From Code to Context

LLM-powered coding agents are introducing a new dimension to software development, where AI systems can interpret both our codified artifacts and human intent to generate consistent, compliant code.

This emerging capability builds on our "Everything as Code" foundation, where we've systematized infrastructure, security, and pipelines, by adding context-aware intelligence that can process business requirements, architectural principles, and domain knowledge.

The result isn't the replacement of code with context, but rather a hybrid approach where LLMs handle boilerplate generation and coordinated updates across multiple systems while engineers focus on novel problems and critical business logic.

As organizations adopt these tools, we'll see existing engineering roles evolve to include AI collaboration skills, new specializations emerge around knowledge curation and AI-systems architecture, and teams gradually develop systematic approaches to managing the context that feeds these AI systems.

Success in this landscape requires addressing real challenges around context versioning, debugging AI-generated code, and varying risk tolerances, while building workflows where humans and AI complement each other's strengths.

The Solid Foundation of Everything as Code

The "Everything as Code" (EaC) paradigm has transformed software delivery over the past decade. By codifying infrastructure, configurations, security policies, and CI/CD pipelines, we've achieved unprecedented levels of automation, consistency, and traceability. Recent literature identifies 25 distinct EaC practices organized across six functional layers, from Infrastructure as Code to Security as Code.

This foundation isn't going away. Instead, it's becoming the structured knowledge base upon which AI-assisted development builds. The machine-readable artifacts we've created through EaC are precisely what make LLM-powered automation possible.

The Emergence of Context-Aware Development

LLM coding agents introduce a new capability: the ability to interpret human intent and generate code based on both structured artifacts (our EaC foundation) and unstructured context (business requirements, architectural principles, design documents). This isn't replacing code with context; it's enriching our code-centric practices with context-aware intelligence.

Consider the two types of context that feed these systems:

• Structured Context: The well-defined, versioned, tested artifacts from our EaC practices. These remain critical because they provide the precise, unambiguous specifications that systems require.
• Unstructured Context: Business requirements, architectural decisions, team conventions, and domain knowledge. LLMs excel at processing this information, but it supplements rather than supplants structured specifications.

A Hybrid Future: Context-Augmented Code

Rather than a wholesale shift from code to context, we're moving toward a hybrid model where different aspects of software development are handled by the most appropriate approach:

• Boilerplate Generation: Standard patterns, CRUD operations, and repetitive code structures
• Coordinated Updates: Keeping infrastructure code, deployment pipelines, and documentation in sync when requirements change
• Initial Prototyping: Rapid creation of proof-of-concepts based on high-level requirements
• Documentation and Tests: Generating comprehensive documentation and test suites based on existing code

What Humans Will Continue to Own

• Novel Problem Solving: Addressing unique business challenges that require creative solutions
• Critical Business Logic: Core algorithms and decision logic that define competitive advantage
• Architectural Decisions: High-level system design and technology choices
• Context Curation: Maintaining and evolving the knowledge base that feeds AI systems
• Quality Assurance: Reviewing, validating, and taking responsibility for generated code

The Pragmatic Implementation Path

Organizations adopting LLM-assisted development will likely follow a gradual path:

Phase 1: Assisted Development

• Developers use AI for code completion and generation of simple functions
• Context is ad-hoc, provided through prompts
• Focus on individual productivity gains

Phase 2: Systematic Context Management

• Organizations develop structured approaches to managing context
• Architectural principles and patterns are formally documented for AI consumption
• Team-level standards for AI-assisted development emerge

Phase 3: Integrated AI Workflows

• AI agents become first-class participants in development workflows
• Continuous feedback loops improve context and generation quality
• Specialized roles emerge for managing AI-human collaboration

The Evolution of Engineering Roles

Rather than replacing engineers with "Context Engineers," we'll see existing roles evolve and new specializations emerge:

The Modern Software Engineer

• Focuses on complex problem-solving and system design
• Reviews and refines AI-generated code
• Maintains expertise in debugging and optimization
• Understands both code and context management

The AI-Systems Architect

• Designs the interaction between human developers and AI agents
• Defines architectural guardrails and principles in AI-consumable formats
• Ensures consistency across AI-generated components
• Manages the feedback loops that improve AI performance

The Knowledge Curator

• Maintains the organizational knowledge base
• Ensures documentation and context remain accurate and accessible
• Manages the versioning and evolution of context
• Bridges between business stakeholders and technical teams

Real Challenges That Need Solutions

This evolution faces several practical challenges that the industry must address:

Context Drift and Versioning
Unlike code, context can be ambiguous and contradictory. We need robust systems for:

• Versioning context alongside code
• Resolving conflicts between different context sources
• Maintaining context-code synchronization

The Debugging Dilemma
When AI generates code based on complex context:

• How do we trace errors back to their source?
• How do we debug issues in the generation process itself?
• How do we maintain accountability for system failures?

The Trust Gradient
Different organizations and domains have varying risk tolerances:

• Financial services may limit AI to documentation and testing
• Startups might embrace full AI-driven development
• Healthcare and aerospace will require extensive validation frameworks

Economic Realities

• The cost of maintaining high-quality context
• The investment in retraining and reorganization
• The ongoing expenses of AI infrastructure and services

A Measured Outlook

The integration of LLM-powered agents into software development represents a significant evolution, but not a revolution. We're not abandoning "Everything as Code" for "Everything as Context"; we're enriching our code-centric practices with context-aware intelligence.

Success in this new landscape requires:

• Building on EaC Foundations: Our investment in codifying everything becomes the structured knowledge base for AI systems
• Gradual Adoption: Starting with low-risk, high-repetition tasks and gradually expanding AI involvement
• Human-AI Collaboration: Designing workflows where humans and AI agents complement each other's strengths
• Continuous Learning: Both our AI systems and our teams must continuously adapt and improve
• Pragmatic Expectations: Understanding that AI augments but doesn't replace human judgment and creativity

Conclusion

The future of software engineering isn't a binary choice between code and context; it's an integration of both. LLM-powered agents will increasingly handle routine tasks, freeing engineers to focus on creative problem-solving and strategic decisions. But this augmentation, not replacement, is the key insight.

As we move forward, the most successful organizations will be those that thoughtfully integrate AI capabilities while maintaining the rigor and discipline that "Everything as Code" brought to our industry. The question isn't "Will AI write our code?" but rather "How do we create systems where humans and AI collaborate effectively to deliver better software, faster?"

The context-aware future is coming, but it's building on, not replacing, the code-centric foundation we've spent years establishing. And that's not just more realistic; it's more powerful.