The best agentic AI books in 2026 depend entirely on your starting point. For complete beginners with no AI background, start with “Artificial Intelligence Basics” by Tom Taulli or “Human Compatible” by Stuart Russell. For professionals building agentic AI systems, “Building Agentic AI Systems” by Sinan Ozdemir is the most comprehensive single volume on agent architecture currently available. For executives and non-technical leaders, “Agentic Artificial Intelligence” by Pascal Bornet and 10 co-authors (Forbes Top 10 Tech Book 2025) is the only book on this list co-authored by 11 enterprise practitioners and is the strongest choice for senior decision-makers.
Key Highlights of Agentic AI Books “Agentic Artificial Intelligence” by Pascal Bornet is rated Goodreads 3.92 with 356 ratings as of 2026 and is the most recommended title for non-technical executives and leaders, according to Aiifi’s 2026 “6 Best AI Agents Books for Non-Technical Leaders” analysis “Building Agentic AI Systems” by Sinan Ozdemir (Pearson/Addison-Wesley, 2025) is described as “the most comprehensive single-volume treatment of agentic AI architecture currently available” by Khired.com’s 2026 roundup of 15 agentic AI books Reinforcement learning books (Sutton and Barto) underpin the core decision-making logic behind advanced agentic systems and remain essential reading for anyone building agents beyond simple LLM wrappers “Measure What Matters” by John Doerr, while not technically an AI book, is the most-cited reference for understanding goal-directed autonomous systems in a business context, because OKR logic maps directly to how well-designed agentic systems set and pursue objectives upGrad’s 2026 list identifies “AI: A Guide for Thinking Humans” by Melanie Mitchell as the clearest conceptual foundation for understanding agent intelligence without requiring a technical background Most 2025-2026 books on agentic AI are engineer-focused framework manuals (LangChain, LangGraph, multi-agent construction), so the non-technical titles on this list represent a more carefully curated category, per Aiifi’s 2026 editorial review Agentic AI books are harder to recommend well than most AI reading because the category splits sharply. Half the books labeled “agentic AI” in 2026 are LangGraph and LangChain implementation guides aimed at software engineers. The other half are executive-level strategy books that gesture at AI agents without teaching how they work. Both types have value, but they serve completely different readers.
This list organizes 15 books into three levels: foundational titles for readers with no AI background who want to understand what agentic AI is and why it matters, intermediate titles for working professionals who want to understand how agentic systems are designed and deployed, and advanced titles for engineers and system architects who want to build production-grade agentic systems.
If you are not sure where to start, the three-question test at the end of this introduction will tell you which level is right for you. And if you want to complement your reading with structured hands-on training, NextAgile’s Agentic AI Workshop is specifically designed for the transition from “I understand agentic AI conceptually” to “my team is building and deploying agentic systems.”
Three-question starting point test:
Can you explain what a vector database is and why it matters? No: start at Level 1. Yes: move to question 2. Have you built anything with an LLM API (OpenAI, Anthropic, Google)? No: start at Level 1 or Level 2. Yes: go to question 3. Do you understand how LangChain or LangGraph routes between agents? No: Level 2. Yes: Level 3. Quick Reference: All 15 Books at a Glance # Title Author Level Best For 1 Artificial Intelligence Basics Tom Taulli Beginner Non-technical professionals starting from zero 2 Human Compatible Stuart Russell Beginner Anyone who wants the philosophical and safety foundation 3 AI: A Guide for Thinking Humans Melanie Mitchell Beginner Readers who want conceptual depth without technical jargon 4 Agentic Artificial Intelligence Pascal Bornet et al. Beginner-Intermediate Non-technical executives and business leaders 5 AI First Brotman and Sack Beginner-Intermediate Senior executives and board members evaluating AI agent adoption 6 The Alignment Problem Brian Christian Intermediate Anyone who needs to understand AI safety and value alignment for governance 7 AI for Business Leaders Various / Edited Intermediate Managers who want functional AI strategy frameworks 8 Designing Autonomous AI Agents Sinan Ozdemir Intermediate Product managers, architects, and technical leads 9 Multi-Agent Systems: An Introduction Gerhard Weiss Intermediate-Advanced Engineers and system architects studying agent coordination 10 Building Agentic AI Systems Sinan Ozdemir Advanced Engineers building production agentic systems 11 Reinforcement Learning: An Introduction Sutton and Barto Advanced Engineers who need the decision-making theory behind agents 12 Mastering Agentic AI Ted Winston Advanced Engineers wanting step-by-step multi-agent system construction 13 Hands-On Agentic AI Michael Lanham Advanced Developers who learn best through project-based examples 14 Prompt Engineering for LLMs John Berryman and Albert Ziegler Intermediate-Advanced Any practitioner needing the prompt engineering foundation agents depend on 15 Measure What Matters John Doerr Foundational Context Leaders who want to understand goal-directed systems in business contexts
Level 1: Best Books for Complete Beginners These books require no prior AI or programming knowledge. They build the conceptual foundation that makes everything else easier to understand.
