Gen AI in software engineering has advanced far past simple autocomplete. The new frontier is agentic coding: AI systems that can plan changes, carry them out over multiple steps and refine their work based on feedback. However, despite the buzz around “AI agents that code,” most enterprise rollouts fall short. The main bottleneck is no longer the underlying model. It’s context: The structure, history and intent surrounding the code being modified. In effect, enterprises now face a systems design challenge: They haven’t yet engineered the environment in which these agents operate.
The shift from assistance to agency
Over the last year, we’ve seen a fast transition from assistive coding tools to agentic workflows. Research is starting to pin down what agentic behavior looks like in practice: Reasoning across design, testing, execution and validation instead of emitting isolated code fragments. Work like dynamic action re-sampling demonstrates that letting agents branch, revisit and revise their own choices dramatically improves performance in large, interdependent codebases. On the platform side, providers such as GitHub are building dedicated orchestration layers for agents, including Copilot Agent and Agent HQ, to enable multi-agent collaboration inside real enterprise delivery pipelines.
Yet early real-world deployments offer a warning. When organizations introduce agentic tools without rethinking workflow and environment, productivity can actually drop. A randomized controlled study this year found that developers using AI assistance in unchanged workflows often finished tasks more slowly, largely because of extra verification, rework and ambiguity around intent. The takeaway is clear: Autonomy without orchestration rarely produces efficiency.
Why context engineering is the real unlock
In every failed deployment I’ve seen, the root cause was context. When agents lack a structured view of the codebase—its relevant modules, dependency graph, test harnesses, architectural patterns and change history—they tend to produce code that looks plausible but doesn’t fit reality. Overloading the agent with information is just as harmful as starving it; too much leads to noise, too little forces guesswork. The objective isn’t simply to push more tokens into the model. It’s to decide what the agent should see, at what moment and in what representation.
The teams achieving real impact treat context as an explicit engineering surface. They build tools to snapshot, compress and version the agent’s working memory: What persists across turns, what gets dropped, what is summarized and what is referenced via links instead of inlined. They design structured deliberation phases rather than ad hoc prompting sessions. They elevate the specification to a first-class artifact—reviewable, testable and owned—rather than a fleeting chat transcript. This evolution mirrors a broader shift some researchers describe as “specs becoming the new source of truth.”
Workflow must change alongside tooling
But context by itself is insufficient. Enterprises also need to redesign the workflows that surround these agents. As McKinsey’s 2025 report “One Year of Agentic AI” emphasizes, productivity gains come not from bolting AI onto existing processes but from reimagining the process end to end. When teams simply drop an agent into an unchanged workflow, they create friction: Engineers end up spending more time validating AI-generated code than they would have spent writing it themselves. Agents can only amplify what is already well-structured: Robust tests, modular architectures, clear ownership and solid documentation. Without those foundations, autonomy devolves into disorder.
Security and governance also require a new posture. AI-authored code brings distinct risks: Unreviewed dependencies, subtle licensing issues and undocumented modules that bypass normal peer review. More mature organizations are starting to wire agentic activity directly into their CI/CD pipelines, treating agents as autonomous contributors whose work must clear the same static analysis, logging and approval gates as human developers. GitHub’s own materials reflect this direction, framing Copilot Agents not as replacements for engineers but as orchestrated actors within secure, auditable workflows. The aim is not to let AI “do everything,” but to ensure that when it does act, it operates inside well-defined guardrails.
What enterprise decision-makers should focus on now
For technical leaders, the right starting point is readiness, not hype. Large monoliths with weak test coverage rarely see net benefits; agents perform best where tests are authoritative and can drive iterative improvement. This is precisely the loop Anthropic highlights for coding agents. Begin with pilots in narrow, well-bounded areas (test generation, legacy system modernization, isolated refactors), and treat each rollout as an experiment with clear metrics (defect escape rate, PR cycle time, change failure rate, reduction in security findings). As adoption scales, treat agents as part of your data infrastructure: Every plan, context snapshot, action trace and test run becomes data that aggregates into a searchable memory of engineering intent—and a durable competitive edge.
Beneath the surface, agentic coding is less about tools than about data. Each context snapshot, test loop and code revision is a structured data point that must be stored, indexed and reused. As agents spread, enterprises will effectively be managing a new data layer: One that records not only what was built, but how it was reasoned about. This transforms engineering logs into a knowledge graph of intent, decisions and validation. Over time, organizations that can query and replay this contextual memory will outpace those that still treat code as static text files.
The next year will likely decide whether agentic coding becomes a foundational enterprise capability or just another overhyped wave. The deciding factor will be context engineering: How thoughtfully teams design the informational substrate their agents depend on. The leaders will be those who view autonomy not as magic, but as an extension of disciplined systems design: Clear workflows, measurable feedback loops and strong governance.
Bottom line
Platforms are rapidly converging on orchestration layers and guardrails, while research continues to refine context control at inference time. Over the next 12 to 24 months, the standout teams won’t be the ones with the flashiest model; they’ll be the ones that treat context as a strategic asset and workflow as the real product. Do that, and autonomy compounds. Ignore it, and your review queue explodes.
Context + agent = leverage. Remove the first term, and the rest collapses.
Dhyey Mavani is accelerating generative AI at LinkedIn.
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