I use AI coding tools every day. They have changed what one person can build and how quickly an idea can become working software. But the faster I could produce code, the more obvious another problem became: writing the change was no longer the slowest part.
The work after the code still moved at normal human speed. Someone had to understand what changed, check whether it matched the requirement, test the important journeys, collect UAT feedback, turn that feedback into a useful ticket, find an owner, verify the fix and decide whether the release was genuinely safe.
None of those steps disappeared because a coding agent finished a feature in twenty minutes. In some cases, they became harder because more work was now moving at the same time.
The bottleneck moved
This is not just a feeling from using the tools. In GitHub's 2024 enterprise survey, more than 97% of the 2,000 respondents said they had used AI coding tools at work at some point. The interesting part was not only adoption. GitHub concluded that individual usage was not enough and that companies needed to operationalise AI across the software development lifecycle.
DORA's 2025 research makes the same problem clearer. Its central finding is that AI acts as an amplifier. It strengthens good systems, but it also magnifies weak processes and fragile feedback loops. A separate DORA analysis found that a 25% increase in AI adoption was associated with a 1.5% decrease in delivery throughput and a 7.2% decrease in delivery stability. That is an association, not proof that AI directly caused the decline, but the explanation is believable: faster code can create larger batches that take longer to review and are harder to release safely.
AI did not remove the delivery system. It increased the amount of work that system has to absorb.
Trust has not caught up either. The 2025 Stack Overflow Developer Survey found that 46% of respondents distrusted the accuracy of AI tools, compared with 33% who trusted them. Only 3% said they highly trusted the output. That is why “the agent finished” cannot be the same thing as “the release is ready.”
What we are trying to build with Ciqora
Ciqora is intended to be a software delivery command center. It will sit above the tools a team already uses and keep the thread between requirement, story, code change, QA evidence, UAT feedback, defect, fix, retest and release decision.
The goal is not to produce another dashboard full of activity. Activity is easy to count. Pull requests opened, tickets moved and tests executed do not tell a delivery lead whether a client commitment is safe.
The useful questions are more direct:
- What is most likely to delay this release?
- Which client requirement is affected?
- Has the critical user journey actually been verified?
- Who owns the next action?
- What evidence supports the release decision?
Ciqora should answer those questions without forcing a manager to assemble the story from Jira, GitHub, Slack, CI logs, test reports and a spreadsheet of UAT comments.
What is different, and what is not
I do not want to pretend that every component of Ciqora is new. There are already strong products solving individual parts of this problem.
Jam captures a bug with screen context, logs and network requests. QA Wolf focuses on automated end-to-end testing. Jellyfish turns engineering data into productivity and operational insight. Jira, GitHub and Linear remain where teams plan and perform much of the work. These are real products solving real problems.
Ciqora's bet is that the missing unit is the release itself. Capture should not end with a recording. A test should not end with a pass or fail badge. A ticket should not lose the client requirement that created it. A release-risk score should be explainable through evidence, ownership and business impact.
Ciqora begins where generated code enters delivery.
The work can remain in the systems teams already trust.
Feedback stays connected until the fix is verified.
Every signal is tied to readiness, risk and the next action.
Why now
A few years ago, adding another control layer might have looked like unnecessary process. Now teams can run several coding agents, produce more changes in parallel and generate tests alongside the code. The cost of creating work has fallen. The cost of deciding what to trust has not.
This is why DORA's amplifier finding matters. Buying a better coding model will not repair unclear ownership, fragmented UAT, weak release evidence or slow feedback. It can expose those weaknesses faster.
The opportunity is not to slow teams down. It is to make the controls around delivery as fast and connected as the systems now producing the code.
What we are building first
We are starting at the point where context is usually weakest: QA and UAT. Ciqora Capture will let a tester report an issue inside the application with the relevant screen, technical context and business explanation attached. CiqoraQA will verify critical journeys and retest fixes. Ticket orchestration will keep ownership and release impact connected.
Those signals will feed the command center so a team can see what is blocked, why it matters and what has to happen before release. Over time, the thread can extend further back into requirements and further forward into production.
We are early. Some of this will change once real teams use it, and I would rather say that openly than write as if every product decision has already been proven.
The standard I care about is simple: if a delivery lead can open Ciqora and understand the real state of a release without chasing five people for updates, the product is doing something useful.