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Writing code was the bottleneck. AI just moved it, into your CI pipeline and your infrastructure bill. Agent loops multiply build volume, and every iteration re-downloads artifacts that haven't changed. That's CI compute paying for idle downloads, WAN egress compounding on the same artifacts thousands of times over, and an Artifactory footprint scaled to absorb traffic that shouldn't exist. The fix isn't replacing JFrog Artifactory, there's no migration project here. Develocity Artifact Cache deploys beside your build agents and serves over 95% of artifacts locally, cutting egress and repository load by the same margin while Artifactory remains your system of record. And as agents multiply what moves through the pipeline, every artifact keeps its provenance origin, signature, lineage,so governance scales with volume instead of eroding under it. The business case runs on your own data: Develocity Analytics models your expected cache-hit rate from what your builds resolve today, then measures the actual rate after rollout. Governance and efficiency, at the pace AI ships. Read why Artifact Cache and JFrog Artifactory are better together → https://lnkd.in/gY5PQ9Bm

We gave the same AI model the same build-caching problems, twice. Working from the repository and build logs, it solved 4 of 40 runs. With Develocity's Build Scan data and encoded build-engineering expertise, it solved 36 of 40. Same model. Same projects. The only difference was what it could see. Models get better every few months. The build data behind your agent is what makes it reliable. Here's the full benchmark and how we ran it:

A 2025 study by Wang, Pradel and Liu looked at agent-written fixes that a benchmark had scored as correct. 7.8% of them failed tests the project's own developers had already written. The benchmark missed it. The existing regression suite caught it. As agents ship more of your changes, that gap grows. DORA's 2025 report found AI adoption improves throughput but still increases delivery instability. How much agent work a team can safely ship depends on what its regression tests catch. Why that won't change, even with smarter AI:

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Your ephemeral CI agents wake up, download 4GB of dependencies they had yesterday, run the build, and die. Then they do it again. Thousands of times a day. Artifact Cache breaks the loop. A Develocity Edge node sits in the same zone as your CI agents and serves 95%+ of dependencies at LAN speed over HTTP/2 — entire dependency trees streamed in multiplexed connections instead of per-artifact round trips. The part your team will actually care about: → Zero build-script changes. It's a standalone CLI running as CI pre- and post-steps. The build tool never knows. → Fine-grained storage — one artifact, one object. A changed dependency is one new object, not a re-packed multi-gigabyte blob. → CI-agnostic. Jenkins, GitHub Actions, GitLab, TeamCity, custom runners — one Edge backend serves all of them. → Upstream outage insulation. Maven Central throttling you? npm having a moment? The Edge keeps serving. → Every restore is visible in Build Scan what resolved, from where, how long it took. Adopt it centrally. No repo has to opt in. No build logic gets edited. It just gets faster. https://lnkd.in/guE_J2Qp

At large enterprises, 20–40% of total build time is spent re-downloading dependencies that haven't changed. That's not an engineering inefficiency. That's a line item: CI compute you're paying for while agents sit idle downloading, WAN egress charges for the same artifacts thousands of times over, repository infrastructure scaled to absorb redundant traffic, and engineering hours lost to builds that fail because an upstream repository had an outage you don't control. Artifact Cache eliminates most of it. Edge nodes co-located with your CI infrastructure serve over 95% of dependencies locally cutting WAN egress and upstream repository load by the same margin, and insulating delivery from third-party repository instability. Rollout risk is minimal by design: it deploys centrally as CI pre- and post-steps with zero changes to build scripts, so there's no team-by-team migration project. And you can project the ROI before committing. Develocity Analytics models your expected cache-hit rate from what your builds resolve today, then measures the actual rate per project after rollout. The business case isn't a vendor estimate; it's your own data. As AI-generated code drives CI volume up, this is the difference between compute costs scaling linearly with delivery or not. https://lnkd.in/guE_J2Qp