I set out to solve a deceptively simple problem: help our teams ask product questions in plain English and get trustworthy, analysis-grade answers—fast. That required more than a powerful model; it demanded agents that genuinely understand the language of product analytics, from behavioral analytics nuances to the messy reality of event taxonomies, funnels, and cohorts. In this post, I share how we engineered agentic AI that speaks our domain fluently and turns questions into decisions.
The core challenge wasn’t data volume or dashboard sprawl; it was semantics. Different teams said “activation,” “onboarding,” or “first value” and meant overlapping but distinct things. Our PMs, analysts, and engineers navigated a maze of synonyms across Amplitude analytics, Pendo, and our unified analytics platform. Generic LLMs stumbled on these nuances, so we built a shared ontology—driver trees anchored to a clear North Star—with canonical definitions for activation, retention, and conversion, plus consistent event naming and cohort logic.
We started with a rigorous metric catalog: every KPI linked to its drivers, exact formulas, cohorts, and time windows; every event mapped to a product taxonomy; every dashboard and SQL snippet versioned with ownership and lineage. That catalog became the ground truth for agents. We embedded data governance and privacy-by-design from the start—permissioning for fields and queries, PII redaction, and scoped access that reflected how product teams actually work.
Next, we built a retrieval-first pipeline to ground the agents in our corpus before generation. We indexed metric definitions, dashboards, experiment readouts, runbooks, and high-signal Slack threads so the agent could cite relevant artifacts, not just predict plausible text. With careful context window management and prompt engineering, the agent retrieves definitions and prior analyses, then plans multi-step actions: run a query, compare cohorts, check “minimum detectable effect (MDE)” for an A/B test, and summarize findings with references.
Architecturally, we treated this as “Agent Analytics”: an orchestrator that selects tools based on intent—querying Amplitude analytics or Pendo for behavioral paths and funnels, hitting our warehouse for cohort tables, or pulling experiment metadata and anomaly detection alerts. Tool use is permission-aware, auditable, and designed to fail safe. The agent’s outputs include citations back to the exact definitions, dashboards, and SQL used, so reviewers can validate and iterate.
Quality came from eval-driven development, not intuition. We built a gold set of representative product questions (activation inflections, retention analysis by segment, funnel drop-offs after feature launches) and scored the agent on faithfulness to definitions, numerical accuracy, latency, and actionability. We incorporated regression checks to catch drifts after schema changes, and we tuned prompts to reduce overconfident answers and push for clarifying questions when context was missing.
Safety and reliability were non-negotiable. We layered AI risk management with role-based access, guardrails that block destructive queries, and risk scoring for unfamiliar joins or sudden spikes in metric deltas. The agent logs every step—what it retrieved, which tools it called, and why—so analysts can replay and refine the chain of thought with transparent provenance.
The payoff: product teams now self-serve nuanced questions in minutes instead of days, and our analysts spend more time on discovery than report wrangling. Retention analysis improved as the agent standardized cohort logic; conversion investigations accelerated thanks to consistent funnel definitions; and cross-functional decisions aligned around the same driver trees and shared language. Most importantly, the agent turned ambiguous asks into structured analyses that stand up to scrutiny.
For fellow product leaders, my lesson is simple: start with semantics, not models. A crisp ontology, disciplined taxonomy, and clear ownership will outperform a flashy stack riddled with ambiguity. Avoid technology FOMO; favor retrieval-first grounding, small sharp tools, and continuous discovery with your product trios. When your organization speaks a common analytics language, agents can finally think with you, not just for you.
Next, we’re extending the agent’s planning skills to recommend experiment designs, estimate power and “minimum detectable effect (MDE),” and propose driver-tree-informed bet sizing. We’re also tightening feedback loops so every accepted answer, edit, or override strengthens the retrieval corpus and evaluations. The vision: a calm, reliable layer that makes rigorous product analytics feel conversational—and helps teams move from questions to confident action.
Inspired by this post on Amplitude – Best Practices.
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