The need for Agentic AI Workflow Product Managers - Intersection of AI Agents, Automation, and Processes.

From traditional automation to Autonomous AI Agent systems that think, plan, and execute independently - a practical roadmap for AI PMs leading the Agentic transformation.

Introduction

The rise of AI Agents capable of autonomously planning and executing tasks is reshaping AI Product Management. We’re no longer just adding single AI/ML features into apps – we’re orchestrating intelligent workflows of AI “doers.” This evolution has given birth to a new AI Product Management focus: the Agentic AI Workflow PM.

What Are Agentic AI Workflows (and How Are They Different)?

Agentic AI workflows are AI-driven processes where autonomous AI Agents make decisions, take actions, and coordinate tasks with minimal human. They leverage core capabilities like reasoning, planning, and tool use to handle complex, multi-step tasks. This is a major leap from traditional automation (e.g. scripted macros or RPA bots) which rigidly follow predefined rules and steps. Traditional systems can excel at repetitive, well-defined processes, but they struggle with dynamic or unexpected inputs.

Unlike traditional workflows – which often require humans to babysit the process with prompts or handle edge cases – an Agentic AI workflow allows the AI Agent itself to act autonomously, searching for information, analyzing results, and taking actions to complete tasks with little oversight. In short, a traditional workflow might be a fixed assembly line, whereas an agentic workflow is more like a skilled assistant that can plan, improvise, and iterate.

To illustrate, a rule-based customer support bot may follow a decision tree and escalate anything unusual to a human. In an agentic workflow, an Customer Support AI support agent could interpret the issue, investigate across systems, take steps to resolve it, and only loop in a human if absolutely needed, adapting its plan if initial attempts fail. This adaptability – powered by techniques like large language models reasoning through steps – is what makes a workflow “agentic.” It’s a shift from static automation to adaptive autonomy.

What Makes AI Workflows "Agentic"?

Agentic AI Workflows represent a significant evolution from traditional automation and static AI systems by introducing dynamic, iterative, and intelligent decision-making capabilities. Unlike traditional workflows that follow predefined rules, agentic systems demonstrate six key characteristics:

1. Autonomy: AI Agents operate independently, making decisions and taking actions without constant human oversight.

2. Dynamic Adaptation: AI Agents adjust their actions in real time based on new data, feedback, or unexpected conditions, rather than following rigid, pre-set rules.

3. Iterative Problem-Solving: Tasks are broken down into smaller, manageable steps. The AI Agent reflects on results at each stage, refines its approach, and iterates until the desired outcome is achieved.

4. Tool Use and Integration: AI Agents leverage a range of tools—like APIs, RPA (Robotic Process Automation), and NLP (Natural Language Processing)—to interact with systems, retrieve information, and execute actions.

5. Multi-Agent Collaboration: Complex Agentic AI Workflows may involve multiple AI Agents collaborating, each specializing in different sub-tasks, to enhance efficiency and problem-solving capabilities.

6. Continuous Learning and Improvement: AI Agents learn from each completed workflow, logging outcomes and refining future actions for greater accuracy and efficiency.

The Evolution: From Static to Autonomous

The progression from traditional automation to agentic AI represents a fundamental paradigm shift:

Real-World Examples: Industry Leaders Building Agentic Systems

Amazon's Customer Support Revolution Amazon is at the forefront of deploying AI agents in customer support, leveraging advanced virtual agents across its platforms to automate, personalize, and streamline the customer experience. Their implementation includes:

• Amazon Q in Connect: Generative AI assistant enabling self-service across voice and digital channels
• Omnichannel Intelligence: AI agents operating across voice, chat, and digital channels with consistent experiences
• 70% Automation Rate: Resolving routine queries without human intervention
• Continuous Learning: System designed for ongoing enhancements with automatic AI model updates

Microsoft's Scientific Discovery Platform Microsoft Discovery acts as a scientific AI assistant that orchestrates specialized AI Agents based on researcher's prompts, identifying which agents to leverage and setting up end-to-end workflows covering the full discovery process.

GitHub's Agentic Code Assistant GitHub Copilot is evolving from an in-editor assistant to an agentic AI partner, with features like agent mode streamlining how developers code, check, deploy and troubleshoot.

UiPath's Hybrid Automation Agentic automation involves a symbiotic combination of AI Agents, RPA robots, and people, where people provide goals and agents execute complex decision-making processes.

