If the last time you evaluated your CI/CD platform was before agentic coding tools became standard, it's time to revisit, and with different criteria than before.
According to our State of Code Abundance 2026 report, which surveyed more than 200 enterprise technology leaders, AI now generates or assists in writing 61% of the average enterprise codebase.
Producing large volumes of code quickly has become easier, but getting that code to work reliably in production hasn’t. Over 80% of organizations have already seen production issues increase linked to agentic coding. So, it’s no surprise that more engineering leaders now say their primary bottleneck is reviewing, testing, and deploying code (57%), not writing it (35%). The bottleneck has shifted from generating code to governing what reaches production.
If your organization is all in on agentic coding and you're evaluating enterprise CI/CD platforms to support it, here's what to look for.
What still matters in enterprise CI/CD
The fundamentals of enterprise CI/CD haven't changed. Any platform you evaluate today should still deliver:
- Pipeline reliability and scalability: Enterprise engineering organizations run hundreds, sometimes thousands, of pipelines. Your CI/CD platform must consistently handle that volume.
- Strong integrations: Your CI/CD platform needs to connect reliably with your source control, security scanners, artifact repositories, and deployment tools.
- Compliance and audit capabilities: Role-based access controls, audit trails, and compliance evidence generation are non-negotiable in regulated industries.
- Developer experience: Fast feedback loops, clear failure reporting, and integrations with the tools developers already use determine whether a platform gets adopted or worked around.
These criteria matter more than ever when AI tools are continuously pushing more code through your pipelines. Every gap in integrations, compliance evidence, and developer feedback loops produces more issues as code volume increases.
New evaluation needs for the agentic coding era
As AI takes on more of the writing, the pressure on your CI/CD infrastructure grows. Engineering leaders are being asked to ship faster as AI generates more code. But faster isn't translating to more reliable. Costs are rising, and production issues are increasing. According to our report, 54% of organizations have seen a significant increase in CI/CD infrastructure spend as AI-generated code volume grows, and 70% say maintaining their test suite is now a bigger burden than writing code itself.
The volume is outpacing traditional code review processes: 93% say they have a formal process for reviewing and releasing AI-generated code into production, but only 56% say those processes are always enforced. For VPs of Engineering, that enforcement gap has a direct consequence: 46% say they are personally accountable when AI-generated code causes a production failure. Closing those gaps requires two capabilities that traditional CI/CD evaluation criteria don't account for.
Cross-toolchain governance
AI-generated code volume exposes a gap that most enterprises already have but haven't had to confront at scale: no single tool governs what happens across all of them.
Enterprise software delivery rarely runs through just one CI/CD system. As organizations scale, acquire teams, or adopt new technologies, they accumulate pipelines across multiple tools. Each one governs only its own slice of delivery, and nothing tells you what's happening across all of them together. Every major CI/CD platform now offers delivery governance features, but those features govern only what happens inside that platform. If your delivery runs through Jenkins, GitHub Actions, and GitLab, one platform's governance features plus two blind spots is not governance.
Answering the question "Is this safe to ship?" means checking each tool separately; pulling pipeline status from one system, security findings from another, and release approvals from a third.
At low code volume, that's manageable. At AI-generated code volume, things get missed. Not necessarily because the tools failed; sometimes it’s simply because nobody had the bandwidth to check everything before the release went out.
To manage cross-toolchain governance in the AI era, you need a software delivery control plane, a layer that sits above the tools rather than inside any one of them. It connects the CI tools, security scanners, source control systems, and deploy tools already in your stack, without replacing any of them, reconciles their signals, and enforces shared policy regardless of which pipeline the code ran through.
A delivery record that reaches production
A traditional CI/CD pipeline shows you what code was shipped. It doesn't show you what happened next: whether that deployment is behaving correctly in production, which release introduced a bug, or whether a rollback actually took effect. That gap matters for day-to-day operations, incident response, and audit readiness alike.
At lower code volumes, teams could track that picture manually. At the volume and speed of AI-generated code, maintaining it becomes much harder.
Without a delivery record that extends into production, debugging means guessing which release is responsible. Audits mean assembling evidence after the fact. And when an incident hits, there's no single source of truth for what's actually running in production.
A delivery record that reaches production fills that gap. Evidence is generated as you ship, not assembled after the fact when an auditor calls or an incident hits. What shipped, who approved it, what's live right now, all captured in real time. Every change, human or AI, stays visible, auditable, and accountable after it ships.
Find a platform built for old and new demands
The fundamentals of enterprise CI/CD still matter, but the code abundance era has added new requirements on top of them. For platform engineering teams managing a multi-tool stack day to day, governance that works across your entire toolchain and a delivery record that doesn't stop at deployment are now just as critical as pipeline reliability or compliance capabilities.
CloudBees Unify is built for this stack shape. It treats Jenkins and the other CI/CD tools and security scanners in your delivery chain as first-class citizens, and it governs them from above. Policy is defined centrally and enforced consistently across every tool, and every change is tracked from commit to production, whether a human or an AI agent wrote it.
Its AI-powered capabilities help your team investigate failures across pipelines, flag vulnerabilities, select the right tests, and surface readiness answers that reflect your actual environment, within the guardrails and approval flows your team defines. Because Unify works with the tools you already run instead of replacing them, your teams avoid lock-in to a single vendor’s ecosystem and get one view of the gaps where risk tends to hide between tools.
