Experience Context

What is experience context?

Experience context is digital analytics data about how customers actually behave, structured so digital teams and AI agents can act on it. It captures what users did, where they struggled, and what that struggle costs, then delivers it to teams and AI coding agents so they know what to fix and optimize first.

Code, logs, and monitoring tools describe what a system did. They rarely show what a customer experienced. A checkout button can fail silently while every request returns a success code and every dashboard stays green. An AI coding agent can only work on problems it can see, so without experience context it optimizes what is visible in the codebase, not what matters to customers. Experience context supplies the missing layer: the behavior behind the problem, the number of customers affected, and the revenue at stake.

What are key aspects of experience context?

• Observed behavior: Sessions from web, mobile app, and kiosk, including clicks, taps, retries, and abandonment, captured automatically so questions can be answered later without pre-planned tagging.
• Technical and business signals: Errors, page performance, and revenue tied to the same session, so a friction point carries a cost.
• Customer voice: Survey responses and feedback linked to the session they came from, connecting what customers say to what happened.
• Impact quantification: Every issue sized by sessions affected and revenue at risk, so fixes are ranked by value.
• Agent-ready delivery: The affected component, the interaction that fails, and the affected sessions, delivered to AI coding agents through a standard interface such as the Model Context Protocol (MCP).
• Measured outcomes: Results checked after release against a control or matched cohort, so the team knows whether the change worked.

What are the benefits of experience context?

• Agents fix what matters: AI coding agents work from evidence of customer impact instead of guessing which changes are worth making.
• Faster path from problem to fix: The exact component, failing interaction, and affected sessions come with the request, which shortens root-cause work.
• Prioritization by value: Issues are ranked by revenue at risk rather than by who asked loudest.
• Proof of return: The same metric that sized the problem shows what the fix recovered.
• Better use of AI spend: Agents spend effort on changes tied to customer outcomes.

What are examples of experience context in practice?

• Silent checkout failure: On autofilled addresses, a "Continue" button does nothing, and customers tap it repeatedly. No error is thrown, so nothing downstream flags it. Experience context groups every affected session and hands the coding agent the component, the failing interaction, and the sessions, so it can fix the bug.
• Device-specific break after a release: Paid campaigns keep sending traffic to a page that fails on one device type. Experience context traces the failure to a release and shows how much spend is affected, so the site team and the media team can both act.
• Agent-traffic friction: Automated shopping agents stall at one step of a purchase flow. Experience context shows where they abandon and which change, such as a missing accessible label, lets them finish.

How does Quantum Metric support experience context?

Quantum Metric captures behavioral, technical, experience, and business data automatically, then uses Felix AI to find where customers struggle, quantify what it costs, and rank what to fix first. That evidence reaches AI agents through MCP, including coding tools such as Claude Code, Cursor, Codex, GitHub Copilot, Devin, and Factory. After the change ships, Quantum Metric measures whether it worked, against a matched control.