
PART II: The New Competitive Advantage
5. Adapt, Specialize, Survive, Flourish
6. Agents Can Be Everywhere at Once
7. Feedback: The New Beautiful UI
8. New Challenges for Designers
9. The Insight Factory: Simulate Your Domain
10. Dream, Distill, Differentiate: Your Agent’s Data Moat
PART III: On The Verge of Autonomy
11. A Skeptic’s Point of View
12. The Role of Reinforcement Learning
13. Tool Use Explosion
14. Simulating Agency & Hierarchical Goals
15. Higher Order Cognition: Creativity, Taste, Humor & Judgment
16. Coding Agents: A Bellwether
17. The Wild West of Social & Crypto Agents
PART IV: Architecture & Development
18. The Infinite Agentic Loop
19. Agent Situational Awareness: Harness Hallucinations
20. Tools, Tuning, Reasoning & Protocols
21. Context Engineering, Memory & Knowledge
22. Beyond Vibes: Rigorous AI Software Development
23. Open Source Agentic Frameworks
24. The Future of Scrum, Kanban, Agile, etc.
PART V: Risk, Robustness, & Security
25. Transparency from the Ground Up
26. Simulation for Security & Robustness
27. Controlling Agent Autonomy
28. Agent Identity, Agent Liability
PART VI: The Path Ahead
29. AGI Research Frontiers
30. Perilous Predictions
31. Product Manager as Behavioral Psychologist
32. Finding Your Personal Path
Next up, here is the full preface of the book! I hope this gives you a good feel for what to expect.
AGENTIFY Preface
This book is for builders of AI agents. Allow me to define builder broadly to include investors, founders, product managers, architects, engineers, and others working in the rapidly evolving space of agentic AI product development. Given that broad definition, my goal is to illuminate the strategic, product, research, and engineering factors essential to making AI agents successful and that will have enduring value as the industry continues to advance.
In my work with teams developing AI products, I’ve found the following three questions cause builders to struggle most:
How are AI agents fundamentally changing the paradigm of software products?
What will make agents successful, competitive products?
What research, engineering, and safety approaches are critical to building effective autonomous agents?
The largest ever paradigm shift in software is well underway and will make these questions central for decades to come. The traditional playbooks and best practices for product management, product design, and engineering are unsuited for what is coming with AI agents. This book seeks to answer these questions in precise yet durable ways that remain relevant even as models, frameworks, and practices evolve.
One deeply satisfying result has been confirming that the answers to these questions are rooted in foundational ideas, in some cases decades old, from cognitive science, complex adaptive systems, control theory, robotics, and neuroscience. My task has been to translate these into principles, patterns, and architectures that are of lasting value for builders.
I’ve tried to provide a balance of my observations from work with teams at startups and large companies, as well as with others in academia, government and venture capital. General conclusions are contrasted with case studies, and I elaborate hypothetical “stories of the future” to give more color and to stimulate your imagination about what may soon be possible. For teams that aspire to create successful AI agents, imagining the future with depth, creativity, and realism will be pivotal.
Part I of the book confronts the first question. How are agents fundamentally different from traditional software products? We examine exactly what we mean by the term agency and discover that the end game is increasing autonomy and human-like behavior for AI agents. Much of traditional software, designed for humans to manipulate, will rapidly become obsolete.
In Part II, we shift to the crucial question: what will constitute competitive advantage for agents? What types of agents should we build? How can builders leverage the unique properties of agents to differentiate and solve real-world problems? We confront these questions, including the design and packaging issues unique to agents. We consider how to simulate the environments in which agents will operate, to more rapidly find ways to make our agents competitive.
Part III focuses on the remaining gaps in performance that are holding agents back from more autonomous roles. Still at the product level, we examine the industry-wide efforts in reinforcement learning, tool use, planning, goal management, and agent judgment that are closing those gaps. Deeper case studies of coding agents, and social and crypto agents provide leading indicators of businesses based on greater agent autonomy.
Robust engineering architectures and methods are vital too. Part IV covers them in depth, laying out the fundamental architectural patterns that support greater agent performance and autonomy. We confront the practical side of tools, context engineering, memory, multi-model and multi-loop architectures, and other essential building blocks for highly specialized, highly performant agents. Part V concentrates on the safety and risk issues agent engineering and product teams must also confront.
Throughout the book, the critical role of AI research is a cross-cutting theme. The book includes multiple research callout sections that curate key papers and insights on topics central to a given chapter. Deeper insights from research power the most successful agents. Whether we take work from Pentti Kanerva on sparse distributed memory, Kenneth Stanley on open-endedness, Melanie Mitchell on analogy making, or Minsky on agencies and emotions, research insights continue to be indispensable. In Part VI, one substantial chapter, “AGI Research Frontiers,” deserves special mention. There I seek to provide a detailed survey of the broad areas of research that may help us reach fully human-level AGI. Complete references are provided in the back of the book.
Even if you are not a builder, this book should still provide value. If you are drawn to hear a detailed account of how AI agents work, how they may be developed, and the types of roles they could soon be playing in our economy and everyday lives, then this book should be a meaningful starting point for you too.
A note on “complex adaptive systems” is also warranted. You may have noticed this term appears on the back cover of the book. Why is it there? And what role does it play in the book? The overall focus of the book is on the product, engineering, and research dimensions of AI agents in a manner geared toward
a broad audience of agent builders. As a result, the importance of complex systems may be hard to make out. The ideas appear when we lay out the architecture for AI agents and we discuss a “multi-loop, multi-model architecture,” and reference Minsky’s Society of Mind. These approaches are heavily influenced by complexity science. I address it even more directly in the AGI chapter (Chapter 29: AGI Research Frontiers), where we look at how complex adaptive systems may be central to achieving human-level general intelligence.
For specialists in complexity science and emergence, the treatment here is, of course, superficial. My apologies to those specialists, but also a thank-you for the real inspiration they have been to this book. My goal is to share with agent builders some important ideas from complexity science most relevant to their goals. Complexity theory (and the engineering innovations it can help shape) may be the unlock for AGI. A fuller explanation of this claim will have to wait for another book.
My readers and I owe a debt of gratitude to my friend John Botti, who helped me meticulously pore over late-stage versions of the manuscript and made innumerable thoughtful improvements. The reading experience is far better thanks to his generous help.
Special thanks to my brother Damon Palmer, who has been my most ardent fan. He was a vital sounding board as this book progressed from idea, through scattered notes and conversations, to manuscript and, after great labor, to edited form. His deep, patient readings and insights have been invaluable and shaped much of how I tell the story in the pages ahead.
M.P.
Tampa, Florida
December 2025
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Just got my copy - can’t wait to read it!