Engineering intelligence for AI-assisted teams

Coding faster
isn't
shipping faster.

Q: How does Agile Analytics collect data?

We combine: Engineering data — Git, CI/CD, Jira. Developer input — surveys and interviews. You get both the numbers and the context behind them — not just dashboards.

Q: What metrics do you use?

Proven frameworks like DORA, SLOs / Error Budgets, and SPACE — applied to the question of 2026: what does AI-assisted development do to the flow, and does quality hold?

Measure faster. Guard quality. Prove the gain.

Agile Analytics reads the tools you already run — Git, Jira, CI/CD, monitoring — and shows per team what AI-assisted development delivers: lead time, stability and quality. Not per developer. Per system.

Book a demo →

See the platform: what we measure, and from which tools →

New here? What Agile Analytics actually does →

Grounded inDORASPACESLOs · Error budgets

// the AA way · live

One loop. Three lenses. Real engineering productivity.

02 · What the research says

AI made coding fast. The rest of the loop didn't follow.

Three large studies from 2025 agree: AI multiplies output, and the pressure lands downstream — in review, in stability, in maintenance. Those are the parts of the loop Agile Analytics already measures.

Faros AI · 2025 · 1,255 teams

Output piles up at review

Teams with high AI adoption merged 98% more pull requests — and review time rose 91%. At company level, the gain did not show up at all.

Lead time and Sprint Insights show where in your loop the queue forms.

DORA · 2025 · ~5,000 respondents

Faster is not more stable

AI adoption goes with higher throughput and with higher delivery instability. 30% of developers report little or no trust in AI-generated code.

Change failure rate, MTTR and Error Budgets guard the line.

GitClear · 2025 · 211M lines

Maintenance grows quietly

Duplicated code rose from 8.3% to 12.3% of changed lines; refactoring fell from 25% to under 10%.

Sprint Insights splits feature work from maintenance, sprint by sprint.

Sources: Faros AI 2025 · DORA 2025 · GitClear 2025

Read the full picture: AI-assisted development, measured →

03 · Feeling faster is not measuring faster

19%

slower with AI, in a randomised trial of experienced developers

24%

speed-up the developers expected before the study

20%

speed-up they still believed they had got, afterwards

16 · 246

developers and real tasks in the METR trial, early 2025

METR itself warns against generalising: sixteen experienced open-source developers, early-2025 tools, repositories they knew deeply. We cite it for the gap, not the direction. Your team's answer may be the opposite — the only way to know is to measure your team. METR, July 2025

04 · The switch

Same AI. Now with visibility.

Flip the switch to see what changes when Agile Analytics is on. Same team, same tools, same copilots — the difference is what you can see.

Agile Analytics

click me

— OFF —Pull requests pile up at review, and nobody sees the queue growing

— OFF —Change failure rate creeps up while velocity looks great

— OFF —Maintenance quietly eats the sprint

05 · Engineering productivity

One platform. Three perspectives.
One continuous loop.

Most tools show DevOps metrics. Agile Analytics connects how work flows through your entire system — from intent to incident, around the loop — so AI-generated output is measured where it lands, not where it was typed.

All lensesAgileExDevExOpsEx

illustration · one sprint

// AgileEx

How work moves

The flow of value, end to end.

Lead time, review wait, deployment frequency
Where AI output waits between teams and steps

// DevEx

How work feels

The human side of velocity.

Interruptions, cognitive load, trust in the tools
Why engineers slow down — even when the copilot is fast

// OpsEx

How systems behave

The reliability of what you ship.

Change failure rate, MTTR, SLOs and Error Budgets
Where faster delivery turns into production risk

Eliminate delay — not just measure it.

Acceleration

Agile Analytics Acceleration

Quickscan & Flow Insights — find where time is lost, in hours and in teams.

Full Stack

Agile Analytics Full Stack

Metrics + Automation — connect Git, CI/CD, Jira → turn data into actionable flow insights.

Dev Support

Agile Analytics Developer Support

Interruptions & SLO visibility — make hidden work and support load measurable.

Dev Portal

Agile Analytics Developer Portal

Golden paths & onboarding — reduce friction before it becomes a delay.

06 · The demo

See where your delivery slows down — in your own data.

In your demo, you'll see:

✓ Where AI-generated work waits between teams and steps

✓ Which bottlenecks drive your lead time

✓ Whether change failure rate and Error Budgets are holding

✓ How much of the sprint is maintenance rather than features

Book a demo →

[illustration · not customer data]

“

We thought our bottleneck was deployment speed. It turned out to be handovers.

Jeroen Bultje Maxeda

08 · FAQ

Frequently asked questions.

Who is Agile Analytics for?

For engineering and platform leaders whose teams build with AI and who want to know what it delivers — measured in lead time, stability and quality, not in impressions.

How does Agile Analytics collect data?

We combine:

Engineering data — Git, CI/CD, Jira
Developer input — surveys and interviews

You get both the numbers and the context behind them — not just dashboards.

What metrics do you use?

Proven frameworks like DORA, SLOs / Error Budgets, and SPACE — applied to the question of 2026: what does AI-assisted development do to the flow, and does quality hold?

How is this different from DevOps monitoring tools?

Monitoring tells you what broke. Agile Analytics shows you where time is lost before things break — and how to fix it.

Does Agile Analytics track AI spend, or label AI-generated code?

Not yet. Today we measure what AI-assisted development does to your loop — review wait, lead time, change failure rate, Error Budgets, the feature-to-maintenance ratio — from the tools you already run. Cost per team and AI-authorship detection are on the roadmap, and we will not put a number for them on this page before the product measures it.