1. Artificial Intelligence Basics – Tom Taulli (2019, Apress) Best for: Absolute beginners who have heard about AI and want a non-technical grounding before reading anything else.
Tom Taulli’s book is consistently recommended as the first AI book for non-technical professionals. It covers what AI is, how machine learning works at a conceptual level, and what natural language processing, computer vision, and robotics mean in plain English. No equations. No code. Just clear explanations of what these systems actually do.
Why it matters for agentic AI specifically: Agentic AI systems combine multiple AI capabilities into one autonomous loop. If you do not understand what a language model does, what a classification system does, or what a search algorithm does, reading about agentic systems is confusing. This book builds the vocabulary that makes everything else comprehensible.
Key takeaway: AI is not magic. It is a collection of specific techniques, each good at a specific type of task. Agentic AI orchestrates those techniques inside a loop. Understanding the components first makes the orchestration obvious.
Limitation: Published in 2019, so it does not cover large language models, generative AI, or the 2023-2026 wave of agentic systems specifically. Treat it as vocabulary-building, then move forward with a more current title.
For a NextAgile resource that covers similar ground but updated to 2026, our guide on what generative AI vs AI means covers the key distinctions in plain language.
2. Human Compatible: Artificial Intelligence and the Problem of Control – Stuart Russell (2019, Viking) Best for: Readers who want to understand the deepest question in AI: how do you build a system that pursues goals without creating unintended consequences?
Stuart Russell is a co-author of the most widely used AI textbook in university programs worldwide (“Artificial Intelligence: A Modern Approach”). Human Compatible is his accessible book for non-technical readers. It explains why goal-directed AI systems are both powerful and dangerous, and what the field of AI safety is trying to solve.
Why it matters for agentic AI specifically: Agentic AI systems are goal-directed. They pursue objectives autonomously. Russell’s book is the most important conceptual grounding for understanding why this creates risks that do not exist in passive AI systems, and what responsible design looks like. The alignment problem that Russell describes, building AI systems whose goals are genuinely compatible with human values, is the same problem that enterprise agentic AI governance is trying to solve at a practical level.
Key takeaway: “Machines that are optimizing a fixed objective are likely to find ways of achieving it that we did not intend and do not want.” Understanding this insight is foundational for anyone designing governance frameworks for agentic AI.
Who reads this alongside: Executives who pair this with MIT Sloan’s Agentic AI course develop the most complete picture of both the opportunity and the governance challenge. NextAgile’s approach to AI governance consulting addresses the practical organizational version of the same challenge.
3. AI: A Guide for Thinking Humans – Melanie Mitchell (2019, Farrar, Straus and Giroux) Best for: Readers who want a rigorous, skeptical, intellectually honest account of what AI actually does and does not do.
Melanie Mitchell is a professor at the Santa Fe Institute and one of the clearest writers in the AI field. Her book examines what AI systems can do impressively and where they fall apart in ways that should make you skeptical of breathless claims. She covers neural networks, machine learning, deep learning, and the ongoing debates about whether any of these systems actually “understand” anything.
Why it matters for agentic AI: In 2026, agentic AI is accompanied by enormous hype. Mitchell’s book gives you the intellectual toolkit to evaluate that hype honestly. She explains why AI systems that appear to reason may be pattern-matching in ways that break unpredictably on novel inputs, which is precisely the failure mode that makes agentic AI governance so important.
Key takeaway: Understanding what AI does not do well is more valuable for building reliable systems than understanding what it does impressively. Every production agentic AI system needs humans who understand the limits.
4. Agentic Artificial Intelligence – Pascal Bornet and 10 Co-Authors (2025, Self-Published) Best for: Non-technical business leaders, executives, and managers who want the most authoritative business-focused account of agentic AI available in 2026.
