Gen AI in software engineering has moved well beyond autocomplete. The emerging frontier is agentic coding: AI systems able to planning changes, executing them across multiple steps and iterating based on feedback. Yet despite the joy around “AI agents that code,” most enterprise deployments underperform. The limiting factor is not any longer the model. It’s context: The structure, history and intent surrounding the code being modified. In other words, enterprises at the moment are facing a systems design problem: They’ve not yet engineered the environment these agents operate in.
The shift from assistance to agency
The past 12 months has seen a rapid evolution from assistive coding tools to agentic workflows. Research has begun to formalize what agentic behavior means in practice: The flexibility to reason across design, testing, execution and validation somewhat than generate isolated snippets. Work akin to dynamic motion re-sampling shows that allowing agents to branch, reconsider and revise their very own decisions significantly improves outcomes in large, interdependent codebases. On the platform level, providers like GitHub at the moment are constructing dedicated agent orchestration environments, akin to Copilot Agent and Agent HQ, to support multi-agent collaboration inside real enterprise pipelines.
But early field results tell a cautionary story. When organizations introduce agentic tools without addressing workflow and environment, productivity can decline. A randomized control study this 12 months showed that developers who used AI assistance in unchanged workflows accomplished tasks more slowly, largely attributable to verification, rework and confusion around intent. The lesson is easy: Autonomy without orchestration rarely yields efficiency.
Why context engineering is the true unlock
In every unsuccessful deployment I’ve observed, the failure stemmed from context. When agents lack a structured understanding of a codebase, specifically its relevant modules, dependency graph, test harness, architectural conventions and alter history. They often generate output that appears correct but is disconnected from reality. An excessive amount of information overwhelms the agent; too little forces it to guess. The goal is just not to feed the model more tokens. The goal is to find out what ought to be visible to the agent, when and in what form.
The teams seeing meaningful gains treat context as an engineering surface. They create tooling to snapshot, compact and version the agent’s working memory: What’s persevered across turns, what’s discarded, what’s summarized and what’s linked as a substitute of inlined. They design deliberation steps somewhat than prompting sessions. They make the specification a first-class artifact, something reviewable, testable and owned, not a transient chat history. This shift aligns with a broader trend some researchers describe as “specs becoming the brand new source of truth.”
Workflow must change alongside tooling
But context alone isn’t enough. Enterprises must re-architect the workflows around these agents. As McKinsey’s 2025 report “One 12 months of Agentic AI” noted, productivity gains arise not from layering AI onto existing processes but from rethinking the method itself. When teams simply drop an agent into an unaltered workflow, they invite friction: Engineers spend more time verifying AI-written code than they might have spent writing it themselves. The agents can only amplify what’s already structured: Well-tested, modular codebases with clear ownership and documentation. Without those foundations, autonomy becomes chaos.
Security and governance, too, demand a shift in mindset. AI-generated code introduces recent types of risk: Unvetted dependencies, subtle license violations and undocumented modules that escape peer review. Mature teams are starting to integrate agentic activity directly into their CI/CD pipelines, treating agents as autonomous contributors whose work must pass the identical static evaluation, audit logging and approval gates as any human developer. GitHub’s own documentation highlights this trajectory, positioning Copilot Agents not as replacements for engineers but as orchestrated participants in secure, reviewable workflows. The goal isn’t to let an AI “write every part,” but to be sure that when it acts, it does so inside defined guardrails.
What enterprise decision-makers should concentrate on now
For technical leaders, the trail forward starts with readiness somewhat than hype. Monoliths with sparse tests rarely yield net gains; agents thrive where tests are authoritative and may drive iterative refinement. This is precisely the loop Anthropic calls out for coding agents. Pilots in tightly scoped domains (test generation, legacy modernization, isolated refactors); treat each deployment as an experiment with explicit metrics (defect escape rate, PR cycle time, change failure rate, security findings burned down). As your usage grows, treat agents as data infrastructure: Every plan, context snapshot, motion log and test run is data that composes right into a searchable memory of engineering intent, and a durable competitive advantage.
Under the hood, agentic coding is less a tooling problem than a knowledge problem. Every context snapshot, test iteration and code revision becomes a type of structured data that have to be stored, indexed and reused. As these agents proliferate, enterprises will find themselves managing a completely recent data layer: One which captures not only what was built, but the way it was reasoned about. This shift turns engineering logs right into a knowledge graph of intent, decision-making and validation. In time, the organizations that may search and replay this contextual memory will outpace those that still treat code as static text.
The approaching 12 months will likely determine whether agentic coding becomes a cornerstone of enterprise development or one other inflated promise. The difference will hinge on context engineering: How intelligently teams design the informational substrate their agents depend on. The winners shall be those that see autonomy not as magic, but as an extension of disciplined systems design:Clear workflows, measurable feedback, and rigorous governance.
Bottom line
Platforms are converging on orchestration and guardrails, and research keeps improving context control at inference time. The winners over the subsequent 12 to 24 months won’t be the teams with the flashiest model; they’ll be those that engineer context as an asset and treat workflow because the product. Try this, and autonomy compounds. Skip it, and the review queue does.
Context + agent = leverage. Skip the primary half, and the remaining collapses.
Dhyey Mavani is accelerating generative AI at LinkedIn.
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