Agent Services Product Manager for Agent Builder

Job Description
You enjoy shaping the future of product innovation as a product leader, driving value for customers, guiding successful launches, and exceeding expectations. Join our team to shape the future of enterprise agentic platform engineering by delivering a high-quality AI agent builder platform that engineers across the firm depend on every day.
As a Technical Product Manager on the AI Agents Platform, you help build the foundational developer platform that engineering teams use to design, build, test, register, deploy, monitor, and operate AI agents safely and at enterprise scale. This platform provides the reusable components, runtime, and governance that enable teams to build and scale agentic experiences with speed, quality, and consistency.
In this role you own the platform and governance services that carry an Agent Development Life Cycle (ADLC): Agent Cloud; Managed Agents Sandbox; the Agent Control Tower services such as kill switch, throttling, and rollback; the Gateway and Proxy; Identity and on-behalf-of (OBO) delegation; and governance as a service, including the Agent Manifest, and the Agent and MCP System of Record. You are accountable for turning lifecycle operation and governance into platform capabilities that engineering teams consume rather than rebuild, so responsible AI is the default path and operational ownership stops being an adoption gate.
Job responsibilities
Develop a product strategy and vision for the agent platform and governance services so lifecycle operation, identity, and control become platform capabilities rather than wheels each engineering team reinvents.
Own, maintain, and develop a product backlog across ADLC, Agent Cloud, Managed Agents, the Control Tower, Gateway, Identity, and governance that supports the strategic roadmap and value proposition.
Own the Agent Control Tower as the firmwide control plane, where every production agent carries a canonical identity, a lifecycle state, a policy status, and an operational status, and authorized operators can disable or kill, throttle, redirect, roll back, and revoke credentials.
Deliver identity and delegation capabilities including multi-hop OBO downscoping, short-lived scoped credentials, and MCP and agent certification lifecycle.
Own governance as a service, including the Agent Manifest and release graph, and the Agent and MCP System of Record, with automatic evidence capture targeting the vast majority of governance evidence at build time.
Document the ADLC consistently across the SDK and other surfaces, and simplify Managed Agent onboarding during feasibility so governance steps enable adopters.
Embed controls for model usage, tool invocation, data access, entitlements, logging, auditability, and human oversight directly into the developer and operator workflow so compliance is the path of least resistance.
Build the framework and track key success metrics such as production conversion, operational overhead, governance automation rate, reliability, and control effectiveness.
Own end-to-end delivery and execution, planning and prioritizing the backlog of supporting scrum teams.
Partner with Legal, Compliance, Risk, Cybersecurity, Model Risk, and Data Governance to operationalize responsible AI and agentic AI requirements as product capabilities rather than manual gates.
Required qualifications, capabilities, and skills
5+ years of experience or equivalent expertise in AI product management or a relevant domain area.
Advanced knowledge of the Agent development life cycle and Spec Driven Development, design, and data analytics.
Proven ability to lead product life cycle activities including discovery, ideation, strategic development, requirements definition, and value management.
Demonstrated experience with, or a working understanding of, AI and agentic products or platforms, including foundational concepts such as LLMs and Harness.
Hands-on experience using LLMs and AI-powered tools to drive personal and team productivity, with a strong instinct for where AI can be applied to solve problems.
Familiarity with evaluating and instrumenting AI systems, including strong analytical skills to measure quality and performance for non-deterministic, agent-driven experiences.