By Arjun Mehta, Principal Engineer at Glue
The DORA research program has spent years studying what separates elite engineering teams from average ones. The findings are consistent across thousands of organizations: elite teams do not just work faster — they learn faster. Their engineering feedback loops are shorter, tighter, and — crucially — they actually close. Average teams work hard and produce output. Elite teams work hard, measure outcomes, and adjust course before problems compound.
The most consequential feedback loops are not the fast ones everyone discusses (CI/CD, automated testing). They are the slow strategic ones that most teams have never closed: the loop between detecting a codebase problem and verifying it has been resolved.
What Most Teams Get Right — And What They Miss
Modern engineering teams have excellent fast feedback loops. A developer writes code and gets compiler feedback in milliseconds. A PR is submitted and CI runs in minutes. A deployment goes out and monitoring alerts within seconds if something breaks. These immediate loops are well-understood and well-tooled.
Where the loops break down is at the strategic level: the feedback loop between "we identified a systematic problem in our codebase" and "that problem is gone." This loop operates over weeks and months, not seconds, which makes it easy to ignore. It also requires connecting two systems that almost never talk to each other — the codebase analysis tools that detect problems, and the work management tools that track remediation.
According to the Standish Group, 66% of software projects experience cost overruns. A primary driver is rework — doing the same work multiple times because there was no feedback mechanism confirming the first attempt worked. Engineers spend 23–42% of their time on technical debt (Jellyfish, 2025), and a significant fraction of that time is spent re-fixing things that were supposedly fixed before.
The Anatomy of a Closed Engineering Feedback Loop
A complete engineering feedback loop has four components, and all four must be present for the loop to actually close.
Signal. A specific, measurable indicator from the codebase. "The auth service has 31% test coverage" is a signal. "The auth service has tech debt" is not. Good signals are precise enough to be re-measured after work is done.
Action. Work done in response to the signal. The action must be connected to the original signal — an engineer who knows the specific coverage target can scope their work accordingly. An engineer who received a vague "fix auth debt" ticket cannot.
Measurement. After the action is complete, re-measure the original signal. Did coverage move? Is the metric above the threshold? This step is where most teams' feedback loops break — the original signal is not re-measured after work is done.
Adjustment. Based on the measurement, either confirm the loop is closed (signal resolved) or create a new work item if the action was insufficient. This is what makes the loop adaptive rather than linear.
Why the Measurement Step Gets Skipped
The measurement step gets skipped for understandable reasons. It requires someone to go back to the codebase analysis tool, re-run the specific analysis, and manually compare the new results to the original detection signal. This is time-consuming, not automated, and requires the original signal to have been documented specifically enough to be re-measurable — which most tickets are not.
The structural problem is that codebase analysis and work management are separate systems with no native connection. The detection happens in one tool; the work tracking happens in another; the verification would require manually bridging them. Most teams find it easier to just run the next quarterly health report and see if things improved — a four-week feedback delay on work that took one sprint.
Elite teams close this gap by being deliberate about signal definition and measurement. They write tickets that include the specific metric being targeted. They treat "the codebase metric changed" as the definition of done, not "the ticket closed."
Building the Feedback Loop Into Your Engineering Process
You do not need to automate everything to start building better feedback loops. The manual version works — it is just not scalable. Start here:
For every technical debt ticket, add two fields: Current Value and Target Value. For example: "Test coverage: Current 31%, Target 70%." After the sprint, re-run coverage for those specific files and update the ticket with the Final Value. If Final Value meets or exceeds Target Value, the loop is closed. If not, a follow-up ticket is created.
This is simple and powerful because it makes the feedback loop visible. Engineers know what they are measuring. Managers can see whether work is moving metrics. The retro becomes data-driven rather than retrospective narrative.
The automated version — where a platform like Glue reads your codebase before and after each sprint and systematically verifies which detected problems were resolved — is more scalable. It handles full verification across your entire codebase, not just the handful of tickets your team is manually tracking. This is what closed-loop engineering intelligence does.
What Changes When Your Feedback Loop Actually Closes
Teams that implement systematic verification consistently report the same shift: engineering conversations become more precise. Instead of "we have been working on tech debt for two quarters," the conversation becomes "we resolved 23 of 31 flagged issues this quarter, with an average resolution rate of 74% per sprint, and here are the 8 that partially resolved."
That specificity changes how leadership views engineering investment. It changes how engineers prioritize and scope work. It changes how sprint retros are structured. And over time, it changes the codebase — because when work is connected to verified outcomes, work tends to actually produce those outcomes.
The teams DORA identifies as elite are not elite because they have better developers. They are elite because their feedback loops close. See how Glue connects sprint work to verified outcomes in Connect Sprint Work Back to the Intelligence That Flagged It.
FAQ
How does an engineering feedback loop relate to DORA metrics?
DORA metrics measure the speed and reliability of your delivery feedback loop — how fast code moves from commit to production and how quickly you recover from failures. But DORA metrics do not measure whether your codebase health is improving over time. A team can have elite DORA metrics while technical debt accumulates unchecked. Codebase health feedback loops are the missing layer.
What is the fastest way to start closing my engineering feedback loop?
Pick one category of technical debt — test coverage, for example — and add Current Value and Target Value fields to every ticket in that category. After each sprint, re-measure the specific files touched and record the Final Value. Do this manually for one quarter. You will quickly see which tickets are closing the loop and which are generating activity without outcomes.
Can a feedback loop help with bug recurrence?
Yes. Many recurring bugs exist because the root cause — a fragile architectural pattern, a module with no test coverage, a poorly understood dependency — was never addressed when the bug was fixed. A feedback loop that connects bug fixes to the underlying codebase conditions that enabled the bug would surface these root causes and prevent recurrence.