General AI has advanced automation in software engineering. The emerging frontier is agentic coding: AI systems able to plan changes, execute them in multiple stages, and iterate based on feedback. Yet despite the excitement surrounding “AI agents of code,” most enterprise deployments are underperforming. The finite element is no longer a model. This is Context: The structure, history and intent around the code being changed. In other words, enterprises now face a system design problem: they haven’t yet engineered the environment for these agents.
Transition from aid to agency
The past year has seen a rapid evolution from supporting coding tools to agent workflows. Research has shown what agentic behavior means in practice: the ability to design, test, implement and validate rather than produce isolated pieces. Work like work Dynamic Action Resample shows that allowing agents to branch, revise, and revise their decisions significantly improves results in large, interdependent codebases. At the platform level, providers such as GitHub are now building dedicated agent orchestration environments, e.g Copilot Agent and Agent Headquarterssupporting multi-agent collaboration within real enterprise pipelines.
But early field results tell a cautionary tale. When organizations introduce agent tools without addressing workflow and environment, productivity can suffer. A randomized control study this year showed that developers who used AI assistance in unchanging workflows completed tasks more slowly, largely because of confusion around validation, rework, and intent. The lesson is straightforward: Autonomy without orchestration rarely leads to performance.
Why context engineering is the real unlock
In every failed deployment I observed, the failure arose from context. When agents lack a structured understanding of the codebase, specifically its related modules, dependency graphs, test controls, architectural conventions, and change history. They often produce output that appears accurate but is disconnected from reality. Too much information overwhelms the agent. It takes very little to guess. The goal is not to feed more tokens to the model. The objective is what should be visible to the agent, when and in what form.
Teams that see meaningful benefits understand context at the same level as engineering. They create tooling to snapshot, compact, and version an agent’s working memory: what is retained in a turn, what is discarded, what is abstracted, and what is linked instead. They design meditation steps rather than prompt sessions. They make the specification a first-class architect, reviewable, testable and owned, not a temporary chat history. The shift aligns with a broader trend some researchers describe as “visuals becoming the new source of reality.”
Workflow should change along with tooling
But context alone is not enough. Enterprises must rebuild the flow around these agents. As McKinsey’s 2025 Report “A Year of Agent AI” Noted, AI in productivity arises from rethinking processes, not existing processes. When teams simply drop an agent into an unsolicited workflow, they invite friction: Engineers spend more time than they would have spent writing on their own. Agents can only extend what is already configured: a well-tested, modular codebase with clear ownership and documentation. Without these foundations, sovereignty becomes chaotic.
Security and governance also demand a change in mindset. AI-generated code introduces new forms of risk: unsupported dependencies, subtle license violations and undocumented modules that escape peer review. Mature teams are beginning to integrate agent activity directly into their CI/CD pipelines, treating agents as autonomous partners whose work must pass the same static analysis, audit logging and approval gates as any human developer. GitHub’s own documentation highlights this trajectory, not as a replacement for engineers, but as copilot agents as orchestrated participants in secure, reviewable workflows. It is not intended to “write everything” to an object, but to ensure that when it does work, it does so within the specified guards.
What should enterprise decision makers focus on now?
For tech leaders, the way forward starts with preparation rather than hype. Integration with sparse tests rarely yields net benefits. Agents thrive where tests are authentic and can drive iterative refinement. It’s quite a loop Anthropic Call for coding agents. Pilot in tightly scoped domains (test generation, legacy modernization, isolated refactoring). Treat each deployment as an experiment with clear metrics (defect escape rate, PR cycle time, change in failure rate, security results burned). As your deployments grow, treat agents as a data infrastructure: each plan, context snapshot, action log and test run data is embedded in a searchable memory of engineering intent, and a sustainable competitive advantage.
Under the hood, agentic coding is less a tooling problem than a data problem. Each contextual snapshot, test iteration, and code revision becomes a form of structured data that must be stored, indexed, and reused. As these agents proliferate, enterprises will find themselves managing an entirely new data layer: one that is not only constructed, but how it is reasoned about. This transformation transforms engineering logs into knowledge graphs for intent, decision making, and validation. Over time, organizations that can find and replay this contextual memory will outpace those that still treat code as static text.
The coming year will likely determine whether agent coding becomes the cornerstone of enterprise development or just another inflated promise. The difference will depend on context engineering: how intelligently teams rely on their agents. The winners will be those who see autonomy not as magic but as an extension of disciplined system design: clear workflows, measurable feedback, and tight governance.
Bottom line
Platforms are changing on orchestration and defenders, and research improves control over individualization time contexts. The winners in the next 12 to 24 months won’t be the teams with the fastest models. They will be the ones who contextualize the engineer as an asset and treat the workflow as a product. Do this, and sovereign compounds. Skip it, and the review queue takes place.
Context + agent = leverage. Skip the first half, and the rest falls apart.
Dhoi Mavani is accelerating generative AI at LinkedIn.
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