Introducing Confidence Agent

A product idea needs to become a clear plan, a measurable hypothesis, a feature flag, an experiment, and eventually a rollout decision. Along the way, teams move between documents, configuration screens, results, and discussions. At every step, they reconstruct context they already had.

Confidence Agent is an AI collaborator built into Confidence that works directly with your flags, experiments, metrics, and project documents. You describe what you want in natural language. The agent reads the relevant resources, takes action with your permissions, and explains what it did and why.

#How does an idea become a tested change?

Imagine your team has an idea for improving onboarding, but several questions remain. Who is the change for? What behavior should it improve? How will you measure success, and what risks should you monitor?

Confidence Agent can help shape the initial idea into a structured product brief. Through a focused conversation, it clarifies the audience, problem, hypothesis, scope, success criteria, and open questions. It can then help design the experience, including user flows, interactions, states, and edge cases, and create diagrams or wireframes to make the proposal concrete.

"Help me turn our onboarding idea into a product brief. Define the hypothesis and how we should measure whether it works."

When the team is ready to test the change, the agent can create and configure the feature flag and experiment. It defines variants, targeting, success metrics, guardrails (metrics you'd halt the rollout for if they regressed), audience allocation, and statistical settings. It can also calculate the required sample size and check whether the experiment is ready to launch.

"Create an A/B test for the new onboarding flag, using signup completion as the success metric and retention as a guardrail."

When you're ready to interpret results, the agent can explain the evidence in plain language. It considers effect estimates, confidence intervals, sample sizes, statistical power, and potential quality problems such as sample-ratio mismatch (an unexpected imbalance in how many users ended up in each variant, which can signal a data quality issue). It can compare variants and metrics, create follow-up analyses, and break results down by dimensions such as country, platform, or days since exposure.

"Should we ship the new onboarding flow? Explain the evidence, risks, and remaining uncertainty."

At Spotify, 42% of experiments are rolled back after guardrail metrics detect regressions. The platform is used to find the truth, not to confirm hypotheses. The agent supports that same discipline: when results contain uncertainty, quality concerns, or insufficient data, it says so.

The final decision remains with your team. The agent helps make that decision easier to understand and defend, then helps safely roll out the selected variant.

#What can the agent do?

The agent can act on the resources you manage in Confidence. It shapes product ideas into briefs, designs, hypotheses, and measurable success criteria. It creates and configures feature flags, defines targeting rules, and sets up A/B tests and gradual rollouts. It configures metrics, guardrails, audiences, and traffic allocation, and calculates sample-size requirements. It launches, ramps, ends, and archives experiments. It interprets results, creates exploratory analyses, and produces reports and project documents. And it can answer portfolio-level questions about your experimentation program or explain Confidence concepts and documentation.

That range covers the full lifecycle of a change, from shaping the initial idea to evaluating and rolling out a solution.

#Why does project context matter?

Confidence Projects give the agent a shared workspace. A project brings together relevant flags, experiments, documents, and surfaces, giving the agent the context it needs to produce grounded answers.

Attach the artifacts related to a product area and add the conventions or background your team wants the agent to consider. Instead of re-explaining the same context in every conversation, you continue from the work already collected in the project. The agent can also create and update durable briefs, reports, and designs, so its output becomes part of the team's shared record.

#What does the agent control, and what does it leave to you?

The agent inspects real resources before answering questions or making changes. Actions use the requesting user's existing Confidence permissions, and sensitive or high-impact operations require approval. You can see what the agent is doing and deny an action before it proceeds.

The agent also distinguishes evidence from judgment. When experiment results contain uncertainty, quality concerns, or insufficient data, it surfaces those limitations rather than presenting an unsupported conclusion.

#Try it

Open a project in Confidence and start with a concrete task:

• "Help me write a product brief for improving account activation."
• "Draw the user flow for the proposed onboarding experience."
• "Create a flag for the new signup flow with control and treatment variants."
• "How many users do we need to detect a 2% improvement in conversion?"
• "Which experiments are currently live on our onboarding surface?"
• "Summarize the results of our checkout experiment and recommend the next step."

#FAQ

Can the agent make changes to live experiments without my approval?
High-impact actions like launching, ending, or modifying a live experiment require explicit approval. You see the proposed action before it executes.

How does the agent handle uncertainty in results?
It surfaces it. If an experiment is underpowered, if there's a sample-ratio mismatch, if the confidence interval is too wide to support a clear decision, the agent tells you. It won't present an unsupported conclusion as a recommendation.

Can I use the agent for portfolio-level questions?
Yes. You can ask "which experiments are running on this surface?" or "how many experiments have we rolled back this quarter?" The agent reads across the experiments and documents in your project.

Does the agent replace our analysts?
No. It handles the configuration and translation work that slows teams down between steps. Complex analysis design, metric selection for novel product areas, and judgment calls about what to test still benefit from human expertise. The agent reduces the overhead so that expertise gets applied where it matters.

#Further reading

• A/B Testing Bandwidth: why experiment velocity is the binding constraint on innovation
• Experimental Evidence: maintaining evidence integrity through the full experiment lifecycle
• Collaboration in Confidence: how access control and organizational surfaces support team-level experimentation
• A/B Tests and Rollouts: the distinction between testing ideas and safely releasing changes