The Complete Guide to Context Engineering for AI

According to Gartner, most AI projects end in failure, with 63% of businesses not having or unsure if they have the right data for AI. Organizations are running into a production wall and struggling to turn AI demos into reliable, revenue-generating systems. When AI agents or applications hallucinate, pipelines break, or answers are stale, the problem isn’t your model – it’s the missing context that surrounds it.

Read this ebook to learn how Context Engineering gives your teams a practical framework for designing, assembling, and governing the continuous context your AI needs to make accurate, real-time decisions at scale and how to pave the path for production ROI.

Highlights

• Architectural shift from traditional ML to GenAI and agentic AI systems, and why it collapses the line between data and application logic.
• 4 pillars of Context Engineering and how they work together in real deployments.
• Why existing patterns fall short, and what unifying data and architecture for continuous context looks like.
• How Confluent Intelligence provides a Real-Time Context Engine to keep AI grounded in live business data.
• Reference architectures and use cases for Retail / Ecommerce, Financial Services, Manufacturing, Telecom, Healthcare, and more.
• Step-by-step guidance for how to serve fresh context to any AI agent or application anywhere.