How We Built Agents That Understand The Language of Product Analytics

Why It Matters

Embedding product‑specific context into AI agents dramatically improves analytics accuracy while controlling cloud‑query costs, making AI‑assisted product intelligence viable for enterprise teams.

Key Takeaways

• Amplitude agents draw on built‑in semantic metadata for contextual queries
• Nova’s pre‑computed model reduces query costs up to eight‑fold
• Evaluation framework ties agent performance to real‑world analytics tasks
• Customer‑created dashboards directly enrich agent understanding in real time
• Asynchronous specialized agents enable continuous insight without user interaction

Pulse Analysis

Amplitude’s Global Agent tackles a core limitation of generic AI‑driven analytics: lack of product‑specific context. By treating the Amplitude platform itself as a semantic layer, every event name, property description, dashboard, experiment and uploaded knowledge document is embedded into a searchable metadata space. When a user asks a question, the agent pulls relevant embeddings, combines them with the current page context and chat history, and feeds this enriched prompt to a large language model. This approach mirrors how seasoned analysts mentally stitch together definitions, cohort nuances and experiment histories, allowing the AI to generate SQL that reflects the true business meaning rather than a syntactically correct but misleading result.

To prove the concept, Amplitude built a four‑tier evaluation framework—descriptive, diagnostic, predictive and prescriptive—mirroring the questions product teams actually ask. Early versions scored only 9 % on realistic, multi‑step workflows, but systematic improvements in semantic retrieval, sub‑agent orchestration and the Nova engine lifted overall accuracy to 76 %. Nova pre‑computes behavioral primitives such as sessionization and funnel steps, turning what would be expensive Snowflake queries into low‑cost, cached operations. In head‑to‑head tests, Snowflake’s compute costs were 2‑8× higher, and up to 65× when data preparation was lacking, underscoring the economic advantage of a purpose‑built analytics engine for AI agents.

The broader impact extends beyond Amplitude’s customers. By demonstrating that a rich, continuously‑grown semantic layer can compensate for model limitations, the company sets a template for AI‑augmented BI tools across the industry. Asynchronous specialized agents—like session‑replay monitors that surface friction points without human prompting—show how AI can shift analytics from reactive reporting to proactive insight generation. With cost‑effective scaling and a rigorous evaluation loop, enterprises can trust AI agents to handle high‑volume, complex product‑analytics workloads, accelerating decision‑making and freeing analysts to focus on strategic initiatives.