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Context Engineering for Enterprise AI Guide
As AI moves from experimentation to real-world deployment, the biggest challenge is no longer the model, it’s the system around it. Over time, even the best AI applications degrade due to “context rot”: growing data, shifting use cases, and increasing complexity.
This guide introduces context engineering as the discipline for building reliable, scalable AI systems. Instead of endlessly tweaking prompts, it shows how to design, orchestrate, and govern the flow of information into models: turning AI from a fragile prototype into a continuously improving system.
You’ll learn how to:
- Structure context across design, runtime, and governance layers
- Orchestrate retrieval, tools, and memory with control and transparency
- Implement continuous evaluation and improvement using a Kaizen mindset
- Move from reactive experimentation to proactive system management
Whether you're scaling RAG systems or building agentic applications, this guide provides a practical framework to engineer context deliberately, measure performance continuously, and improve AI systems over time.
Download the guide to start building AI systems that don’t just work, but keep working.
Featured Speakers
Disclaimer
Gartner Cool Vendors in AI Engineering, Arun Chandrasekaran, Manjunath Bhat, Arup Roy, George Brocklehurst, 6 November 2024
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