5 Context-Engineering Principles for Production AI

This post introduces five production-focused principles for context engineering in AI agents: 1. Budgeting cost per entire agent run, not per turn, highlighting how cumulative input costs grow and the role of semantic caching (e.g., Redis LangCache) in reducing inference costs. 2. Filtering tool outputs at ingestion to prevent bloat and context poisoning, suggesting strategies like offloading large responses, truncating noisy content, and clearing stale results. 3. Layering static (instructions, persona) and dynamic (retrieved data, history) context, advocating for a unified data layer (e.g., Redis Iris) to manage these distinct layers coherently. 4. Treating retrieval as a budget decision, emphasizing the 'lost in the middle' problem and the damage caused by 'hard negatives,' and detailing Redis's support for various index types (FLAT, HNSW, SVS-VAMANA) and hybrid retrieval. 5. Recognizing that context failures are often invisible to standard evaluations, implying the need for more robust testing methodologies.