Why Agentic Workflows Matter for AI Agent Products

Agentic AI workflows are becoming the backbone of cutting-edge AI Agent products. Instead of just making a prediction or answering a question, these products can autonomously drive an entire process. This capability unlocks several advantages:

• End-to-End Autonomy: Agentic AI Workflows enable AI Agent products that plan, decide, and act to fulfill user goals, end-to-end. For example, rather than a scheduling app that suggests meeting times, an AI Agent could coordinate your schedule, send invites, book a venue, and remind participants – all from a single user request. This hands-free experience is transformative for users.
• Higher Efficiency & Scale: By letting AI Agents handle complex, multi-step processes, businesses can achieve new levels of efficiency. These AI Agents don’t get tired and can work 24/7, scaling operations without proportional headcount. Studies note that by automating intricate workflows and making real-time decisions, Agentic AI Workflows can speed up processes and reduce the need for manual intervention (and errors) that traditional automation would struggle with automation. Organizations benefit through improved operational throughput, faster response times, and the ability to handle tasks that previously bogged down teams.
• Adaptability: AI Agent systems can adapt on the fly. Where a hard-coded process would break or require human help in the face of an exception, an AI Agent can adjust its strategy. It can learn from new data or feedback, meaning the workflow improves over time. This makes AI Agent products more resilient in real-world conditions. Unlike static automation, Agentic AI workflows remain flexible by adapting to real-time data and unexpected conditions – a key to handling the messiness of real business scenarios.
• Better Decisions: Because these AI Agents leverage AI (often large language models or other ML components), they can analyze large amounts of data and make informed decisions within the workflow. This means smarter handling of tasks without waiting on human judgment calls. According to industry reports, Agentic AI workflows not only automate work but also support more informed decision-making by dynamically analyzing data and surfacing insights that conventional tools might miss. In essence, an AI Agent product doesn’t just do more – it decides more, potentially leading to better outcomes.
• New Product Paradigms: Perhaps most importantly, Agentic AI workflows open up entirely new product experiences. We’re moving from a world of single-point tools to interconnected AI conductors. One observer described it as going from “manual clicks in 5 tools → a single NLP command triggering multi-agent logic.” In other words, instead of a user manually operating multiple apps to get something done, a single request to an AI Agent could orchestrate a whole suite of actions across those apps. This is a fundamental shift in how we design products and user experiences. AI Agent products built on agentic workflows can delight users by delivering outcomes (the meeting scheduled, the report written, the error fixed) rather than just outputs or suggestions. Just as robotics automated physical factory work, Generative AI and Agentic AI workflows are poised to automate digital knowledge work at scale. AI Product teams that harness this will shape the next generation of “autonomous” applications.

Distinguishing AI Agent Workflows vs. Agentic AI Workflows

Understanding the Spectrum

While both involve AI-driven automation, they differ significantly in scope, complexity, and autonomy:

AI Agent Workflows Excel At:

• Automating predictable tasks (e.g., 80% of IT helpdesk requests)
• Single-domain problem solving
• Scenarios requiring high reliability and consistency

Agentic AI Workflows Tackle:

• Open-ended challenges requiring creativity and adaptability
• Multi-domain coordination and optimization
• Complex business processes requiring judgment and context

Why We Need a Dedicated Agentic AI Workflow PM

Given the above, it’s clear that building an AI Agent product isn’t business-as-usual. It’s not just adding an AI API to a feature; it’s designing a whole autonomous process. This complexity is why a specialized Agentic AI Workflow Product Manager role is emerging – someone who owns the techno-functional orchestration of these AI-driven workflows. Here’s why this role is critical and how it differs from a classic PM role:

• Techno-Functional Ownership: An Agentic AI Workflow PM sits at the intersection of product vision and technical execution. They need to understand the capabilities and limitations of AI Agents (LLMs, tools, memory, etc.) and also have the product sense to apply these to user problems. In many teams today, pieces of this puzzle are scattered: engineers wire up agents and APIs, ops teams tinker with automation scripts, and product managers might still think in terms of siloed features. The Agentic Workflow PM brings these threads together into one holistic roadmap. They provide a comprehensive product vision plus the technical fluency in areas like orchestrating AI Agents, designing prompts, and integrating AI into workflows. This PM essentially becomes “the orchestration layer between humans and machines” in the organization – translating business objectives into AI workflow designs and vice versa. This techno-functional leadership ensures the autonomous agent isn’t just a cool demo, but is delivering real user and business value in a reliable way.
• Designing the Orchestration Logic: Traditional PMs define feature requirements and user stories. An Agentic AI Workflow PM, on the other hand, must design the logic of an AI-driven process: e.g. how an AI agent breaks down a user goal into tasks, which tools or APIs it should use at each step, how it should react to different outcomes, and how multiple agents might hand off tasks between each other. This is essentially workflow design meets product design. For instance, if the product is an AI that handles support tickets, the Agentic AI Workflow PM will outline the agent’s process: listening to the issue, checking knowledge bases, maybe calling a “refund agent” or a “tech troubleshooting agent” as needed, then closing the loop with the customer. Such orchestration is dynamic and requires thinking in terms of flows and states, not just screens and clicks. It’s product management for an adaptive system. In fact, the shift from designing static software to designing LLM-assembled workflows is so significant that it “requires someone to design the AI workflows, connect tools, and ensure a coherent UX” across the automated process. This isn’t just coding a Zapier script; it’s architecting an intelligent sequence of actions. Having a PM focused on this ensures the logic is robust, user-centric, and aligned with business rules (e.g. compliance or quality checks at the right points).
• Agent-to-Agent & Human-in-the-Loop Coordination: In agentic products, it’s often not a single AI doing everything. There may be multiple specialized agents that need to coordinate (as in our support example with a refund agent, a QA agent, etc.), and there are often points where human input or approval is critical (for example, confirming a high-stakes decision or providing feedback on an AI-generated output). The Agentic AI Workflow PM owns this coordination design. They decide which tasks are handled by which agent, and how those agents communicate results to each other. They also define when to insert a human-in-the-loop. For instance, the PM might specify that an AI-generated email draft should get a human manager’s approval before sending, or that an agent must get confirmation for any action involving spending money. By thoughtfully inserting human checkpoints or fail-safes, the PM balances autonomy with control. This coordination aspect is a new kind of challenge – it’s like managing an invisible team of AI workers. The dedicated PM ensures smooth handoffs between agents and between AI↔human, so that the overall workflow is seamless and trustworthy. As one practitioner put it, this role involves shaping the collaborative dynamics between humans and AI, building user trust by acknowledging where AI might err and designing appropriate oversight. In practical terms, that means things like audit logs, approval queues, fallback options, and transparent UX for users to intervene if needed.
• Performance Monitoring & Iteration: When you have autonomous AI Agents running around doing work, you absolutely need to monitor their performance. Part of this PM’s job is defining the right metrics and feedback loops to measure how well the Agentic AI workflow is doing and to improve it. Is the agent completing tasks successfully? How often does it need human help? Are its decisions correct and efficient? The PM should set up dashboards or reports showing key performance indicators (KPIs) for the AI-driven workflow (e.g. task success rate, time saved, user satisfaction, error occurrences). Moreover, because these systems can learn, the PM leads the charge on feeding the right data back into the agents for continuous improvement. Industry guidance suggests baking in an evaluation phase in the workflow where the AI collects results and performance metrics and learns from them. For example, after an AI Agent completes a process, it might log the outcome and user feedback; the PM can then analyze where the agent got confused or took too long, and work with engineers to refine the prompts or add training data. Ensuring “accountability” for an autonomous agent is a new responsibility – one that this PM takes on by treating the AI’s output like any other product feature that needs quality assurance and optimization. This includes maintaining auditability (so issues can be traced and debugged) and setting guardrails so that if the AI goes off-script, it fails safe. In sum, the Agentic Workflow PM is continuously tuning the orchestration for reliability and performance, much like an operations manager for a digital workforce.

All of the above highlight why a focused role is needed. It’s not that traditional PM skills go away – in fact, they’re more important than ever – but they must be applied to a new context. The Agentic AI Workflow PM combines the strategist’s view (what should this AI agent do for users?) with the architect’s view (how will this multi-agent system accomplish it?). One LinkedIn author succinctly noted: “This isn’t just automation – it’s dynamic, adaptive orchestration, and it needs product leadership and coordination, not just engineering.”

In other words, without a PM driving the show, you risk ending up with a bunch of tech demos rather than a coherent autonomous product. With the right ownership, however, these agentic workflows can truly transform your product’s value proposition.

Bringing MVP Thinking to AI Agents: The Minimum Automated Concept (MAC)

Building Agentic AI products comes with high technical uncertainty. Can the AI Agent actually handle the task? Will users trust it? Traditional MVP thinking falls short for autonomous systems. The MAC framework provides a structured approach to building autonomous capabilities safely and incrementally.