This is the flagship executive book on agentic AI in 2026. Co-authored by 11 practitioners from institutions including NUS, Babson College, Northeastern University, and Microsoft Research, it is the only book on this list with a genuine multi-disciplinary practitioner perspective. It reached the Forbes Top 10 Tech Books list for 2025. Goodreads rates it 3.92 from 356 ratings as of 2026.
What it actually covers: What agentic AI is and how it differs from earlier automation. Real enterprise case studies of agentic AI deployment across banking, healthcare, manufacturing, and professional services. The economic impact of autonomous AI systems. The governance, ethics, and organizational change management required to deploy agents responsibly. A framework for building an organizational agentic AI strategy.
Who it is best for: Aiifi’s 2026 “6 Best AI Agents Books for Non-Technical Leaders” analysis identifies this as “the best AI agents book for executives” among all 2025-2026 releases. “It is the longest book on this list at 550 pages and the only one co-authored by 11 practitioners.”
Limitation: At 550 pages, it requires significant reading commitment. For executives who want the core thesis without the full depth, Aiifi’s review recommends “AI First by Brotman and Sack” as the shorter alternative.
The governance and organizational change chapters directly align with how NextAgile approaches enterprise agile transformation combined with agentic AI adoption. The same organizational conditions that make agile transformations succeed, leadership alignment, psychological safety, and iterative capability building, are the ones that make agentic AI deployments sustainable.
5. AI First – Brotman and Sack (2024) Best for: Senior executives and board members who want a shorter, faster read on the strategic implications of AI agents, with direct interview content from Sam Altman (OpenAI CEO) and Reid Hoffman (LinkedIn co-founder).
AI First draws on direct interviews with AI industry leaders to construct an argument about how business strategy changes when AI agents can perform meaningful portions of knowledge work autonomously. Aiifi’s 2026 non-technical executive book analysis notes it “leans heaviest on the agent-economy thesis” while covering less governance detail than Bornet’s more comprehensive volume.
What it actually covers: How AI agents change competitive dynamics. What the “agent economy” means for talent strategy, organizational structure, and business models. How to think about AI-first organizational design. Case studies from early enterprise adopters of agentic systems.
Key takeaway: This is a strategic framing book rather than an implementation guide. It is most valuable for C-suite executives who need vocabulary and frameworks for board-level conversations about AI strategy, not for managers who need to make implementation decisions.
Level 2: Best Books for Working Professionals These books assume you understand what AI is and are ready to understand how agentic systems are designed, deployed, and governed in organizational contexts.
6. The Alignment Problem – Brian Christian (2020, W. W. Norton) Best for: Engineers, product managers, policy professionals, and anyone who needs to understand AI safety from first principles to design responsible agentic systems.
Brian Christian spent years reporting from inside AI research labs. The Alignment Problem is the most readable account of the central challenge in AI: building systems that are reliably beneficial. He covers reinforcement learning, reward hacking (when an AI finds ways to achieve its stated goal without achieving the intended goal), interpretability, and fairness.
Why it matters for agentic AI: Every agentic AI system has a goal. Reward hacking, the failure mode where a system achieves its stated objective in an unintended way, is the production failure mode that Brian Christian describes throughout this book. Understanding it conceptually is the prerequisite for designing governance rules that prevent it in practice.
Key takeaway: “The reward is not the goal. The reward is your best attempt to specify the goal in a way the system can optimize. Those are different things, and the gap between them is where disasters happen.” For teams designing agentic AI use cases , this insight is not theoretical. It is the design principle behind every well-constructed stopping condition and human-in-the-loop checkpoint.
7. Designing Autonomous AI Agents – Sinan Ozdemir (2025, O’Reilly) Best for: Technical leads, product architects, and senior engineers who want to understand agentic system design from a practitioner who has built production systems, not a researcher describing what they could be.
Sinan Ozdemir’s O’Reilly book approaches agentic AI from the angle of an engineer who has shipped real deployments. It covers reasoning patterns, planning architectures, tool use, memory management, and the specific failure modes that matter in production: context rot, tool calling failures, permission boundary violations, and observability gaps.
What it actually covers: How to design an agent’s reasoning loop. How to structure memory for long-running agent sessions. How to design tool calling interfaces that fail safely. How to build observability into an agentic system so you can debug it. How to scope the governance rules that define what an agent can and cannot do.
