The Power of AI Agents in Product Operations Workflows

How product ops teams can move from busywork to big-picture strategy with agentic AI.

In product operations today, AI is everywhere… but often in the wrong places. Embedded into individual tools like Jira or Confluence, AI tends to solve isolated problems: a chatbot here, a smart assistant there. But product operations teams don’t solve in silos. Their job is to orchestrate across systems, people, and strategy. So how do you harness AI to improve the whole workflow, as opposed to just doing one thing better?

Enter AI agents.

In a recent Productboard webinar, Ross Webb (Founder, Product Team Success) and Graham Reed (Head of Product Operations, HeliosX Group) showed how agentic AI—intelligent workflows built on a trained foundation of your business context—can save product ops teams hours each week, reduce cognitive load, and empower more strategic decision-making.

Here’s a glimpse of what they covered, and why you’ll want to watch the full session to see it in action.

Watch “The Future of Product Operations: Empowering teams with AI Automation” on-demand. 

Agentic AI Workflows > One-Off AI Features

AI in most product tools today feels like a sprinkle on top. It might auto-summarize a meeting or rewrite a story description, but it doesn’t connect the dots.

What Ross and Graham argued is simple but powerful: product ops teams shouldn’t be solving individual problems. They should be designing holistic workflows that support the entire product lifecycle.

That’s what makes AI agents different. They don’t just act once. They reason across systems. They work towards outcomes. And, when trained well, they become like a teammate who never sleeps.

“Think of it as automation with a driver’s license,” Ross said. “It’s not just doing tasks. It’s understanding the goals, pulling in the context, and acting across tools.”

Use Cases for Agentic AI Workflows

A few high-impact use cases Ross and Graham discussed include:

• Weekly product health updates: Automatically pull dev progress, usage metrics, and sentiment data into a single report.
• Proactive issue detection: Surface funnel drop-offs or red flags in user behavior before a product manager (PM) even checks the dashboard.
• Context-aware status reports: Generate insights and recommendations tailored to business goals, not just activity logs.
• Custom stakeholder reporting: Adapt the depth, focus, and framing of reports based on audience (e.g. CPO vs. Eng Lead).
• Capacity-aware planning: Feed in engineering capacity and skillsets to model optimal team allocations, now and in the future.

The common theme? Each use case reduces manual effort while improving decision quality. A one-two punch every product team wants.

What You Need to Build an Agentic AI Workflow

Yes, you can use ChatGPT. But as Ross emphasized, that’s just the surface. To build an effective AI agent, you need:

• A centralized brain: All your product context (vision, OKRs, user personas, product architecture) needs to be stored and retrievable—ideally via a vector database like Supabase.
• APIs that play well together: Tools like Productboard, Linear, and PostHog were used in the demo because they have robust APIs. Choose tools that let you extract what you need.
• An orchestration engine: Ross used n8n, a low-code automation platform that connects agents to trigger actions, query tools, and synthesize outputs.
• Time and testing: You’ll need to fine-tune the workflow. Ross’ demo took two weeks to build with a technical partner, and early iterations produced “garbage in, garbage out” results.

“The quality of the information you feed it matters,” Ross reminded. “You have to train the workflow. It won’t be perfect on the first go.”

Building a Product Ops Copilot

To move beyond theory, Ross walked the audience through a real-world example of an AI-powered workflow he built using open tools and real company data. The use case? A weekly product update that would normally require hours of manual work—gathering status updates, analyzing product usage, cross-referencing with goals, and surfacing insights. With his agentic AI workflow, all of that happens automatically.

The first step was creating a “brain” for the system. Ross pulled together the core strategic documents of the company—its mission, vision, product goals, and OKRs—all stored in Google Docs. He imported this documentation into a vector database (Supabase, in this case), a key difference from traditional SQL storage. This setup allows the AI agent to retain memory, retrieve context, and reason across sources. Without this foundational step, the agent wouldn’t understand what “success” looks like for the business or how to prioritize the information it’s gathering.

“Since we did this—just a week or so—she’s saved hours and hours. And more importantly, she’s using that time to talk to stakeholders, push new ideas forward.”

Once the brain was in place, Ross connected a set of tools that product teams already use in their day-to-day work: Productboard for roadmapping and prioritization, Linear for tracking development progress, PostHog for user analytics, and a feedback tool with built-in sentiment analysis. Each system had a specific role in the workflow, feeding data into the central AI engine.

He built the entire workflow using n8n, a low-code automation platform that allowed him to create custom flows across these tools. The agent was programmed to run automatically—say, every Sunday evening—and by Monday morning, a fully-generated status update would land in the product manager’s inbox.

The output wasn’t just a simple digest. It was a weekly-generated executive report that included:

• A high-level summary of product performance
• Key insights surfaced from live data
• Prioritized action recommendations
• Specific next steps tied to business goals

In the demo, the system flagged a major drop-off in a comment-posting funnel inside the app, classifying it as a critical insight. Based on the company’s goals and KPIs, the AI recommended investigating the issue immediately and suggested next steps: initiate user research, analyze feedback, and explore whether the comment flow itself needed optimization.

For the product manager using this system, the impact was immediate. 

Instead of spending hours toggling between tools, compiling data, and trying to spot patterns, she could begin each week focused on the work that matters: shaping strategy and driving outcomes. “Since we did this—just a week or so—she’s saved hours and hours,” Ross said of the PM testing the tool. “And more importantly, she’s using that time to talk to stakeholders, push new ideas forward.” And that’s the promise of agentic AI in product operations: not just automation for automation’s sake, but intelligence that lets you operate at a higher level.

The Reality Check: Agentic AI Isn’t Magic (Yet)

This isn’t plug-and-play.

Building a real, reusable agent takes effort, technical support, and stakeholder buy-in. Not all tools will integrate easily. Not all product teams will be ready. But the cost of not exploring this? Wasting 10+ hours a week on manual work that AI can already do.

Where to Start: Practical Tips + Next Steps

So what can you do today?

• Start with your brain: Inventory your strategy docs, goals, and product context. Is it centralized? Searchable? Up to date?
• Map your stack: What tools do you use for product planning, development, analytics, and feedback? Do they have APIs?
• Identify a workflow: Pick a repeatable report or status update that always eats up time. Could it be automated?

🎥 Watch the full webinar replay. Get the full behind-the-scenes look at how agentic AI is changing product operations.