About the Role
The role involves shaping the product vision and roadmap for autonomous AI agents in conversational systems, focusing on how agents reason, plan, execute multi-step workflows, and collaborate across channels such as chat, voice, email, and messaging. The candidate will bridge AI research, enterprise deployment, and platform reliability, defining agent behaviors including goal understanding, tool use, state management, failure recovery, and handoff to human agents. Success requires deep understanding of production-grade agent performance, including reasoning loops, execution reliability, memory management, observability, and scalability under cost and latency constraints.
Responsibilities
- Lead the full lifecycle of the Autonomous Agents roadmap, from identifying problems to delivering solutions and measuring outcomes.
- Convert enterprise customer and partner needs into actionable product requirements through collaboration with Customer Success, Delivery, and Sales/RevOps teams.
- Collaborate with Applied Research and Data Science to establish benchmarks, evaluation methods, and quality standards for AI agent capabilities.
- Coordinate across Engineering (Runtime, Studio, Channels, SDKs), QA, and Delivery teams to align on scope, dependencies, and launch readiness.
- Define and enforce telemetry requirements so all deployed agent features include monitoring for usage, reliability, and business impact.
- Manage the product planning rhythm, including quarterly roadmap inputs, monthly scope finalization, weekly execution reviews, and launch approvals.
- Lead post-launch analysis through monitoring periods, incident reviews, and feedback integration to inform future roadmap decisions.
About the Role
We are seeking a Senior Product Manager to define and execute the product strategy for Autonomous Agents in the conversational AI space. You will shape how AI agents reason, plan, and act across multiple steps and channels in enterprise environments. Your work will connect cutting-edge AI research with real-world deployment needs, ensuring agents can understand goals, use tools and APIs, manage context, recover from errors, and escalate appropriately. You must understand what successful production deployment looks like, including multi-turn reasoning, reliable workflow execution, memory handling, human-in-the-loop processes, behavioral observability, and performance at scale.