General AI in software engineering has gone well beyond autocomplete. The emerging frontier is agentic coding: AI systems are able to plan changes, execute them in multiple stages, and iterate based on feedback. Yet despite the excitement around “AI agents that code,” most enterprise deployments perform poorly. The limiting factor is no longer the model. Its Context: The structure, history, and intent of the code is being changed. In other words, enterprises now face a systems design problem: They have not yet engineered the environment in which these agents operate.
Shift from assistance to agency
The past year has seen rapid evolution from helpful coding tools to agentic workflows. Research has begun to formalize what agentic behavior means in practice: the ability to reason in design, testing, execution, and validation rather than generating isolated snippets. act like dynamic action resampling Shows that allowing agents to branch, reconsider, and modify their own decisions significantly improves results in large, interdependent codebases. At the platform level, providers like GitHub are now building dedicated agent orchestration environments, e.g. Copilot Agent and Agent HeadquartersTo support multi-agent collaboration inside real enterprise pipelines.
But early field results tell a cautionary tale. When organizations introduce agentic tools without addressing workflow and environment, productivity may decline. A randomized control study this year showed that developers who used AI assistance in unchanged workflows completed tasks more slowly, largely due to validation, rework, and confusion around intent. The lesson is simple: Autonomy without orchestration rarely creates efficiency.
Why is reference engineering the real unlock?
In every failed deployment I’ve seen, the failure arose from context. When agents lack a structured understanding of the codebase, particularly its relevant modules, dependency graph, test harness, architectural conventions, and change history. They often produce output that appears correct but is different from reality. Too much information overwhelms the agent; Very little leaves it guessing. The goal is not to feed more tokens to the model. The goal is to determine what, when, and in what form the agent should appear.
Teams looking to see meaningful benefits treat the context as an engineering surface. They create tooling to snapshot, compact, and version the agent’s working memory: what is retained across turns, what is discarded, what is summarized, and what is appended instead of inline. They set the stage for discussion rather than inspiring sessions. They make exclusivity a first-class artifact, something reviewable, testable, and owned, not a fleeting chat history. This shift aligns with a broader trend that some researchers describe as “attributes becoming the new source of truth”.
Workflow will have to change along with tooling
But reference alone is not enough. Enterprises will have to reorganize workflows around these agents. As McKinsey’s 2025 report “A Year of Agent AI” As noted, productivity gains arise not from layering AI on existing processes but from rethinking the process. When teams leave an agent in an unchanged workflow, they invite friction: Engineers spend more time verifying AI-written code than they spend writing it. Agents can simply extend what is already structured: a well-tested modular codebase with clear ownership and documentation. Without those foundations, autonomy becomes anarchy.
Security and governance also demand a change in mindset. AI-generated code introduces new forms of risk: undocumented dependencies, subtle license violations, and undocumented modules that escape peer review. Mature teams are starting to integrate agentive activity directly into their CI/CD pipelines, treating agents as autonomous contributors whose work must pass through the same static analysis, 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 systematic participants in secure, reviewable workflows. The goal is not to let the AI “write everything,” but to ensure that when it acts, it does so within defined guardrails.
What should enterprise decision makers focus on now
For technology leaders, the path forward starts with urgency rather than hype. Monoliths with sparse testing rarely deliver net benefits; Agents thrive where tests are authoritative and can promote iterative refinement. it’s just a loop anthropic Coding calls agents. Pilots in tightly scoped domains (test generation, legacy modernization, isolated refactors); Treat each deployment as an experiment with clear metrics (defect resolution rate, PR cycle time, change failure rate, burnout security findings). As your usage grows, treat agents as data infrastructure: each plan, context snapshot, action log, and test run is data that builds into 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 reference snapshot, test iteration and code modification 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 captures not only what was created, but also how it was reasoned about. This transformation transforms the engineering log into a knowledge graph of intent, decision making, and validation. Over time, organizations that can find and replay this episodic memory will overtake those that still treat code as static text.
The coming year will likely determine whether agentic coding becomes a cornerstone of enterprise growth or just another overblown promise. The difference will depend on context engineering: how intelligently teams design the informational substrate that their agents rely on. The winners will be those who see autonomy not as magic, but as an extension of disciplined systems design: clear workflows, measurable feedback, and rigorous governance.
ground level
Platforms are converging on orchestration and guardrails, and research continues to improve context control at inference time. The winners over the next 12 to 24 months will not be the teams with the most attractive models; They will be the ones who engineer the context as an asset and treat the workflow as a product. Do this, and the autonomy compounds. Skip it, and the review queue will start.
Context + agent = leverage. Leave the first half in place, and the rest collapses.
Dhyeya Mavani is accelerating Generative AI on LinkedIn.
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