Experiment and analyze
Agent Visibility Analytics Insights agent
Agent Visibility Analytics Insights is an Optimizely Opal agent that analyzes an Agent Visibility Analytics dashboard in Optimizely Analytics. The agent surfaces trends, anomalies, and insights from your dashboard data and delivers a report.
Experiment Backlog Prioritization agent
Experiment Backlog Prioritization is an Optimizely Opal agent that scores each idea in your experimentation backlog using the Potential, Importance, Ease (PIE) framework and generates a prioritized report with recommended experiments and the reasoning behind every score. Challenge – Experimentation backlogs grow fast, but bandwidth is limited.
Experiment Conflict Checker agent
Experiment Conflict Checker is an Optimizely Opal agent that detects conflicts between a proposed experiment and any live experiments in the same project. Challenge – Running overlapping experiments on the same page corrupts your data, skews results, and leads to flawed conclusions.
Experiment Planning agent
Experiment Planning is an Optimizely Opal agent that transforms raw ideas into a fully formed experiment plan, complete with hypotheses, key metrics, risks, and critical assumptions. Challenge – A poorly planned test results in few learnings.
Experiment Program Health Review agent
Experiment Program Health Review is an Optimizely Opal agent that analyzes Optimizely Web Experimentation or Optimizely Feature Experimentation projects and routes to the correct interactive dashboard for each program type. Challenge – Experimentation programs generate large volumes of data.
Experiment Value Estimator agent
Experiment Value Estimator is an Optimizely Opal agent that generates a stakeholder-ready report with the projected annualized impact of rolling out a winning experiment variation. Challenge – Communicating the business impact of a winning experiment is difficult.
Experimentation Program Overview agent
Experimentation Program Overview is an Optimizely Opal agent that analyzes experimentation performance within a specified timeframe to generate a comprehensive overview of program velocity, win rate, and top-performing experiments. Challenge – Manually compiling a comprehensive overview of your Experimentation program, including win rates, top performers, and key learnings, is time-consuming and complex.
Feature Experimentation Governance agent
Feature Experimentation Governance is an Optimizely Opal agent that analyzes an Optimizely Feature Experimentation project and generates a read-only HTML governance report on a canvas. Challenge – Feature flags accumulate across environments, and teams lose visibility into which flags are live, stale, or abandoned.
Feature Flag Implementation agent
Feature Flag Implementation is an Optimizely Opal agent that generates a portable SKILL. md file to implement an Optimizely Feature Experimentation flag in a codebase.
Heatmap Analysis agent
Heatmap Analysis is an Optimizely Opal agent that transforms a provided heatmap image and URL into precise, tailored test ideas. Challenge – Creating high-quality test ideas is difficult.
Real-Time Audience Builder agent
Real-Time Audience Builder is an Optimizely Opal agent that creates, updates, and troubleshoots Optimizely Data Platform (ODP) real-time audiences . It answers expert questions about audience purpose, overlap, and Real-Time Segment (RTS) expressions.
Standard Audience Builder agent
Standard Audience Builder is an Optimizely Opal agent that builds and troubleshoots standard Optimizely Data Platform (ODP) audiences through natural language. Challenge – Standard ODP audiences require manual configuration and specialized schema knowledge.
Test Ideation agent
Test Ideation is an Optimizely Opal agent that generates a prioritized package of experiment ideas for any page. Challenge – Identifying high-impact experiments is time-consuming.
Variation Creation agent
Variation Creation is an Optimizely Opal agent that creates an experiment or personalization campaign and builds its variations. Challenge – Building experiment variations by hand requires manual work in the visual editor for every page change.