How does Daxtack analyze CI/CD failures?

Daxtack captures your build output — logs, errors, and stack traces — from your existing CI tools. The on-premise agent sanitizes logs locally, removes secrets and PII, then sends only the relevant failure context to our AI layer. The result is a root-cause summary and suggested fix your team can review.

Does Daxtack work with Jenkins?

Yes. Daxtack fully supports Jenkins via the Enterprise Agent. The agent runs inside your network, captures Jenkins build logs, sanitizes them on-prem, and delivers AI-generated fix suggestions without any code changes to your Jenkins setup.

Is my source code safe with Daxtack?

Your source code stays inside your network. The Daxtack agent runs on-premise, strips secrets and PII locally, and sends only sanitized failure context for analysis. Zero inbound ports are required.

What CI/CD platforms does Daxtack support?

Daxtack supports GitHub Actions, GitLab CI, Jenkins, CircleCI, Azure DevOps, Bamboo, TeamCity, Travis CI, and any CI/CD platform with webhook support.

Does Daxtack have a free plan?

Yes. The free plan includes 30 analyses per month, support for all CI platforms, basic error detection, and shareable results. No credit card required to start.

How is Daxtack different from asking ChatGPT about build errors?

Daxtack is purpose-built for CI/CD failures. Unlike copy-pasting logs into a generic AI chat, Daxtack automatically captures build context, sanitizes secrets first, applies multi-model analysis, and returns a ranked diagnosis and suggested fix in a workflow your team can review.

What is the Daxtack Enterprise Agent?

The Daxtack Enterprise Agent is a Docker-based on-premise component that runs inside your network. It integrates with legacy CI systems like Jenkins and Bamboo, sanitizes logs locally before any data leaves, and requires zero inbound ports. It is designed for teams that need private deployment and stricter security controls.

Why Rerunning Your Pipeline "Fixes" It (And Why That Should Scare You)

Your CI pipeline fails. You click "Re-run." It passes. Problem solved?

No. You just swept a non-deterministic failure under the rug. And if this happens regularly — if your team treats re-running as the fix — you're accumulating invisible technical debt that will cost you far more than the 5 minutes you saved by not investigating.

The Uncomfortable Truth

"Just rerun it" means your pipeline has non-determinism somewhere. That non-determinism has a source, and that source will keep producing failures. The only question is whether it's a small annoyance now or a major problem later.

Here's what a successful rerun is actually telling you:

Resource limits were hit — your job is close to the memory or disk limit. It fails when resource contention is high, passes when it's low. As your codebase grows, it will fail more often
A race condition exists — in your tests, in your build process, or in your deployment. Race conditions get worse under load, not better
An external dependency is unreliable — a package registry, a staging API, a cloud service. You're one outage from blocking every PR on your team
Your cache is doing something wrong — the first run after a cache miss behaves differently from subsequent runs. This is a ticking time bomb

None of these problems go away on their own. All of them get worse over time.

What It Actually Costs

Let's do the math on a team of 10 engineers:

Average re-run rate: 15% of CI runs need at least one rerun
Average PRs per engineer per day: 2
That's 20 CI runs per day, 3 of which need re-runs
Each re-run takes 8 minutes (pipeline time) + 2 minutes (context switch) = 10 minutes
3 re-runs × 10 minutes = 30 minutes per day, 2.5 hours per week, 130 hours per year

At $100/hour fully-loaded engineer cost, that's $13,000/year in re-runs alone. And that doesn't count:

The time other engineers wait for the re-run to pass before their PR can merge
The merge conflicts that accumulate while PRs wait in the queue
The genuine bugs that get missed because "eh, it'll pass on rerun"

The Team-Culture Problem

The bigger cost is cultural, not financial. When re-running becomes normal:

1. Investigation Dies

Nobody looks at failures anymore. Why spend 10 minutes investigating when you can re-run in 2 clicks? But that 10 minutes of investigation would fix the root cause once, permanently. Instead, you pay the re-run tax forever.

2. Signal Gets Lost in Noise

When 15% of builds fail randomly, engineers stop treating failures as signal. A real regression gets merged because "that test is always flaky, just re-run." This is how bugs reach production.

3. New Engineers Learn the Wrong Habits

A new engineer joins your team. Their first CI failure, they ask: "What does this error mean?" Someone replies: "Oh, just re-run it." That engineer will never develop the debugging instinct. They'll re-run for the rest of their tenure.

What to Actually Do Instead

Track Rerun Rate as a Metric

You can't fix what you don't measure. Start tracking:

Overall rerun rate — what percentage of CI runs are re-runs? Healthy: under 5%. Concerning: 10-15%. Urgent: above 20%
Per-job rerun rate — which specific job gets rerun most often? This identifies your worst offenders
Time trend — is the rerun rate going up or down? An upward trend means the problem is compounding

# Quick and dirty: count re-runs in GitHub Actions
# via the GitHub API
gh run list --limit 100 --json conclusion,attempt | \
  jq '[.[] | select(.attempt > 1)] | length'

Treat Repeated Reruns as a P2 Bug

If the same job has been re-run more than 3 times in a week, create a ticket. Assign it. Set a deadline. Treat it the same way you'd treat a production bug that happens 3 times — because it's costing you just as much in developer time.

Enforce "Investigate First" Culture

Simple team rule: before clicking "Re-run," spend 2 minutes looking at the failure. Just 2 minutes. Read the error message. Check if you've seen it before. If it's a known flaky issue, note it and re-run. If it's new, investigate.

2 minutes of investigation × 3 daily re-runs = 6 minutes per day. That's nothing. And it will catch genuine regressions that would otherwise get re-run past.

Add Automated Failure Classification

The fastest way to decide "investigate or re-run" is to have failures automatically classified:

Is this a known flaky test? → Re-run is acceptable, but track the flake for future fixing
Is this a new error we've never seen? → Investigate, don't re-run
Is this an infrastructure issue (network, OOM)? → Re-run, but track the infra issue

If you're not already tracking which jobs get rerun most often, that's the single highest-signal metric for pipeline health. It's also exactly the kind of pattern Daxtack surfaces automatically across a project's log history instead of you noticing it anecdotally six months later.