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For the past two years, the conversation about AI-assisted software development has been dominated by speed. A new GitLab survey of more than 1,500 developers and technology leaders found that 60% say AI coding ROI has already exceeded expectations, and 78% report their teams are writing and committing code faster since adopting AI tools.
But speed without control is a liability.
Most organizations have pursued agentic engineering by adding AI coding tools on top of their existing infrastructure. Coding agents are delivering speed, but that speed isn’t showing up across the full software lifecycle: Only 21% of respondents report productivity gains beyond code generation itself.
“Speed without control is a liability.”
The infrastructure problem runs deeper. Git backends, toolchains, and governance frameworks were built for human-scale concurrency. Agents operate at machine scale, and that mismatch shows up fast. Platform reliability breaks down with millions of agent sessions hitting the same backend, security exposure widens as agents touch dependencies at scale, and cost overruns mount as agents consume tokens inefficiently on infrastructure that wasn’t built for them.
The adoption curve for AI coding tools outpaced the development of required guardrails, with 80% of organizations saying they adopted AI tools faster than they developed policies to govern them, and 82% reporting that AI-generated code risks creating a new form of technical debt that their organizations are not prepared to manage.
In practice, that means platform reliability challenges under agent load, security and compliance exposure that widens as agents touch dependencies at volume, and agents operating with artificial confidence because they lack full context. Only 28% of organizations say their software development lifecycle tools are fully integrated with shared data and workflows, which means most teams are trying to govern agent actions across a toolchain that was never designed for them.
Agentic engineering requires two things: agentic coding and agentic infrastructure. Most organizations have the first but lack the second.
Agentic infrastructure spans four areas: the execution layer, the context layer, the governance layer, and the orchestration layer working together.
The first is machine-scale execution. Git backends, CI/CD pipelines, and deployment systems were designed for human-paced development. In the agentic era, they need to handle millions of agent sessions without breaking. When a production incident occurs, the path from symptom back to origin should take minutes, not days.
The second is context that travels with code. As Bastian Stahmer, Business Owner of Vehicle Software Development Platform at Mercedes-Benz, put it on a panel recently, “An agent can only be as good as the context and semantics fed to it.” A context graph connecting code, work items, pipelines, security findings, and production signals is what makes agents genuinely useful at scale and keeps artificial confidence in check.
“An agent can only be as good as the context and semantics fed to it.”
The third is governance built into the flow. Agent actions need to be tied to an identity, logged against a policy, and provable to a reviewer. Low-risk changes move fast, while higher-risk changes trigger review. For Mercedes, operating under automotive regulatory standards that require full traceability and human accountability, GitLab is the control plane where that accountability lives.
The fourth is orchestration. Execution, context, and governance are only as effective as the system coordinating them. The orchestration layer coordinates agent actions across the full software lifecycle according to the policies teams define, determining which agents run, in what order, and how failures and handoffs are managed. Without it, agentic infrastructure is a set of independent capabilities rather than a working system.
The next phase of AI in software will focus less on generating code and more on governing it, according to 85% of respondents. That shift reflects how enterprises are maturing their thinking about AI, from a productivity tool to a foundational capability that needs to be trusted, traced, and maintained at scale.
When governance is built into the platform, speed and control are no longer in tension. Traceability becomes a competitive advantage. Context becomes institutional memory. And the codebase, rather than accumulating invisible risk, becomes an asset that grows more reliable over time.
