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From AI Context to AI Agents: How to Make Amplitude’s AI Actually Work
Amplitude’s AI is moving fast. It’s no longer just a chat box that answers questions, it’s starting to behave like a set of “workers” that can summarize dashboards, generate insights on a schedule, and help teams move from analysis to action. But there’s a catch: most teams will get disappointing results if they treat AI as a switch you turn on.
In our recent webinar, Gregor Spielmann (Co-Founder, Adasight) and Olivia Natasha (Growth Consultant, Adasight) walked through what’s practical today: AI Context, Chat, and AI Agents, plus the patterns we’re seeing across growth and analytics teams working with Amplitude.
If you want the full walkthrough, watch the webinar replay on YouTube at the bottom of the page.
Why AI in analytics often disappoints
Most “AI in analytics” fails for predictable reasons:
- Teams ask vague questions and get vague answers back.
- AI tries to infer business context it doesn’t have (and gets it wrong).
- Dashboards aren’t built around decisions, so the AI can’t be either.
- AI outputs sound confident, but the logic behind them isn’t always trustworthy.
The takeaway: AI doesn’t replace analytics thinking. It amplifies whatever system you already have—for better or worse.
The shift we’re seeing inside Amplitude
Amplitude started as product analytics, expanded into broader digital analytics and qualitative signals (replays, heatmaps, feedback), and now it’s clearly leaning into an AI-first future.
Practically, the platform now clusters “AI” into a few core modes:
- Conversational analytics (Chat)
Analytics becomes more question-driven: ask for a chart, ask why a spike happened, ask for a breakdown, ask for next steps. - Agents (automated analysis)
Agents run in the background and produce recurring summaries, insights, and suggested follow-ups—often tied to dashboards, replays, or feedback. - More inputs as context (not only quantitative data)
AI becomes much more useful when it can incorporate qualitative inputs like replays, support tickets, survey feedback, docs, OKRs, and “how we measure success.”
1) AI Context: the foundation (and why it’s underrated)
If you remember one thing from this article, make it this:
AI Context is the feature that prevents generic answers.
In Amplitude, AI Context can be defined at:
- Organizational level (shared context across projects)
- Project level (context specific to a product, site, or team)
What AI Context should include (simple version):
- Your business model and what “success” means
- Your main users and lifecycle stages
- Key conversions and important behaviors
- Your team goals (OKRs), decision priorities, and constraints
- Any internal language, acronyms, or definitions AI should understand
Gregor’s practical observation: Amplitude picks up AI Context surprisingly quickly, meaning the value shows up fast once the context is in place.
Pro tip: AI Context is not only text. You can attach files too (docs, summaries, references). Even if the naming changes over time, the principle remains: better context = better AI reasoning.
2) Chat in Amplitude: where conversational analytics actually helps
Amplitude’s chat experience has gone through multiple names, but the usefulness comes down to a few high-value patterns:
Use chat to explain charts (fast)
When you see a spike or drop, chat can:
- summarize what changed,
- suggest dimensions to check,
- and propose follow-up questions.
Use chat to create analysis (when you know the intent)
If you know the question (e.g., “What is our add-to-cart → purchase conversion over time?”), chat can build the first version of the chart for you, and you iterate from there.
Use threads to collaborate
Chat conversations can become “analysis threads” you share with teammates. You can also ask chat to create a clean summary for sharing.
3) AI Agents: where most teams get it wrong (and how to get value)
Agents are the most exciting shift—because they move AI from “on demand answers” to “recurring decision support.”
But they’re also where setups fail most often.
What agents are good at
Agents are best as:
- a summary + pattern surfacing layer, and
- a reasoning partner that suggests next questions
They’re not magic. They won’t fix broken dashboards or missing event structure. They’ll amplify those problems.
How to use a Dashboard Agent effectively (live example pattern)
Olivia’s demo followed a simple workflow:
Step 1 — Build a dashboard that has a job
Dashboards should be designed around either:
- Team-specific needs (onboarding for product, activation for lifecycle, etc.), or
- KPI-specific monitoring (acquisition, conversion funnels, retention, monetization)
If the dashboard doesn’t already answer a clear question, the agent won’t either.
Step 2 — Add decision context to the agent
The agent needs a “brief,” like a senior analyst would:
- What’s the business goal of this dashboard?
- Who is it for?
- What decisions should it support?
- What should it not focus on?
Step 3 — Let the agent run, then interrogate it
The best value comes after the summary:
- ask what changed week-over-week,
- compare segments,
- test hypotheses,
- and use it to guide deeper analysis.
Step 4 — Share, monitor, notify
Agents are powerful when they reduce manual monitoring:
- send weekly Slack/email updates,
- collaborate on insights,
- and keep stakeholders informed without “staring at dashboards.”
Trust & validation: the honest caveat
A key Q&A question was: “How trustworthy is AI analysis?”
Our grounded answer:
- The raw data layer is usually reliable (because you can trace charts and events).
- The interpretation can be wrong (false correlations, invented narratives, connecting dots incorrectly).
A real example from our experience: the AI referenced an “experiment winner” that never actually happened.
So the right mental model is:
- AI is useful for summarizing, pattern surfacing, and speed.
- Humans still own judgment and validation.
If your team treats AI as an oracle, you’ll lose trust quickly. If you treat it as an assistant that accelerates thinking, it’s valuable.
A simple AI-readiness checklist for Amplitude
If you want AI Context + AI Agents to work, check these basics first:
- Is your tracking structured enough to answer real questions?
Events, properties, naming consistency, governance. - Do your dashboards have decision intent?
Not “everything we can track”—but “what decisions this supports.” - Do you have AI Context defined (org + project where needed)?
Business model, users, conversions, goals, definitions. - Are prompts and agents documented for reuse?
Across teams and clients—avoid one-off magic. - Do you validate AI outputs and back-trace logic?
Treat it like a junior analyst: helpful, not infallible.
What to do next (if you’re adopting Amplitude AI now)
If you’re experimenting with Amplitude AI today, don’t try to “run” immediately.
Use a crawl → walk → run approach:
- Crawl: Chat for daily analysis + simple questions, basic context.
- Walk: Dashboard agents for weekly reporting + focused areas.
- Run: Multi-tool workflows (Slack, MCP-style integrations, broader automation) once foundations are strong.
Watch the webinar replay
🎥 From AI Context to AI Agents: Making Amplitude’s AI Actually Work
Want help making your Amplitude setup AI-ready?
At Adasight, we work with growth teams to make sure AI doesn’t just generate answers, it supports real decisions.
If you want a structured, hands-on assessment and setup support, explore our AI Readiness offering here:
https://ai-readiness-webinars.adasight.com/?utm_source=blog&utm_medium=post&utm_campaign=ai_context_to_agents
