Your AI Product Manager,
One visible agent is being designed to coordinate research, specs, design, build and QA across Slack, WhatsApp and the tools your team already uses, while ProductOS preserves the product context.
No new workspace to live in.
The PM Agent is intended to meet your team in the channels they already use, while ProductOS keeps the product context and decisions together.
One agent your team sees. Six product responsibilities behind it.
The PM Agent would be the interface. Behind it, named agents would handle each product-development stage through ProductOS MCP.
Each specialist would work within its stage. The PM Agent would route between them and keep product memory current across transitions.
The work product managers lose hours to every week.
The goal is not another chatbot that only answers questions. It is an agent designed to handle the work between the decisions humans make.
Research synthesis
Turn interview notes, support threads and feedback into structured findings and decisions, not another document nobody reads.
Spec drafting
Write PRDs and user stories from approved research. Keep them in one project memory so nothing has to be re-explained at each stage.
Sprint coordination
The PM Agent would keep context across the pod, route work to the right stage, flag what needs a decision, and report what is in flight.
Progress reporting
Summarise what the pod is working on, what blocked it, and what is ready for review, without scheduling another meeting.
Preview preparation
Pull together a pre-flight review package: what was built, what passed QA, what was decided and why.
QA and release readiness
Check browser, API, accessibility and edge cases against the approved spec before anything ships.
A concrete first-week concept to validate.
Not a demo that assumes everything works perfectly. A realistic sequence based on how the pod stages are designed to connect.
Existing docs, roadmaps, interview notes, open decisions. The PM Agent would read them and start building the memory.
A churn reason, a feature request, an interview quote. The PM Agent would route it to the pod and start the first stage.
Research would be logged with sources, or a spec section drafted. You would approve what is ready and flag what needs adjustment.
Design would follow an approved spec, build would follow approved design, and QA would run against the spec rather than a generic checklist.
The PM Agent would prepare a summary of what is ready, what passed, what was decided, and what to act on next.
Automated without being autonomous in ways that matter.
The proposed PM Agent would handle routine work. Anything consequential would be proposed, never pushed through without you.
Automated
Routine read and draft work: research synthesis, spec drafting, status summaries, meeting prep, QA checks.
Requires approval
Anything that touches production, sends externally, deletes content, or has a consequential outcome.
The design requires approval for stage transitions. Work would not move past an unapproved spec or build. Production deploys, external sends, deletion and consequential mutations would always require a human.
Before you join the waitlist.
What is the ProductOS Agent?
ProductOS Agent is a proposed autonomous AI Product Manager. The design uses one visible agent to coordinate a specialist Product Pod across Ideation, Research, Define/PRD, Design, Development, and QA/Review. ProductOS MCP is planned as the permission-aware connection to your product context.
Where does the agent actually work?
The intent is for it to work where your team already communicates: Slack, WhatsApp, Teams, Telegram, email, and the ProductOS web interface. The specific integrations are being shaped as part of the design process.
Does it make decisions on its own?
The proposed design would automate routine read and draft work while requiring human approval for production deploys, external sends, deletion, and consequential mutations. Stage transitions would be proposed, not taken. You would stay in the loop on what matters.
What can it actually do day to day?
Based on what product teams describe losing time to: turning conversation threads into decisions and specs, running research sweeps, maintaining product context across stages, drafting PRDs and user stories, coordinating work across the pod, preparing previews, and checking QA and release readiness.
What is a Product Pod?
The proposed Product Pod groups six specialist responsibilities behind one visible PM Agent: Ideation, Research, Define/PRD, Design, Development, and QA/Review. Each would map to a stage of the product development lifecycle.
Is this live and available today?
No. This page exists to shape the product before it is built. Candid input is useful. Joining the waitlist means you want to be involved in the design process, not that you have access to something that already works.
How is this different from a chatbot or a single AI assistant?
Ordinary standalone chat workflows can require product context to be introduced again. The ProductOS Agent is being designed around persistent product memory and clear specialist responsibilities, coordinated through one PM Agent.
Who is this for?
Founders who are their own product manager, product managers at small to mid-size teams who are coordinating too many tools and too many meetings, and product leaders who want to compress the time between a customer signal and a shipped decision.
Tell us what you need.
This is being shaped with input from product teams. What you tell us directly influences what it does and how it works. Candid answers are more useful than polished ones.
Want to see what the full
ProductOS Agent is being shaped as part of an AI-native product development system spanning research, specs, design, build and deployment.
No commitment required. Early design partners shape the roadmap.