Why it is on this list at Level 2 (not Level 3): The writing is clear enough for technical product managers and system architects to follow without a deep engineering background. You do not need to write the implementation code to benefit from understanding the design decisions.
The observability and governance design concepts in this book align with what NextAgile’s approach to agentic AI architecture for enterprises covers in consulting engagements.
8. Prompt Engineering for LLMs – John Berryman and Albert Ziegler (2024, O’Reilly) Best for: Practitioners at any level who want the technical foundation of how prompts drive agent behavior, which is the most leveraged skill in working with agentic systems today.
Every agentic system runs on prompts. The planning prompt tells the agent how to decompose a goal into steps. The tool selection prompt tells the agent which tool to use for each step. The validation prompt tells the agent whether the retrieved information is sufficient. Understanding how to write prompts that produce reliable behavior at each of these steps is the most directly applicable technical skill in agentic AI work.
What it actually covers: How LLMs process and respond to prompts. Techniques for structured output (getting the LLM to return JSON, markdown tables, or specific formats reliably). Few-shot prompting for agent planning. Chain-of-thought prompting for multi-step reasoning. How to test and evaluate prompt quality systematically.
This book covers the same technical prompt design principles taught in Advanced Prompt Engineering Techniques Workshop . Reading the book gives you the theory. The workshop gives you supervised practice applying it to real agent design scenarios.
9. Multi-Agent Systems: An Introduction – Gerhard Weiss, ed. (MIT Press, 2013) Best for: Engineers, AI researchers, and senior architects who want the academic foundation of how multiple agents coordinate, negotiate, and collaborate.
This is the definitive academic text on multi-agent systems. It covers agent architectures, communication protocols between agents, game theory for agent negotiation, and the coordination mechanisms that allow multiple specialized agents to work toward a shared goal.
Why it is still relevant in 2026: The academic foundations of multi-agent systems established in this book are the theoretical underpinning for modern frameworks like CrewAI, AutoGen, and Microsoft’s Magentic-One. The specific technologies have changed, but the coordination problems the book describes are identical.
Who reads this: Engineers building enterprise multi-agent systems who want to understand why certain coordination patterns work and others do not, rather than just following framework documentation. This book is the “why” behind the “how” of tools like CrewAI and LangGraph.
10. Measure What Matters – John Doerr (2018, Portfolio) Best for: Every level of reader, because the OKR framework Doerr describes is structurally isomorphic to how well-designed agentic systems set and pursue goals.
This is the OKR book. It documents how Andy Grove invented OKRs at Intel and how John Doerr brought them to Google, where they became the organizing principle for the company’s growth from 40 to 140,000 employees. The core idea: define a clear objective and 3-5 measurable key results, check progress weekly, and grade honestly at the end of the cycle.
Why it is on an agentic AI book list: Well-designed agentic systems work exactly this way. The objective is the goal the agent pursues. The key results are the stopping conditions and success criteria. The weekly check-in is the validation loop. The grading is the retrospective that informs the next cycle. Managers who internalize OKR thinking design better agentic AI systems, because they understand goal specification, measurable outcomes, and adaptive iteration at an operational level.
For professionals already familiar with OKRs through NextAgile’s OKR consulting services , this book reframes that knowledge in the language of autonomous goal-directed systems.
Level 3: Best Books for Engineers Building Agentic Systems These books require Python proficiency and familiarity with LLM APIs. They cover the technical architecture of production agentic systems.
11. Building Agentic AI Systems – Sinan Ozdemir (2025, Pearson / Addison-Wesley) Best for: Engineers who want the complete picture of agentic system design, from concept to deployable implementation.
Khired.com’s 2026 roundup of 15 agentic AI books describes this as “the most comprehensive single-volume treatment of agentic AI architecture currently available.” The book covers autonomous agent reasoning, planning architectures, tool integration, multi-agent coordination, memory management, and deployment at enterprise scale. It addresses the architectural patterns that separate toy demos from robust deployable agents.
What it actually covers: How to design an agentic system’s reasoning loop from first principles. How to implement tool calling safely with permission boundaries. How to structure memory for long-running multi-agent sessions. How to build observability and audit logging into an agentic system. How to handle failure modes: context rot, tool calling errors, infinite loops, and permission boundary violations.
