Create product analytics agents that turn PostHog data into evidence-backed decisions and follow-up.
Example outcome
Convert PostHog analytics context into an investigation brief with evidence, hypotheses, and recommended next actions.
Agent examples
4 guides
Build a product analytics assistant that helps teams understand behavior changes and prioritize follow-up.
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Build a product analytics assistant that checks experiment setup before traffic or decisions depend on it.
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Build a product workflow that connects usage evidence with planned product work.
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Build a search quality workflow that compares retrieval behavior with real user interaction data.
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PostHog workflows in LatchLoop help agents investigate product questions with analytics, experiments, feature flags, replays, and error context when available. The agent can summarize what changed, which users or events are affected, and what action the product team should consider next.
A strong PostHog agent starts with a specific question: why did a metric move, how is a feature being used, whether an experiment is healthy, or which session replays explain a support report. It should separate measured evidence from hypotheses and recommend follow-up instrumentation when data is weak.
Use this overview to describe analytics investigation patterns while canonical capabilities remain in the catalog. PostHog pairs well with GitHub, Sentry, Linear, and customer-context tools when insights need to become code, bug triage, or product decisions.
Start with the product analytics investigation workflow below to build an agent that produces evidence-backed product handoffs.
Combine plugins
Outcome pages can describe combinations: one plugin for source context, another for project tracking, and another for delivery or notifications. Use PostHog as one layer in a larger agent workflow when the outcome needs more than one connected app.
Available plugin capabilities
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