AI Context Engineering with Apache Airflow®
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Every engineering team has access to the same foundation models (like Claude, GPT, and Llama), but what sets your AI apart is the data it has access to; the context you give it.
This eBook shows you how to build context engineering pipelines to provide your AI agents the context they need, when they need it so you can build AI applications your competitors can't replicate.
You'll learn how to:
- Build context engineering pipelines using Airflow's @task.agent, HITLOperator, and asset-aware scheduling to extract, embed, and load context to storage for AI agents
- Manage context units over time with ContextOps: detecting staleness, resolving contradictions, pruning low-value chunks, and closing knowledge gaps
- Capture human decisions as structured decision traces and distill them into context graphs that make AI agents self-improving over time
What is Context Engineering and ContextOps?
Context engineering is the practice of filling an AI agent's context window with precisely the right information for each step of its work. While everyone has access to the same foundation models, the quality and relevance of the context those models receive determines the quality of their output.
ContextOps extends this further: just as DataOps brought operational discipline to data pipelines, ContextOps covers the ongoing processes that keep context accurate and fresh over time such as detecting staleness, resolving contradictions, and capturing human decisions as reusable precedent. Together, they're how data teams turn existing pipelines and institutional knowledge into a durable competitive advantage for their AI applications.
