Agentic analytics: managed analytics with an AI operator at the wheel
An AI operator runs the pipeline and reads your product analytics, then turns what it finds into concrete fixes with the evidence attached. You approve; we implement. The autonomy is scoped, and it starts at zero.
What "self-driving" means here
Two jobs, one operator. On the pipeline side it monitors ingest, drain rates, quarantine and error spikes, and repairs operational problems such as a stalled component. On the product side it links each error to the session replay that threw it and to that user's full cross-session history, and drafts fixes and improvement proposals from that evidence. Every proposal cites the events, replays, and errors it came from.
How the loop runs
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1. Observe
Every event is validated against a registered schema at ingest, and bad data is quarantined instead of polluting what the operator reads. Errors, replays, and user history sit on one identity spine.
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2. Propose
The operator writes up what it found, why it matters, and the change it recommends, with the evidence linked. Proposals land where your team already works.
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3. Approve
A human approves. By default that is every action. You can opt specific narrow operations into self-approval once you trust them.
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4. Implement and verify
We implement the fix inside your existing systems or hand it to your team, then watch the same signals to confirm it worked. If a fix's own verification fails, it escalates to a human.
The guardrails
Autonomy is a dial, and it starts at zero. Anything you opt into self-approval runs fail-closed:
- Allowlists. Components and operations are named explicitly; an empty list means deny everything.
- Scoped credentials. The operator holds exactly the access those operations need, nothing wider.
- Circuit breakers. Cooldowns and rolling caps stop a misbehaving loop from acting repeatedly.
- Escalation. A fix that fails its own verification goes to a human instead of being retried.
- Audit trail. Every observation, proposal, approval, and action is recorded and reviewable.
Your agents, your data
The same platform exposes an MCP server and governed SQL access, so your own coding, support, or growth agents can ask questions of your product data directly. Personal data is redacted before anything reaches a model, and reads run under resource ceilings so an agent cannot starve the pipeline. If you are weighing this against a self-serve platform's AI features, the PostHog, Amplitude, Mixpanel and LogRocket comparisons put the approaches side by side, and the case study shows what the loop produced for one client.
Frequently asked questions
What does the AI operator do on its own?
By default, it observes and proposes. It watches ingest, drain rates, quarantine and error spikes on the pipeline side, and on the product side it links each error to the replay that threw it and to that user's history, then writes up a concrete fix with the evidence. A human approves before anything changes.
Can it make changes without asking?
Only for operations you opt in explicitly, and that mode is fail-closed: component allowlists where an empty list means deny everything, credentials scoped to exactly those operations, circuit breakers with cooldowns and rolling caps, escalation to a human when a fix's own verification fails, and a full audit trail of every action.
Can my own agents use the data?
Yes. The platform exposes an MCP server and governed SQL access, so your coding or support agents can query your product data directly. Personal data is redacted before anything reaches a model, and read access runs under resource ceilings so an agent cannot starve the pipeline.