Here’s where we introduce the idea of a Minimum Automated Concept (MAC) – to adapt the classic MVP (Minimum Viable Product) approach to AI agents. In traditional product development we start small (an MVP) to test viability; in agentic AI development, we start with a MAC to test autonomy.

A Minimum Automated Concept is the smallest functional Agentic AI Workflow that can prove out the core idea of your AI agent product. It’s about identifying the toughest, most critical part of the automation and building just enough to see if the agent can pull it off in a real-world scenario. Especially in AI Agent projects, going straight for a fully-loaded, feature-rich AI Agent is risky. You could spend a year building a complex AI Agent system only to find that, fundamentally, the AI Agent can’t reliably do the job or users won’t accept its decisions. By starting with a MAC, you mitigate risk and answer the burning question first: “Can an AI agent actually perform this task end-to-end in a real setting?”. If the answer is no, it’s far better to learn that early with a lightweight prototype than after massive investment.

Think of MAC as the MVP of an autonomous AI Agent processes. You’re not trying to build every feature, you’re trying to prove the agent can autonomously “walk” before you make it run a marathon. For example, if you’re building an AI sales assistant that autonomously sends follow-up emails, your MAC might be an Agentic AI workflow that can draft one follow-up email and have a human user decide to send it (or edit it). That single loop – reading a conversation, drafting an email, and incorporating feedback – could be a MAC. From it, you’d learn: does the AI’s email sound on-point enough? Do users feel comfortable with it? What’s the failure rate? Those insights are invaluable before you scale up to an agent that handles a full campaign. MAC is all about technical and user validation at minimal cost.

Importantly, a MAC isn’t throwaway; it forms the foundation you’ll later expand. If it works well, you then iterate and enhance toward a more “awesome” product. MAC embodies the MVP ethos of “build-measure-learn” but tailored to autonomy. You build the smallest Agentic AI autonomous workflow, measure its performance and user reception, and learn what to improve or whether to pivot. By proving out an end-to-end automation on a small scale, you de-risk the path to a larger AI Agent. This concept shapes your MVP thinking by forcing you to prioritize the hardest parts of an agentic product first – the core automation that delivers value. Everything else (nice-to-have features, UI polish, additional use cases) can come later once you validate the MAC.

For AI PMs, adopting the MAC mindset means when you start an AI Agent project, ask: “What’s the minimum autonomous capability that will make this product worthwhile?” Focus there. Maybe it’s the agent being able to successfully complete one type of transaction without help, or an agent reliably using two tools in sequence to achieve a goal. Nail that, test it in the wild, and use the feedback to guide your roadmap. This approach not only conserves resources, it also helps shape an MVP for an agentic product that is truly viable – viable in terms of autonomy, not just code completeness.

Core MAC Principles:

1. Core Autonomous Loop Start with the smallest autonomous decision-making capability that can operate without human intervention for basic scenarios, with built-in safety mechanisms and fallback procedures.

2. Minimal Viable Intelligence Define the minimum level of "reasoning" required for autonomous operation, establish clear success/failure criteria, and create learning mechanisms that improve performance over time.

3. Human-AI Handoff Points Design clear escalation paths when autonomous systems reach their limits, maintain oversight without disrupting workflows, and enable seamless transitions between automated and manual processes.

MAC Implementation: Progressive Autonomy

Level 0: Current Manual Process

• Human detection, decision, and action
• No automation
• High error rates and processing time

MAC Level 1: Single Autonomous Action

• Automated detection/classification
• Human decision and action
• Safety net: Human review of all automated actions

MAC Level 2: Decision + Action Loop

• Automated detection and decision-making
• Automated action execution
• Safety net: Human monitoring with override capability

MAC Level 3: Full Autonomous Workflow

• Complete autonomous operation
• Self-learning and optimization
• Safety net: Exception handling and escalation paths
• Practical MAC Example: Customer Support Evolution

Practical MAC Example: Customer Support Evolution

Traditional Workflow: Agent reads ticket → Manually categorizes → Routes to specialist → Human resolution

MAC Level 1: Auto-categorization → Human review → Manual routing → Human resolution

MAC Level 2: Auto-categorization + Auto-routing → AI-generated initial response → Human review

MAC Level 3: Complete autonomous resolution → Continuous learning → Human escalation only for complex cases

The Agentic AI Workflow Product Manager Role

Redefining Product Management for AI Agent Systems

Traditional AI PM Focus: Managing features that respond to user inputs

Agentic AI Workflow PM Focus: Orchestrating systems that pursue goals autonomously

Agentic AI Workflow Product Management is the practice of leveraging autonomous AI Agents to orchestrate, manage, and optimize the entire product management lifecycle. Unlike traditional AI, which typically supports discrete tasks, agentic AI brings autonomy, agency, and accountability to product operations.

