---
title: "It's Time to Lead Your Team of AI Agents"
speaker: Gopika Mohanan
role: Director of Product
company: BestSecret Group
conference: Just Product 2026, Munich, 16 October 2026
track: AI End-2-End PM Workflows
slot: 13:30
duration_minutes: 26
video: https://media.product-masterclass.com/just-product-2026/video/06-gopika-mohanan.mp4
slides: https://media.product-masterclass.com/just-product-2026/slides/06/Just Product 2026 - Gopika Mohanan - It's Time to Lead Your Team of AI Agents.pdf
page: https://www.product-masterclass.com/just-product-2026/recap/talks/06-gopika-mohanan.html
language: en
source: automatic transcript of the livestream, edited summary
---

# It's Time to Lead Your Team of AI Agents

**Gopika Mohanan**, Director of Product at BestSecret Group. AI End-2-End PM Workflows, 13:30, Just Product 2026.

> Working with AI agents forces every product manager, even the most junior, to practice classic product leadership skills from day one.

## Summary

Gopika Mohanan opens with a thought experiment. On Monday your boss tells you five new people joined your team. They research a market in minutes, pull customer insights, write code, take no bathroom breaks and have no working hours. The catch: sometimes they hand you something with full confidence and it is wrong. Would you check every output, write a 15 page instruction and hope? Or would you think about roles, context and decision rights? Gopika leads about eight product managers at BestSecret, where multi-agent systems have moved into the daily work of product teams. Her observation: this shift teaches less about technical skills and more about old product leadership skills, and it pushes them onto junior PMs who used to wait years for a team.

The mental model has moved from question and answer to goal and result, since around May or June. An agent observes, reads expectations, picks tools, proposes, inspects and loops until the task is done. You are directing work, so output quality depends on what you put in. Skill one is delegation and context. Her example: leadership asks for a strategy to reduce returns caused by size and fit. The prompt "look at our size returns and tell me what you find" fails because the agent does not know which markets, which customers, whether return rate or revenue matters, or the time horizon. Like a new employee, it does not know BestSecret is off-price or that an experiment failed six months ago. The fix is an environment of structured context.

Skill two is decision boundaries. Gopika calls "how autonomous should my agents be" the wrong question, since nobody asks that about a human team. Instead you define the points where the agent must involve you. An agent analysing return data may trigger a second analysis on its own, but it must not launch a live test. With agents you can hard code this: the research agent gets web access, the analysis agent gets read access. Skill three is judgment and the quality bar. Agents do not tire after the fifth idea, so fire several at one problem with different lenses such as time to market, risk and user experience, and welcome disagreement. The agent expands the decision space, the PM narrows it. BestSecret now hires PMs mainly for critical thinking.

Skill four is organisational capability. When an agent keeps returning superficial customer analysis, you do what you would do with a team member: teach it how to think, which principles to follow and what good looks like, then encode that into its instructions or memory and test whether outputs improve. Every piece of feedback is a permanent upgrade, and once it works for one agent you can build it into a whole workflow. In production, BestSecret runs product classification with one agent classifying and another challenging the output. Her closing: define the outcome, provide context, set boundaries, own the judgment while we still can, and build a system that improves as a whole. In Q&A she stresses that not everything is agentic, and cost is weighed every time.

## Key takeaways

1. Treat a new agent like a new hire: give it the company context it cannot know, including past failed experiments and what the business is.
2. Replace vague asks with a brief that names markets, customer types, the metric that matters and the time horizon.
3. Build a reusable context environment with strategy, constraints, history and evidence instead of rewriting a long prompt for every request.
4. Stop asking how autonomous agents should be. Write the checklist of decisions where they must escalate to you, such as launching a test.
5. Give agents hard-coded permissions per role: web access for research, read access for analysis, no write access to live systems.
6. Run several agents on the same problem with different lenses such as time to market, risk and user experience, and value their disagreement.
7. When an agent repeats a mistake, teach it what good looks like and encode that in its instructions or memory, then test the next output.
8. Scale agentic setups only where the benefit beats the cost. Build the system around one concrete problem, not around everything.

## Chapters (timestamps in the talk video)

- 0:01 Host intro: four applications later
- 1:48 Monday exercise: five new team members
- 5:16 From question and answer to goal and result
- 7:32 Skill 1: delegation and context
- 13:26 Skill 2: defining decision boundaries
- 16:54 Skill 3: judgment and the quality bar
- 20:35 Skill 4: building organisational capability
- 23:40 What actually changes for a PM

## Highlight moments

- 2:07 to 3:31: **Five new hires who never take breaks**. The opening thought experiment sets up the whole talk: fast, confident, sometimes wrong, so how do you manage them?
- 5:30 to 6:52: **You are no longer using a tool**. The core shift from asking questions to delegating outcomes, ending on the line that you are now directing work.
- 8:48 to 9:42: **Why the vague prompt fails**. A concrete size-and-fit example shows exactly what context the agent lacks and the PM takes for granted.
- 13:55 to 14:57: **How autonomous should my agents be?**. Her sharpest reframe: nobody asks that about a human team, so define escalation points instead.
- 18:45 to 20:03: **Agents expand, the PM narrows**. The drill-me agents and the division of labour between agent and PM, the most quotable practical idea of the talk.
- 24:08 to 25:25: **Own the judgment while we still can**. The five-step closing that ties the four skills together and sends the audience off to lead their new team.

## Quotes

- "You're no longer just using a tool. You're directing work. And so the quality of output very much depends on what you're putting inside." (6:37)
- "You know all of these things as a product manager because you live inside of the problem, but the agent does not." (9:24)
- "If you draw these parallels again with your human team, you will never ask yourself how autonomous should my team be." (14:06)
- "The agent gives you the possibility to expand the decision space and the product manager narrows it." (19:47)
- "The benefit with an agent is every piece of feedback that you give it, it is a constant permanent upgrade." (21:48)
- "Truly work with them to flesh out the problem and solution, but at the end, you own the judgment while we still can." (24:42)

## Q&A (not in the video, summarised from the stream)

**Q: How do you handle enablement, since you cannot just install Claude locally and hand it all your customer data, plus the legal and technical questions that come with it?**

Not everything a product person does is agentic. BestSecret builds systems around specific problems in isolation: for size and fit the setup connects meeting transcripts, Qualtrics data, app review outputs and supply analytics, while customer-facing product classification uses entirely different tooling. Internal and external use cases get their own environment, and because feeding agents more data costs money, every setup is weighed against its true benefit.

## Mentioned

- BestSecret Group, the off-price fashion e-commerce company where Gopika leads about eight product managers
- Size and fit returns, the running example for a product strategy brief delegated to agents
- Drill-me agents, agents some BestSecret colleagues set up whose only job is to challenge every input
- Product classification agents in production, one classifying products, another challenging the output
- Qualtrics, customer feedback data connected to the size-and-fit agent system
- Claude Code, shown on the slides as the multi-layer agent development kit behind the team's setup
- Claude, which host Sebastian used to check that Gopika had applied to speak at every Just Product edition
- TIER, Zalando and Wayfair, where Gopika led product before BestSecret

## About the speaker

Gopika Mohanan is Director of Product at BestSecret Group, where she leads the search, browse, recommendations and infrastructure product organisation. She has led products at scale at TIER, Zalando and Wayfair, and speaks at Product at Heart, Product Management Festival, Turing Fest and Product Led Summit.

- LinkedIn: https://www.linkedin.com/in/gopikaem/