I continually study how high-velocity teams turn AI ambition into shipped product, and Amplitude’s approach stands out. "Leo Jiang is the Head of Engineering, AI Products at Amplitude, focused on building new AI and marketing products. He has helped build Ask Amplitude, Agents, and AI Visibility." From a product management leadership lens, that portfolio signals a clear AI strategy: enable insight (Ask Amplitude), drive action (Agents), and ensure trust and observability (AI Visibility).
What I appreciate most is the sequencing: start with user-facing value, build agentic AI capabilities where tasks repeat and outcomes can be evaluated, and layer AI workflows with robust governance. For PMs and LLMs for product managers, the implication is to define success via eval-driven development—quantitative rubrics, offline test sets, and real-time feedback loops—before scaling automation. This also hints at an emerging discipline of Agent Analytics: instrument prompts, tool calls, and outcome quality so we can tune performance like we tune a funnel.
Ask Amplitude gives a relatable example: natural-language questions lower the activation barrier for product and growth teams inside an Amplitude analytics environment. When agents turn answers into next-best actions, product-led growth becomes measurable—from hypothesis to change to impact—inside a unified decision loop. That tight loop is where product strategy, design, and reliability meet to create compounding value.
Operationally, I organize a product trio around each capability and pair it with forward deployed engineers to accelerate discovery with customers. I also invest in privacy-by-design and data governance early, ensuring marketing use cases respect compliance while keeping iteration speed high. The goal is a repeatable path from prototype to scale that preserves momentum without compromising safety.
My takeaway for peers: pick one high-frequency workflow, define clear agent boundaries, ship a narrow slice, and measure relentlessly. Use retrieval-first pipeline patterns for grounding, add human-in-the-loop checkpoints, and close the loop with qualitative insights from in-app guides. When that works, expand capabilities—not just features—and let outcomes vs output OKRs steer prioritization.
Inspired by this post on Amplitude – Best Practices.
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