Product Manager, Enterprise Agent Platform

Decagon Decagon · Vertical AI · San Francisco, CA · Product

Product Manager for an Enterprise Agent Platform, focusing on developer experience, integrations, and enterprise readiness for AI agents. The role involves defining strategy, roadmaps, and working with engineering and security to enable complex enterprise deployments.

What you'd actually do

  1. Define the vision, strategy, and roadmap for developer experience, integrations channels, and enterprise readiness
  2. Decide what we build natively, what we make self-serve, and what we open up so customers and partners can extend it themselves
  3. Work with infrastructure and security on data handling, retention, and residency
  4. Drive channel and partner expansion with go-to-market and BD, including marketplace listings and co-sell motions

Skills

Required

  • 6+ years of relevant experience
  • Experience building integrations, API platforms, or developer-facing products
  • Shipped something where the hard part was the long tail: many systems, inconsistent APIs, and no clean abstraction
  • Experience with requirements large enterprises impose before they'll deploy anything: security review, data handling, compliance artifacts, deployment constraints
  • Strong technical acumen
  • Comfort in front of enterprise customers, including technical evaluations and security reviews
  • Track record of shipping features that moved a real metric

Nice to have

  • Computer Science, Engineering, or Math degree, or equivalent technical experience
  • Experience with iPaaS, API platforms, SDKs, RPA, or browser automation
  • Experience with data privacy and residency requirements (GDPR, CCPA, HIPAA, PCI)
  • Experience in a regulated industry: financial services, healthcare, or similar
  • Experience with headless, single-tenant, or on-premise deployments
  • Experience building or scaling a partner or marketplace ecosystem
  • Experience at a startup or high-growth company

What the JD emphasized

  • least solved part of enterprise AI
  • Getting an agent live inside a bank means wiring into systems built decades before LLMs existed, then clearing a security organization whose job is to say no
  • Experience with the requirements large enterprises impose before they'll deploy anything: security review, data handling, compliance artifacts, deployment constraints

Other signals

  • AI agents
  • conversational AI platform
  • enterprise readiness
  • developer experience
  • integrations