TL;DR
Dropbox shares how consolidating tools, filtering retrieval results, and using specialized agents improves LLM reasoning while reducing token overhead.
Key Points
- Replaced multiple retrieval APIs with single unified Dash Search index to reduce model decision overhead
- Implemented knowledge graph layering to rank and filter results, ensuring only relevant context reaches the model
- Separated complex query construction into dedicated search agent, freeing main planning agent for higher-level reasoning
- Found that leaner contexts improve both performance and cost while reducing analysis paralysis in agentic workflows
Why It Matters
As LLM-based systems scale to handle more complex tasks, context engineering becomes critical for performance and cost. These patterns—tool consolidation, relevance filtering, and agent specialization—directly address the token efficiency and reasoning quality challenges developers face when building production agentic AI systems.
Source: dropbox.tech