Key Characteristics of the New Role

1. Autonomy Management: AI Agents that independently plan, execute, and adapt tasks across the product lifecycle, from market research to roadmap prioritization and stakeholder management

2. Context-Aware Orchestration: AI Agents are trained on a company's unique product context—its vision, objectives, user personas, and architecture—allowing them to reason across systems and make informed decisions

3. Proactive Intelligence: Agentic AI Workflows don't just wait for prompts; they proactively surface insights, flag issues, and recommend actions based on real-time data and business goals

4. Cross-Tool Coordination: AI Agents connect and coordinate data from multiple product management tools (e.g., Productboard, Linear, analytics platforms) to generate holistic reports, status updates, and recommendations

5. Accountable Autonomy: Actions taken by AI Agents are trackable, with clear guardrails and audit trails, ensuring product managers can review and adjust as needed

Technical Competencies for Agentic AI Workflow PMs

1. Multi-Agent System Design

• Understanding AI Agent orchestrator-worker patterns
• Designing AI Agent communication protocols
• Managing inter-AI Agent dependencies and conflicts
• Implementing shared memory and context systems

2. Autonomous Decision Frameworks

• Creating decision trees for autonomous systems
• Risk assessment and mitigation strategies
• Escalation path design and human-in-the-loop integration
• Performance monitoring for autonomous decisions

3. Workflow Orchestration Understanding that AI Agent development involves business logic and workflows, natural language, machine learning, data management, security, and monitoring, you need expertise in:

• State machine design for complex workflows
• Event-driven architecture patterns
• Distributed system principles
• API-first integration strategies

How to Succeed as an Agentic AI Workflow PM

For those AI product managers and tech PMs looking to pivot into this emerging role, here are some practical tips and takeaways:

