Codex · AI Impact

Measure Codex impact beyond usage.

Connect Codex and Codex Enterprise with engineering context in Oobeya. Monitor AI costs and evaluate connected activity alongside team performance, review time, quality, rework, and delivery.

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Codex

Integration available

AI activity · AI Cost · Team outcomes

Codex engineering impact in context

AI Impact Overview

How AI-assisted development relates to engineering outcomes

Coding Efficiency67.7%↑ 7.3%

Higher AI adoption coincided with improved coding efficiency.

Coding EfficiencyExplore the trend

Security: A
Maintainability: A
Reliability: C
Technical debt: 86d

Performance Overview

AI attribution alongside delivery and usage

AI share of new code61%↑ +3.7%

75,403 of 123,666 added lines are AI-attributed.

AI impact on deliveryExplore the trend

Delivery velocity: 158commits/week
↓ −2%prev. 161.3
Time per commitderived: 36.2min
↓ −53.3%prev. 77.6 min
Reclaimed capacityderived: 1,401hours
2,031 commits × 41.4 min saved
Developers using AIderived: 17developers
↑ +54.5%65.4% of 26 developers

Coding time split: 219.4h AI40.8h Human
Avg. lines / AI commit: 114.4 AI83.5 Human
Token spend: 905.5M
Cache efficiency: 99.3%

AI Cost & Credits

Credit consumption, forecasts, and financial summary

AI Credits Overview

Credit overview

July 2026

Remaining credits −3,204.3↓ 99.8%

Credit pool usage 100.2% ↓ 44.3%

0% remaining

AI credit pool value: $18,772.00
Total AI credits used: 1,880,404.3
Avg. daily burn rate: 58,762.6
Total users: 988

Forecasted month-end usage: 1,880,404.3 ↓ 44.3%
Forecasted overage: 3,204.3 ↓ 99.8%

Team Performance Overview

Engineering performance at a glance

Team Member Insights

New Work
Refactor
Help Others
Churn

Member impact ratio, coding efficiency, AI code share, and work distribution as percentages of New Work, Refactor, Help Others, and Churn.
Member	Impact Ratio	Coding Efficiency	AI Code Share	New Work	Refactor	Help Others	Churn
Maya PatelSenior	2.4x	94%	36%	52%	25%	17%	6%
Alex MorganMid	1.6x	92%	48%	38%	34%	20%	8%
Sam TaylorMid	0.8x	80%	76%	47%	18%	15%	20%
Jordan LeeJunior	0.6x	72%	84%	20%	30%	22%	28%

Coding Efficiency95.01%↑ 1.76% more than avg

Coding efficiency recovered to 95.01%, above the company average.

Coding EfficiencyExplore the trend

Active symptoms: 10
Critical symptoms: 2 need attention
Symptom monitoring: 10 active
Most affected area: Code Review (3)

Explore AI activity, contribution, costs, and team performance in Oobeya. Available financial measures depend on the connected source.

From signals to decisions

Evaluate what changes with Codex

Use connected assistant signals alongside the work, quality, and delivery evidence your team already reviews.

Track Codex AI costs

Monitor Codex spending in AI Cost. Review costs alongside adoption, review time, quality, and delivery to evaluate your AI investment.

AI Usage & Cost Monitoring

Understand adoption and enterprise rollout

Connect Codex activity with team and repository context. Compare consistent groups and time periods when reviewing adoption and engineering trends.

AI Impact

Read delivery with review and rework

Compare pull request time, coding efficiency, churn, and quality. Investigate the changes behind a trend before deciding what to improve.

Team Performance

Add code-origin context with Blamely

Use Blamely for supported Codex coding workflows to capture attribution locally and retain it with Git commits. Connect that provenance to engineering outcomes in Oobeya.

Explore AI code attribution

What to look at

Connect the integration. Investigate the result.

Start with the available source data and build a consistent engineering evaluation.

Configured integration and source coverage
Team and repository mapping
Consistent reporting periods
Review, quality, and delivery context

Connect Codex: Configure the Oobeya integration for your environment. Review the available data, access requirements, and collection scope during setup.
Align workspace and engineering scope: Identify the teams, repositories, and reporting periods to evaluate. Keep the scope consistent when comparing assistant activity with delivery and quality.
Review outcomes with the team: Investigate changes in review time, coding efficiency, rework, and quality. Use the findings to guide enablement and follow-up improvements.

Explore measurement by coding assistant

Common questions

Codex integration questions

Need to discuss your team or technical setup? Contact us.

Can we monitor Codex costs in Oobeya?

Yes. AI Cost supports Codex as well as GitHub Copilot. Review Codex spending alongside usage and engineering outcomes. Available financial measures depend on the connected source and reporting scope.

Does Oobeya integrate with Codex?

Yes. The Codex integration is available in Oobeya. Connect the integration and review the configured data scope to evaluate assistant activity alongside engineering outcomes.

What should we evaluate after connecting?

Review assistant activity with team performance, pull request flow, coding efficiency, churn, quality, and delivery trends. Use consistent teams and time periods, then inspect the work behind any change.

Is integration activity the same as AI code attribution?

Integration activity and code-origin attribution answer different questions. Blamely can capture supported Codex edits locally and attach attribution to Git commits. Use that additional provenance when investigating AI-written changes.

Does this integration support Codex Enterprise?

Yes. Codex Enterprise is included in the Codex integration. Review your workspace, access requirements, available signals, and team mapping during configuration.

Can Oobeya run on our own infrastructure?

Oobeya supports cloud, on-premise, and air-gapped deployment options. Review the connectivity required by your Codex integration for the proposed environment. Blamely local attribution and Codex service access are separate parts of the setup.

Does more Codex usage prove a productivity improvement?

Higher usage alone does not establish an improvement. Compare it with review effort, rework, quality, and delivery, and account for changes in the team and task mix. Correlation does not prove causation.

See Oobeya in practice

Evaluate Codex with your engineering questions

Bring your setup, team structure, and the outcomes you need to understand. We’ll focus the walkthrough on your evaluation.

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