Agentic Engineering Needs More Than Speed

For the past two years, Australian engineering teams have been racing to capitalise on AI-assisted software development, with speed driving much of the conversation. The global picture backs the local instinct. A new GitLab survey of more than 1,500 developers and technology leaders found that 60 per cent say AI coding ROI has already exceeded expectations and 78 per cent report their teams are writing and committing code faster since adopting AI tools.

Yet without governance, speed isn’t an advantage. It’s a risk.

Most organisations have approached agentic engineering by layering AI coding tools onto their existing infrastructure. The agents deliver speed, no question. Yet that speed rarely carries through to the rest of the software lifecycle, with only 21 per cent of respondents reporting productivity gains beyond code generation itself.

The underlying issue is structural. Git backends, toolchains and governance frameworks were all built for the pace of human teams. Agents operate at machine scale and that mismatch becomes apparent quickly. Platform reliability suffers when millions of agent sessions converge on the same backend, security exposure grows as agents engage dependencies at volume and costs escalate as agents consume tokens inefficiently against infrastructure that was never designed for them.

AI adoption has outrun governance

The adoption curve for AI coding tools outpaced the required guardrails, with 80 per cent of organisations saying they adopted AI tools faster than they developed policies to govern them and 82 per cent reporting that AI-generated code risks creating a new form of technical debt their organisations 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 per cent of organisations 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.

Infrastructure must evolve with AI

Agentic engineering requires two things: agentic coding and agentic infrastructure. Most organisations have the first, but are missing 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.

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.

What’s next for Australian organisations

A clear majority of 85 per cent of respondents agree that the next phase of AI in software will focus less on generating code and more on governing it. 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. For Australian organisations working under tightening privacy reforms and sector-specific obligations, that maturity isn’t optional. It’s the cost of doing business.

When governance is built into the platform, speed and control stop being 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.

Manav Khurana is Chief Product and Marketing Officer at GitLab.

Last Updated on July 3, 2026 by Manav Khurana