As AI agents become essential in SaaS products, chief product officers at Rubrik, Glean, and Harvey share insights on overcoming challenges to build trusted, category-defining AI-driven features that customers will pay for.
- Prioritize workflows where agent mistakes have minimal risk
- Build an extensible AI platform rather than fixed agents
- Empower all product teams with agent-first thinking
What happened
Chief product officers from Rubrik, Glean, and Harvey discussed their experiences building AI-powered agents in SaaS products aimed at different buyer personas. Rubrik’s CPO highlighted the unique challenges of developing agents for cybersecurity recovery workflows where errors are unacceptable. Building an AI agent that can completely replace the UI or operate by chat adds complexity, requiring them to essentially rebuild the product with new failure modes.
Why it matters
Shipping AI agents that customers pay for is a critical challenge reshaping the CPO role in B2B SaaS. Unlike traditional features, AI agents must be carefully scoped by risk and reliability, since errors can erode customer trust and create operational hazards in areas like security and legal software.
This shift demands new product strategies such as extensive workflow mapping, phased rollout focusing on error tolerance, and blending probabilistic AI outputs with deterministic execution for sensitive workflows. The leaders’ approach of democratizing AI agent development across product teams contrasts with centralized AI teams and points to scalable product innovation models.
What to watch next
Also observe the evolving collaboration between AI models and deterministic operational systems, especially in compliance-sensitive sectors like cybersecurity and law. The ability to explain AI decisions and integrate rigorous, auditable workflows will increasingly differentiate market leaders.