Key technical skill it builds: The ability to design an agentic system that fails safely, not just one that succeeds impressively in demo conditions. The Javarevisited Medium review notes: “It walks you through creating intelligent, autonomous AI agents step by step. From reasoning and planning to adaptive learning, it’s written with developers in mind, offering clear examples and implementation details.”
12. Reinforcement Learning: An Introduction – Richard S. Sutton and Andrew G. Barto (2018, 2nd ed., MIT Press) Best for: Engineers and researchers who want to understand the decision-making theory that underpins agentic AI’s core reasoning patterns.
Sutton and Barto’s textbook is the foundational academic text for reinforcement learning. RL is the computational framework where an agent learns which actions to take in which situations by receiving rewards and penalties. It is the theoretical ancestor of modern agentic AI systems, and the concepts of agents, states, actions, rewards, and policies that every current agentic AI framework uses come directly from this literature.
Why it matters in 2026: Modern LLM-based agents use different mechanisms than classical RL, but the vocabulary and the framing of autonomous decision-making under uncertainty remain identical. Understanding Sutton and Barto’s framework makes LangGraph, ReAct agents, and tool-calling LLMs easier to reason about at a deep level.
Limitation: This is a dense academic textbook. It is not a weekend read. Engineers who want to understand the intellectual lineage of agentic AI will find it invaluable. Engineers who want to ship an agent next month should start with Ozdemir’s books instead.
13. Mastering Agentic AI – Ted Winston (2024, Amazon KDP) Best for: Engineers who want step-by-step guidance on building self-directed AI systems with coverage of reinforcement learning, cognitive architectures, multi-agent coordination, and deployment at scale.
Ted Winston’s book takes a practical, project-first approach to the full technical stack of agentic AI. It covers the four core components of autonomous systems (perception, reasoning, learning, and action), provides concrete coding examples throughout, and addresses deployment challenges including cloud, edge, and hybrid environments.
What it actually covers: How to build goal-driven AI agents from the ground up. How to implement reinforcement learning for adaptive agent behavior. How to design multi-agent coordination mechanisms. How to deploy agentic systems at scale with security and performance requirements addressed. The Amazon listing describes it as offering “step-by-step guidance on building goal-driven AI agents, complete with practical coding examples and real-world applications.”
14. Hands-On Agentic AI – Michael Lanham (2025) Best for: Developers who learn best by building projects rather than reading architecture theory.
Lanham’s book is project-based throughout. It provides worked examples of agentic AI applications across different domains, from chatbots to automation systems to research agents, with code walkthroughs for each. The Javarevisited review notes: “It’s great if you learn best by doing. Lanham provides example projects and walkthroughs to help you understand how agents can be applied in different domains.”
What it actually covers: Complete project implementations for a research agent, a coding assistant agent, a customer support agent, and a data analysis agent. How to connect each to real tools and APIs. How to handle the common failure modes that emerge during project development.
The project patterns in this book align with the agentic AI project ideas covered in agentic AI projects guide, which documents real production examples across enterprise contexts. Reading Lanham alongside those examples gives you both the how-to and the organizational context.
15. AI Agents in LangChain – (2026, BookAuthority Curated) Best for: Developers who are specifically using LangChain as their orchestration framework and want a focused, hands-on guide to building agents in that ecosystem.
BookAuthority’s 2026 beginner AI agents books list identifies several LangChain-focused books as the most-searched titles for developers building their first agents. This category of book covers LangChain tools, agent architectures within the LangChain ecosystem, prompt engineering for LangChain agents, memory management using LangChain’s built-in tools, and deployment patterns.
What to look for: When selecting a LangChain-specific book, prioritize those published in 2025 or later because the LangChain framework underwent significant API changes with the 0.3.x release. Books based on the earlier 0.1.x or 0.2.x APIs contain patterns that will not work with current versions.
Better alternative: If you want structured, current LangChain training with direct instructor support, LangChain Mastery Workshop is updated to the current API and taught by practitioners who have deployed LangChain-based systems in enterprise environments, which eliminates the version-currency problem entirely.
How to Build a Reading Plan From This List The most effective approach is to read in sequence within your level, then advance to the next level only when you can comfortably explain the core concepts from the previous level to someone else.
