Thinking Machines Lab: Building Agentic AI and What It Means for Product Leaders
Overview
Mira Murati, former OpenAI CTO, launched Thinking Machines Lab in early 2025. In June, the startup closed a record $2 billion seed round, valuing it at $10 billion . Its mission: create agentic AI—systems that can both reason and take action—signalling a shift in how we define AI utility.
Moving from tools to actors
Traditional AI enhances features—autocomplete, search, recognition. Agentic AI, by contrast, acts on behalf of users: it plans, executes and adapts. That brings new requirements: context awareness, decision transparency and error handling.
Product implications
- Defining agency: Start with clear boundaries. What decisions can the AI make? Where does human oversight sit?
- UX design changes: Interfaces must reveal motivations and next steps. Users expect to inspect or intervene.
- Safety infrastructure: Real-world actions require reliable fallback methods and rollbacks.
- Ethical safeguards: Agentic systems can raise bias or misuse concerns. Audit trails and limits matter.
Roadmap adaptation
To support agentic AI, integrate long-range planning early. Combine feature-level goals with agent behaviour objectives. Build pipelines that feed models with feedback loops, and adapt metrics to include autonomy, trust and human collaboration.
Ecosystem and partnerships
Agentic AI needs access to external systems—APIs, user data, enterprise services. Partnerships become essential, covering domain expertise, compliance and integration tasks.
Challenges ahead
- Complexity: Agents require architecture that manages planning, execution and monitoring.
- Regulation: Expect frameworks on AI actions and accountability to evolve fast.
- Skill shift: Teams need system designers, not just feature builders. Roles will change.
Thinking Machines Lab’s ambitions reflect a fundamental shift: AI will do more than assist—it will act. Product managers must rethink roadmaps, metrics, UX and team structure. Embrace agency now, and you stay ahead.