• Adopt a Systems & Workflow Mindset: Start thinking of product solutions not as static features, but as end-to-end workflows. Practice mapping out processes (e.g., draw flowcharts of how an AI Agent might handle a user request). This systems thinking will help you spot edge cases and interdependencies. It also aligns with core PM skills – in fact, process mapping and understanding complex flows are listed as key strengths for this role. So, hone those skills and apply them to AI-driven scenarios.
• Build Technical Fluency in AI Orchestration: You don’t need to be a software engineer, but you do need to understand how AI Agents function. Invest time in learning the basics of LLMs (Large Language Models) and how they can be prompted, as well as how tools like APIs or web actions can be integrated. Try out frameworks for creating agentic workflows – for example, experiment with an open-source library like LangChain or a platform like Dust or AutoGen. These tools let you chain AI reasoning with tool usage, and playing with them will give you intuition on what’s hard vs. easy for an AI Agent. The more conversant you are in the tech, the better you can design feasible workflows and communicate with your engineering team. Essentially, aim to become that PM who can sketch the architecture of an AI agent on a whiteboard – connecting data sources, AI models, and steps – not just the UI screens.
• Start Small with a Minimum Automated Concept: When working on AI Agent projects, always define the MAC for your idea. Ask, “What’s the smallest autonomous task we should test first?” Use that to drive an early prototype. This might mean picking a narrow use-case or a single type of request to automate. By delivering a MAC to users or stakeholders, you gather early feedback and de-risk the project. You also demonstrate progress. For example, if your vision is an AI that handles all HR onboarding paperwork, your MAC might be “AI agent automatically sets up a new hire’s email account and schedules orientation.” Prove it can do that reliably before expanding scope. Adopting this approach will save time and build credibility – you’re showing that the agent can actually do something valuable on its own before pouring resources into the grander vision.
• Design for Trust and Transparency: Users and colleagues will understandably have concerns about letting AI Agents run loose on important workflows. As the PM, you need to architect trust into the product. Concretely, this means identifying where a human should review or approve actions, logging what the agent is doing (so there’s an audit trail), and providing users with visibility or control. For instance, you might include a “preview” step where the AI’s result is shown to a user for confirmation (like an AI email draft that the user can edit or okay to send). Or if the agent is customer-facing, ensuring it clearly identifies as AI and can hand off to a human on request. Plan for the failure modes — what if the agent gets stuck or makes a bad call? Design safe fallbacks (e.g. alert a human agent or revert the changes). By acknowledging the AI’s fallibility and building in safeguards, you not only protect the user experience but also help your organization feel comfortable scaling the workflow. Remember, maintaining user trust is paramount in automation. A breach of trust (like an autonomous agent spamming customers or making a critical error unnoticed) can be disastrous. So bake ethics, compliance, and user respect into your workflow requirements from day one.
• Master Cross-Functional Coordination: AI Agent products sit at the crossroads of many domains – data science, engineering, UX design, operations, and of course the business stakeholders. To be effective in this role, you must coordinate all these players. Emphasize collaboration: bring engineers into early workflow design discussions (their input on technical feasibility is gold), involve UX in creating interfaces for oversight (like dashboards or review screens for the AI’s actions), and align with operations/support on how to handle exceptions. You may even find yourself coordinating AI Agents as new team members in a sense, treating them as part of the cross-functional team. Make sure everyone understands the goals of the Agentic AI workflow and their part in it. Also, educate your broader team and management about what agentic AI can and cannot do – setting the right expectations prevents panic or unrealistic demands later. Being a strong communicator and “translator” between AI tech and business will set you apart in this role.
• Measure, Monitor, Iterate: Define what success looks like for your AI-driven workflow and instrument it. Maybe it’s reducing resolution time from 2 days to 2 hours, or automating 80% of Level-1 support tickets, or achieving a certain accuracy in task completion. Use those metrics to monitor the live performance. Treat your AI Agent’s output like a product feature you continuously improve. For instance, track errors or when the AI defers to a human, and categorize those cases to inform what to tackle next (do you need to expand the agent’s knowledge base? Improve its prompt? Add a new tool integration?). Plan regular review cycles of the agent’s performance with your team – this is analogous to model evaluation in ML, but as a PM you frame it in terms of user impact and business KPIs. By closing the loop (monitor -> learn -> update), you’ll keep the workflow’s quality high. In an agentic world, launch-and-forget doesn’t fly – launch, monitor, and learn is the mantra.
• Stay Current and Keep Experimenting: The field of AI Agents and Agentic AI workflows is evolving rapidly. New frameworks, techniques, and case studies are coming out literally every week. Dedicate time to continuously learn – follow research blogs, join communities (there are many forums discussing prompt engineering, multi-agent systems, etc.), and even toy with the latest demos. This will spark ideas for your own product and keep you from reinventing the wheel. For example, if someone open-sources a great tool for memory management in agents, you’d want to know and possibly leverage it. Encourage a culture of experimentation in your team: perhaps run hackathons or proof-of-concepts to try out new agent capabilities. The more hands-on you are, the better you can lead this function. Remember that you’re at the frontier of product management; comfort with ambiguity and eagerness to tinker will serve you well.

Agentic AI Workflows represent a paradigm shift in product design, and they demand a new flavor of product management. By understanding what they are, why they matter, and how to manage them, you position yourself at the forefront of AI-driven innovation. Whether you’re enabling a single AI Agent to automate a task or coordinating an army of agents, the principles remain: start small (find your MAC), ensure clarity and trust, and orchestrate with purpose. The Agentic AI Workflow Product Manager isn’t just a title – it’s a strategic function that will increasingly determine which AI products succeed. With the insights and examples above, you can begin to apply these ideas in your own role. Good luck, and happy orchestrating!

In summary, the Agentic AI Workflow PM role is both challenging and exciting. You get to pioneer new ground where AI Product Management meets AI systems design. By focusing on workflows (not just features), balancing autonomy with oversight, and iterating via concepts like MAC, you can drive the creation of truly intelligent products. This role calls for a blend of visionary thinking and nuts-and-bolts coordination. If done right, you’ll be orchestrating AI agents to deliver magical user experiences – effectively scaling yourself by managing not just human teams, but AI “teams” as well. For AI PMs looking to level up, this is a prime opportunity to become the translator between human needs and AI capabilities in your organization. It’s still early days, which means now is the time to build expertise in agentic workflows and establish yourself as a leader in this space. The era of “autonomous product management” is just beginning – and those who embrace it will help shape the future of how we build products in the age of AI.