If you are a non-technical executive or business leader: Start with Taulli’s “Artificial Intelligence Basics” for vocabulary, then move directly to Bornet’s “Agentic Artificial Intelligence” for the business-specific treatment. Add “Human Compatible” by Russell if you want the governance and safety foundation for your board-level decision-making. If you are a product manager or technical lead: Start with Ozdemir’s “Designing Autonomous AI Agents” (O’Reilly, Level 2) for the architecture framework, then read “The Alignment Problem” by Brian Christian for the failure mode awareness, then move to “Building Agentic AI Systems” (Level 3) when you are ready to design implementation details. If you are an engineer building agentic systems: Start with “Building Agentic AI Systems” by Ozdemir. Add Lanham’s “Hands-On Agentic AI” for project-based practice. Add Sutton and Barto’s RL textbook when you need the theoretical foundation behind the decision-making patterns you are implementing. Across all levels, John Doerr’s “Measure What Matters” is worth reading to connect agentic AI design principles to the goal-setting and measurement frameworks that make agents useful in organizational contexts. For a practical bridge between OKR thinking and agentic AI design, OKR programs cover the operational OKR practice that connects naturally to well-designed agentic workflows.
Conclusion The agentic AI book category in 2026 is split between two very different audiences, and choosing the wrong book for your level wastes time. Non-technical executives and managers need Bornet, Russell, and Mitchell before they need Ozdemir or Sutton and Barto. Engineers building production systems need Ozdemir and Lanham before they need the strategy titles.
Your next step: use the three-question starting-point test from the introduction of this guide to confirm your level. Then pick the first book in that level and commit to completing it, not starting six books in parallel. One book read carefully and discussed with your team produces more organizational learning than a reading list checked off but not applied.
If you want to accelerate the journey from reading to building, NextAgile’s Agentic AI Workshop is designed for exactly that transition. It takes the concepts from these books and applies them to real organizational use cases in a structured, two-day format with practitioners who have deployed agentic systems in enterprise environments.
Frequently Asked Questions 1.What is the single best agentic AI book for a complete beginner?
“Artificial Intelligence Basics” by Tom Taulli is the most accessible starting point with no prerequisites. If you want to start with a book that is specifically about autonomous AI systems rather than AI in general, Bornet’s “Agentic Artificial Intelligence” is the best choice, but expect to look up some terminology because it assumes basic AI familiarity.
2.Are there agentic AI books specifically for non-technical business leaders?
Yes. Aiifi’s 2026 guide identifies Bornet’s “Agentic Artificial Intelligence,” “AI First” by Brotman and Sack, and “Human Compatible” by Stuart Russell as the three strongest options for non-technical leaders. Most 2025-2026 agentic AI books are engineer-focused, so this category requires careful selection.
3.What books should a software engineer read to understand agentic AI architecture?
Sinan Ozdemir’s two books: “Designing Autonomous AI Agents” (O’Reilly, 2025) for architecture patterns and design principles, then “Building Agentic AI Systems” (Pearson/Addison-Wesley, 2025) for the comprehensive implementation guide. Supplement with Sutton and Barto’s “Reinforcement Learning: An Introduction” for the theoretical foundation.
4.Is “Measure What Matters” by John Doerr really relevant to agentic AI?
Yes, for anyone in a business or product context. Agentic AI systems are goal-directed: they pursue objectives and measure progress toward outcomes. The OKR framework in Doerr’s book describes how to set good goals and measurable success criteria, which is exactly the skill needed to design effective agentic system objectives and stopping conditions. Engineers who understand OKRs design better agentic workflows than those who do not.
5.Are books enough to build practical agentic AI skills, or do I also need courses and workshops? Books build understanding. Practice builds skill. For practitioners who want to build agentic systems, reading Ozdemir’s books alongside a structured workshop like NextAgile’s LangChain Mastery Workshop combines conceptual depth with supervised implementation practice. Neither alone is as effective as both together.
6.What is the best book for understanding agentic AI safety and governance?
“Human Compatible” by Stuart Russell is the conceptual foundation. “The Alignment Problem” by Brian Christian is the operational-level translation of that foundation into specific failure modes that matter for enterprise governance. Together they provide the most rigorous non-technical treatment of why agentic AI needs governance and what good governance addresses. These connect directly to AI governance framework resources for organizations designing practical governance systems.
Rahul seasoned technology leader with 20+ years of experience, now dedicated to mentoring and training individuals and groups in Generative AI, advanced AI/ML system design, and production best practices. He is a hands-on tech entrepreneur and has deep industry experience in building cutting-edge AI products.
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