Introduction

AI has reached a critical point in the workplace. Agent-based code generation assistants are capable of planning and executing changes across multiple steps and iterating based on feedback. However, most enterprise deployments fail. It's no longer the model that is the problem, but the context.

Context: Architecture of Design

Designing the context for these systems is crucial for success. The structure, history, and intent surrounding the code being changed are essential for the agents' performance. Companies are facing a system design challenge: they haven't yet designed a stable environment for these systems to operate in.

Agent Behavior

The recent evolution from assistive tools to workflow-based agents has led to redefining what agent behavior means in practice: the ability to reason through design, testing, execution, and validation instead of producing isolated pieces. Research like dynamic action re-sampling shows that allowing agents to change direction, re-evaluate, and revise their decisions improves performance significantly in interdependent codebases.

Orchestration and Guardrails

Platforms like GitHub are building dedicated orchestration and guardrail environments to support multi-agent workflow collaboration.

The Hard Part: Context

Introducing agent-based code generation assistants without addressing workflows and context results in decreased productivity. A study this year showed that developers who use AI assistance in unchanged workflows complete tasks slower, primarily due to verification, rework, and intent confusion.

Designing Context: The Key to Success

In every failed deployment I've observed, the lack of context has been the cause. When agents lack a structured understanding of the codebase, including relevant modules, dependency graphs, test harnesses, architectural conventions, and change history. They produce output that seems correct but is disconnected from reality.

The Right Part: Context Engineering

Teams that see significant gains are treating context as an engineering surface. Creating tools to snap, compact, and version the 'agent working memory': what is persisted across turns, what is discarded, what is summarized, and what is linked instead of inline.

Integrating with Workflows

But context alone is not enough. Companies must rethink workflows around these agents. As McKinsey’s 2025 report noted, productivity gains do not come from superimposing AI on existing processes but from a new process-oriented mindset. When teams simply insert an agent into unchanged workflows, problems arise: developers spend more time verifying the code generated by agents than would have spent writing it themselves. Agents can amplify only what is already structured: well-tested, modular, and documented code.

Security and Governance

Security and governance are also concerns. AI-generated code presents new forms of risk: uncontrolled dependencies, intellectual property rights violations, and documentally tested components that escape peer review.

CI/CD Integration

Mature teams are starting to integrate agent activity directly into CI/CD workflows, treating agents as autonomous contributors whose work must meet the same static analysis, audit logging, and approval gates as human work.

Future Outlook

The next season will determine whether agent-based coding becomes a central aspect of enterprise development or another empty promise. The key is engineered context, i.e., creating a stable and structured environment for these systems.