Context Engineering at Scale: Enterprise Lessons and the Future of Development

Most Tool Changes Are Context Switch Disasters

When I switch from my terminal to my IDE to my note-taking app to my project management tool, the AI loses the thread. Each tool maintains its own context bubble. My carefully curated understanding gets scattered across a dozen interfaces, none of which talk to each other.

The result? I spend more time re-establishing context than actually working.

The Enterprise Reality Check

At GE Aerospace, where I work as Principal Architect on the Cloud AI services team, I see this playing out at enterprise scale. Each area and person has knowledge but that knowledge is shared more often in meetings than by reusable artifacts that can be shared freely and iterated on.

Every new hire and contractor has to "re-learn" what everyone else in the company already knew.

It's the same reason reading is the foundation of knowledge transfer.

Teams are adopting AI-powered development tools, but they're hitting productivity walls because context doesn't flow between systems.

The tools that are winning—Claude Code—aren't just adding AI features. They're fundamentally rethinking how context flows through development workflows.

Context Engineering: A Practical Example

Consider a typical debugging session to see the difference between traditional and context-engineered approaches:

Traditional Approach:

1. Developer encounters bug in production
2. Searches through logs manually
3. Opens multiple browser tabs with documentation
4. Switches between terminal, IDE, and Slack
5. Asks teammates in chat about similar issues
6. Context gets lost with each tool switch
7. Spends 60% of time reconstructing what they already knew

Context-Engineered Approach:

1. AI has persistent access to:
   • Recent deployment logs and metrics
   • Related code changes and PR discussions
   • Historical bug patterns and solutions
   • Team knowledge base and runbooks
2. Developer describes the issue once
3. AI correlates information across all sources
4. Provides targeted debugging steps with full context
5. Solution includes prevention strategies based on team patterns

The difference isn't just efficiency—it's the preservation and amplification of institutional knowledge.

The Evolution: From Tools to Context Architecture

Phase 1: Intelligent Context Submission

The first phase focuses on improving how we create and submit context:

• AI-powered grammar and style checking for technical documentation
• Real-time context validation and enhancement
• Seamless integration between the world's best text editor (VS Code) and AI interfaces
• Why recreate inferior chat interfaces when we already have superior editing tools?

Phase 2: Context Persistence and Retrieval

The next evolution involves making context repositories truly intelligent:

• Automatic context categorization and tagging
• Semantic search across historical decisions and outcomes
• Context suggestion based on current work patterns
• Integration with existing development workflows

Phase 3: Predictive Context Engineering

The ultimate goal is context systems that anticipate information needs:

• Proactive context gathering based on project phase
• Automated context correlation across team members
• Intelligent context filtering to reduce cognitive load
• Self-improving context models based on decision outcomes

Enterprise Implementation Strategy

Measuring Success

Context engineering success isn't just about AI interactions—it's about team knowledge velocity:

• Time to Context: How quickly can new team members access relevant project context?
• Context Retention: How much institutional knowledge is preserved during team transitions?
• Decision Quality: Are teams making better-informed decisions with improved context access?
• Knowledge Reuse: How often is existing context being leveraged for new problems?

The Competitive Advantage

Organizations that master context engineering will have a fundamental advantage in the AI era. While competitors struggle with context switching overhead, context-engineered teams will:

• Onboard new developers faster with comprehensive, searchable context
• Make better architectural decisions informed by historical outcomes
• Reduce duplicate problem-solving across teams
• Scale AI assistance more effectively with richer context inputs

Building Context-First Culture

The technical implementation is only half the battle. Enterprise success requires cultural shifts:

From Information Hoarding to Context Sharing

• Reward context documentation as highly as code contributions
• Make context accessibility a key performance indicator
• Build context sharing as core criteria for everything
  • if your work doesn't produce context, whats it really doing
  • your work makes a part, it shoudld produce to thing an MCP call could search for it
  • did i send and email to Josh yesturday. LLM with a MCP tool for your email should be able to answer that.
  • make a note to call Julie should add a task to a list that you can see needs to be done.
  • every context artifact should be linked to a specific project, feature, or task

From Meeting-Heavy to Artifact-Heavy Communication

• Default to written, searchable documentation over verbal communication
• Structure meetings to produce reusable context artifacts
• Treat context creation as investment, not overhead

From Individual Knowledge to Team Intelligence

• Build systems that capture and share mental models
• Create feedback loops that improve context quality over time
• Measure team knowledge velocity, not just individual productivity

The Path Forward

This represents more than a feature update—it's positioning development teams at the center of how organizations will work with AI for the next decade.

For enterprise teams looking to embrace context engineering:

1. Audit your context switching overhead: Identify where valuable context gets lost
2. Invest in context infrastructure: Treat context repositories as critical business systems
   1. ideally a monorepo with a single source of truth
3. Experiment with MCP: Explore how different tools can share context through standardized protocols
4. Build context habits: Make context capture as natural as version control
5. Measure context flow: Track how efficiently knowledge moves through your organization

The future belongs to those who master the art and science of context engineering at scale.

In the AI era, context architecture is your competitive advantage.