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Feature ownership
Members

Roadmap

What we're building

  • No-code A/B testing

    A visual editor for experiments would allow users to test changes to their website / app without having to touch the code.

    https://posthog.com/docs/experiments/no-code-web-experiments

    Project updates

    No updates yet. Engineers are currently hard at work, so check back soon!

Goals

Q2 2026 Objectives

This quarter we're making experiments work great with AI agents, removing feature flag friction, and improving the results experience.

AI

API layer Marcel Poelker

Get the API layer ready for AI agents. All experiment operations should be covered, well tested and work the same way whether the caller is our frontend or an agent.

Skills Marcel Poelker

Create experiment skills so PostHog AI knows how to use our MCP tools effectively.

MCP Rodrigo Iloro

External callers like PostHog Code or any other agent can connect via MCP and run experiment operations reliably.

Signals Juraj Majerik

Ship our first signal - notifying users when an experiment reaches significance.

Feature flags Anders Asheim Hennum

Make it possible to run multiple Experiments per Feature Flag. Rollout configurations will be more flexible and it will be easier to understand how Feature Flags and Experiments are related.

Query performance Juraj Majerik

Extend precomputation to the main subquery for all metric types. Make precomputation observable - see where it kicked in, how it ran and compare results against direct-scan queries.

Loading experience Rodrigo Iloro

Loading is painful for our high-volume users. Consolidate query fetching, only re-query when the user asks and set better expectations around loading states.

Handbook

What this team does

We build PostHog's experimentation platform, allowing users run A/B tests and feature experiments easily in their apps. We help teams validate ideas, measure impact, and make informed decisions quickly and confidently.

Some of the things we're working on:

  • Experiments UI – an interface for setting up, managing, and analyzing experiments in PostHog.
  • Experimentation API – backend services that handle experiment creation, user assignment, and result analysis.
  • Statistical analysis – implementing statistical methods to ensure accurate interpretation of results.
  • Documentation – writing documentation and tutorials to help users get the most out of Experiments.

User personas

Assessing customer fit

Use this guide to determine if a prospect's use case is a good fit for Experiments now, in the future, or at all.

Engineers (product, growth, software, etc.)

✅ Fully supported now

They get the most value since they can run experiments end-to-end, which allows them to test ideas quickly without coordination overhead.

Best fit: Teams where engineers can run experiments directly.

Product Managers

✅ Fully supported now

They use PostHog to decide what to test, monitor experiment results, and make data-driven decisions. While they typically need an engineer to implement the test, they can independently analyze results using our charts, statistical analysis, and AI summaries.

Best fit: PMs who work with engineers for implementation but want to independently analyze results.

Data Scientists/Analysts

⏳ Growing support → Full support planned (2026)

What works today: Frequentist and bayesian analysis, delta charts, timeseries, funnel breakdowns

Coming soon: Advanced statistical methods (CUPED, variance reduction techniques, multilayer experiments) - planned for 2026

Is this customer a fit?

  • ✅ Yes, if they need standard statistical analysis
  • ⏳ Wait or set expectations if they require advanced methods like CUPED (coming 2026)

Growth/Marketing teams

⏳ Limited support → Full support planned (further out)

What works today: Can run experiments on messaging, landing pages, campaigns - but require engineering help for implementation

Coming later: AI-powered visual editor will enable independent experiment setup without engineering (further out - requires mature code generation)

Is this customer a fit?

  • ✅ Yes, if they have dedicated engineering resources
  • ⏳ Wait if they need no-code/independent setup (AI editor coming but no firm timeline)

Designers

⏳ Limited support → Full support planned (further out)

What works today: Can implement simple experiments (copy changes, styling updates) independently; need engineering for complex components

Coming later: AI-powered visual editor will enable more complex implementations (further out - requires mature code generation)

Is this customer a fit?

  • ✅ Yes, if simple UI experiments or they have engineering support
  • ⏳ Wait if they need to independently implement complex UI changes (AI editor coming but no firm timeline)

Slack channel

#team-experiments

Feature ownership

You can find out more about the features we (and the other teams) own in the feature ownership handbook page

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