Gen AI in software engineering goes far beyond autocomplete. The new frontier is agent coding. It’s an AI system that can plan changes, execute changes across multiple steps, and iterate based on feedback. But despite the excitement around “AI agents that code,” most enterprise deployments have underperformed. The limiting factor is no longer the model. the context: The structure, history, and intent surrounding the code being changed. In other words, companies are now facing a system design problem. This means that you have not yet designed the environment in which the agent will run.
Transition from aid to agency
The past year has seen a rapid evolution from assisted coding tools to agent workflows. Research is beginning to formally express what agent behavior actually means: the ability to reason across design, testing, execution, and validation rather than producing isolated snippets. work such as Dynamic action resampling Enabling agents to branch, rethink, and modify their decisions has been shown to significantly improve outcomes for large, interdependent codebases. At the platform level, providers such as GitHub are currently building dedicated agent orchestration environments, including: Co-pilot agent and agent headquarterssupporting multi-agent collaboration within real enterprise pipelines.
But early field results speak with caution. When organizations deploy agent tools without addressing their workflows and environments, productivity can suffer. A randomized controlled study this year showed that developers who used AI assistance in unaltered workflows completed tasks more slowly, primarily due to validation, rework, and confusion about intent. The lesson is simple. Autonomy without orchestration rarely yields efficiency.
Why context engineering is the real key
In all the failed deployments I observed, the failure was due to context. When agents lack a structured understanding of the codebase, especially its associated modules, dependency graphs, test harnesses, architectural conventions, and change history. It often produces output that appears correct but deviates from reality. Too much information can overwhelm agents. I have to guess it’s too little. The goal is not to feed the model more tokens. The goal is to decide what to display to the agent, when to display it, and in what format.
Teams that achieve meaningful results treat context as an engineering aspect. They create tools to snapshot, compress, and version an agent’s working memory. That is, things that are kept across turns, things that are discarded, things that are summarized, and things that are linked rather than inlined. They design deliberative steps rather than prompting sessions. They turn specifications into first-class artifacts that can be reviewed, tested, and owned, rather than ephemeral chat history. This shift is consistent with a broader trend that some researchers describe as “specifications becoming the new source of truth.”
Workflows need to change with tools
But context alone is not enough. Enterprises will need to restructure their workflows around these agents. as McKinsey 2025 Report “A Year in Agent AI” They point out that productivity gains come not from layering AI onto existing processes, but from rethinking the processes themselves. If a team simply drops an agent into an unchanged workflow, it creates friction. Engineers spend more time validating code created by AI than they do writing code themselves. Agents can only extend what is already structured: a well-tested, modular codebase with clear ownership and documentation. Without such a foundation, autonomy becomes chaotic.
A shift in thinking is also required when it comes to security and governance. AI-generated code introduces new forms of risk, including unexamined dependencies, subtle license violations, and undocumented modules that escape peer review. Mature teams are starting to integrate agent activity directly into CI/CD pipelines, treating agents as autonomous contributors whose work must pass through the same static analysis, audit logs, and approval gates as human developers. GitHub’s own documentation emphasizes this trajectory, positioning Copilot Agent not as a replacement for engineers, but as coordinated participants in secure, reviewable workflows. The goal is not to force the AI to “write everything,” but to ensure that when the AI operates, it operates within defined guardrails.
What corporate decision makers should be paying attention to now
For technology leaders, the path forward begins with preparation, not hype. A monolith with sparse testing rarely yields a net benefit. Agents can succeed where tests are trusted and drive iterative improvements. It’s just a loop human Call for coding agent. Pilots in tightly scoped domains (test generation, legacy modernization, isolated refactoring). Treat each deployment as an experiment with explicit metrics: defect avoidance rate, PR cycle time, change failure rate, and burndown security findings. As usage grows, treat the agent as data infrastructure. All plans, context snapshots, action logs, and test runs constitute a searchable memory of engineering intent, giving you a lasting competitive advantage.
Internally, agent coding is more of a data problem than a tool problem. Every context snapshot, test iteration, and code revision becomes a form of structured data that needs to be stored, indexed, and reused. As these agents proliferate, companies will be managing an entirely new layer of data, one that captures not just what was built, but how it was inferred. This change transforms the engineering log into a knowledge graph of intent, decision-making, and validation. Over time, organizations that can retrieve and reproduce this contextual memory will outnumber those that still treat code as static text.
The next year could determine whether agent coding becomes a cornerstone of enterprise development or just another over-the-top promise. The difference lies in context engineering: how teams intelligently design the information infrastructure that agents rely on. The winners will be those who see autonomy not as magic, but as an extension of disciplined system design, including clear workflows, measurable feedback, and rigorous governance.
conclusion
Platforms are converging on orchestration and guardrails, and research continues to improve context control during inference. The winners over the next 12 to 24 months won’t be the teams with the flashiest models. They will design context as an asset and treat workflow as a product. This will give you more autonomy. Skip will skip the review queue.
Context + Agent = Leverage. If you skip the first half, the rest will fall apart.
Dhyey Mavani accelerates generative AI at LinkedIn.
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