Skip to main content

Glossary Term

Telemetry

By The Codegen Team · Updated March 26, 2026

The collection and analysis of performance data from AI agent runs, including cost, execution time, and success rate metrics.

In AI agent infrastructure, telemetry refers to the collection and analysis of performance data from agent runs. This includes metrics like execution time, token usage, cost per task, success rate, error frequency, and output quality scores.

Telemetry is essential for teams running agents in production because it enables optimization. Without visibility into what agents are doing, how much they cost, and where they fail, teams cannot improve agent performance or justify the investment.

Codegen provides built-in telemetry across agent runs, including cost tracking and performance analytics. Most standalone tools like Claude Code and Cursor do not offer comparable telemetry at the organizational level.

In plain English

Automatic data collection that shows how your software is actually performing in production — what is slow, what is failing, and how often.

Why it matters

When AI agents are producing and deploying code at scale, you cannot manually inspect every change. Telemetry is the feedback mechanism that tells you whether agent output is performing as expected, which task types generate the most post-merge issues, and where the cost-per-output ratio is worth paying. Without it, you are flying blind on whether the agents are actually helping.

In practice

A team runs 60 agent tasks over two weeks. Telemetry shows 51 produced clean PRs, 7 required significant human revision before merging, and 2 introduced regressions caught post-merge. Drilling into the 7 revision cases: all had ticket descriptions under 50 words. The team adds a ticket description template with required fields. The following two weeks: 57 clean PRs, 3 revisions, 0 regressions.

How Codegen uses Telemetry

Codegen's built-in analytics surface per-task cost, success rate by task type, time from assignment to PR, and patterns in which task descriptions produce better outcomes. This is useful for engineering leaders making the case for agent ROI and for teams diagnosing why certain task categories produce inconsistent output. It does not replace your application monitoring — it tracks agent performance specifically, not the behavior of the code those agents produce in production.

Frequently Asked Questions