James Evans, Head of AI at Amplitude, shares insights on outcome-driven AI agents, responsible practices, and the future of product analytics in this exclusive interview.
In an era where AI-powered copilots and chatbots are becoming commonplace, James Evans, Director and Head of AI at Amplitude, is charting a different course. As the co-founder of Command AI—acquired by Amplitude in late 2024—Evans now leads the charge in developing outcome-driven, context-aware AI agents designed to transform how product teams work.
Under his guidance, Amplitude’s AI strategy goes beyond generic automation, embedding intelligence directly into its behavioral analytics platform to deliver measurable impact on conversion, onboarding, and engagement. In this interview, Evans shares how his team differentiates Amplitude’s AI offerings, balances innovation with responsible AI practices, and builds a high-performing culture. He also offers a glimpse into the future of AI at Amplitude—from multi-agent orchestration to richer, multimodal feedback systems—highlighting a bold vision for the next generation of product analytics.
Full interview;
How did Amplitude’s AI agent strategy differentiate its offering in a competitive market?
We focused on delivering outcome-driven, context-aware AI Agents, not just generic copilots or code generators. Amplitude AI Agents are task-specific experts that work 24/7 to improve conversion, onboarding, feature adoption, and monetization. By deeply embedding AI into our behavioral graph and digital analytics infrastructure, we created more impactful agents, more autonomous (with user control), and more integrated into how teams build and ship products.
How do you prioritize AI initiatives with uncertain ROI and assess long-term value?
We prioritize initiatives that remove friction and unlock speed, like turning slow, resource-bound product workflows into high-speed, multi-track systems. Our north star is measurable customer impact. We ask: Can this agent meaningfully reduce time-to-insight or time-to-impact? Does it help teams do things they couldn’t before? If the answer is yes and it’s scalable across our customer base, it moves forward.
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Given growing ethical and data privacy concerns, how does Amplitude ensure responsible AI practices?
Customer trust is built into the architecture. Users set the autonomy level and guardrails for every AI Agent. Nothing customer-facing happens without human approval. We’re committed to a human-in-the-loop model where AI augments, not overrides. Additionally, we don’t treat data as a black box. We use data transparently, within the limits of customer consent and control.
How do you build a high-performing AI team, from recruitment and skills development to fostering innovation?
We hire people who are not just technically strong, but deeply curious about solving real customer problems. From there, it’s about creating the right conditions for them to thrive. We focus on early alignment around goals, prioritize cultural onboarding, and give people the autonomy to own meaningful work. That foundation enables a culture of experimentation, fast iteration, and collaboration across product, engineering, and design.
How do Amplitude’s AI agents leverage their behavioral graph to surface insights that traditional analytics miss?
The agents don’t rely on static dashboards, but operate directly on Amplitude’s unified data model and behavioral graph. That allows them to detect subtle patterns in user behavior, identify anomalies, form hypotheses, and test them in real time. Traditional tools surface what’s already being asked. Our agents surface what should be asked (but isn’t) based on continuous, contextual observation.
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What technological frontiers are you exploring for Amplitude’s AI?
We’re investing in multi-agent orchestration, enabling teams of AI Agents to collaborate toward complex goals. We’re also exploring more autonomous decision-making agents that can suggest, test, and validate product changes with minimal human input. Over time, we see opportunities in multimodal inputs and richer feedback loops that combine qualitative and quantitative data.
How do you design AI agents for intuitive, seamless user integration?
We solve real, high-friction problems, like diagnosing drop-offs or improving feature adoption. Each agent is purpose-built with a clear objective and works in a way that’s native to the user’s workflow. Our agents are customizable, tailored to your unique needs. We also prioritize explainability and the idea that agents don’t just act; they show their reasoning, surface evidence, and invite user feedback. That balance of power and transparency is what makes them stick.
