AI impact measurement
AI impact measurement is the practice of tracing AI activity, from coding-agent sessions to the AI applications you run in production, through to its effect on cost, engineering output, and the experience of your users. It answers the question every organization paying for AI eventually asks: is this spend creating value, and at what cost?
For coding agents, that means connecting usage and spend to engineering delivery and code quality. For the AI agents and applications you build, it means measuring task completion and customer outcomes alongside response quality, safety, latency, and cost.
Coralogix measures AI impact in AI Center, across the two places AI runs in your organization, so you can combine those signals with your engineering and product outcome data to assess value.
| Where AI runs | What impact means | Start here |
|---|---|---|
| Coding agents your developers use: Claude, Codex, GitHub Copilot, Cursor | Whether AI-assisted work improves engineering delivery and quality relative to its cost. Coralogix measures spend and tokens per model, user, session, and repository, and code impact: commits, pull requests, lines of code, and the share of AI suggestions developers accept. | Code Agents Intelligence |
| AI applications you build and ship: agents, chatbots, LLM-backed services | Whether they complete users' tasks, provide a good experience, and improve the product outcomes they were built for. Coralogix measures cost, latency, and token usage per application and model, output quality and safety through evaluations and guardrails, and every interaction, end to end, in AI Explorer. | Monitor AI applications |
Both sets of data run on the same telemetry pipeline as the rest of your observability data, so AI activity can be queried and correlated alongside the logs, metrics, and traces your applications already send.
The three levels of measurement
Measuring AI impact gets progressively harder, and each level needs the data from the level beneath it.
| Level | The question it answers | Where you measure it |
|---|---|---|
| Tokenomics | What are we spending on AI, and who is spending it? | Coding agents: the Cost tab in Code Agents Intelligence, for spend and tokens by model and user. AI applications: Optimize AI costs, for spend, model pricing overrides, and cost-pattern insights. |
| Code impact | What did that spend produce? | Coding agents only: the Impact tab on each agent dashboard, for commits, pull requests, lines of code, and acceptance rate. Repository breakdown attributes that output to the repositories each session touched, and splits company-owned work from everything else. |
| Production impact | Did the work hold up once it shipped? | Coding agents: compare your Service Catalog health and error data from before and after AI-assisted changes shipped. AI applications: trace individual interactions in AI Explorer, and assess output quality and safety with Evaluations and Guardrails. |
Measure the impact of your coding agents
Start with tokenomics: how developers use coding agents, and where spending goes across tools, models, users, and sessions. Then explore code impact through the commits, pull requests, and repositories associated with that activity.
Connect an agent, and Code Agents Intelligence starts attributing spend and output per developer, per session, and per repository:
- Connect one or more agents. Each has its own setup guide, linked from Code Agents Intelligence.
- Configure your GitHub Organizations in Settings, then AI Center, then Code agent, so sessions can be split into Managed and Unmanaged repositories.
- In Coralogix, navigate to AI Center, then Code Agents, and review the Activity, Cost, and Impact tabs for the period you care about.
- Relate that output to delivery outcomes, such as completed work, cycle and review time, and deployments. Speed and quality together give a fuller picture than tokens or code volume alone.
- Set up alerts on the patterns worth catching early, such as high token consumption with no commits.
Measure the impact of your AI applications
For the AI you build and run yourself, start with what the application is meant to achieve for its users and the business: task completion and outcomes such as customer satisfaction, successful resolutions, or conversion, alongside the cost of delivering them.
AI Center helps explain the performance behind those outcomes: monitoring shows usage, cost, latency, and errors; evaluations assess output quality; guardrails enforce policies in real time; and AI Explorer lets you investigate individual interactions.
- Instrument your application with OpenTelemetry. See Getting started with AI observability.
- Track cost, latency, and token usage per application in the Application Catalog and Optimize AI costs.
- Assess output quality with Evaluations, and enforce policies in real time with Guardrails.
- Trace any individual interaction in AI Explorer.
Bring these signals together with your product data to see where the AI works well and where it needs improvement.