How Dash uses context engineering for smarter AI (opens in new tab)

Dash evolved from a traditional RAG search system into an agentic AI that can interpret information, plan tasks, and act on users’ behalf. Dropbox’s experience shows that better agent performance comes not from adding more tools and data, but from carefully engineering context: limiting choices, filtering for relevance, and delegating complex work to specialized agents. The central conclusion is that precise, timely context improves reasoning speed, accuracy, and efficiency.

From Search to Agentic AI

• Dash initially combined semantic and keyword search to retrieve documents and generate concise answers.
• Users began asking it to interpret, summarize, and act on retrieved information.
• This required Dash to plan and execute multi-step tasks rather than simply search and summarize.
• The resulting challenge was determining which information and tools the model actually needed at each stage.

The Cost of Too Many Tools

• Every tool adds descriptions and parameters to the model’s context window.
• More tools expand the model’s decision space, potentially causing slower or less reliable choices.
• Tool definitions also consume tokens, increasing cost and reducing room for reasoning.
• Longer-running tasks suffered from “context rot,” where accumulated tool-call information degraded accuracy.
• Model Context Protocol (MCP) standardizes tool descriptions, but does not eliminate the problem of excessive context.

Limiting Tool Definitions

• Dash found that exposing retrieval tools from many services—such as Confluence, Google Docs, and Jira—created confusion.
• Instead of requiring the model to choose among numerous APIs, Dash consolidated retrieval into one purpose-built tool backed by its universal search index.
• A single retrieval interface:
  • Simplifies planning
  • Reduces tool-selection errors
  • Keeps the context window focused
  • Provides consistent access across connected services
• The same principle shaped Dash’s MCP server, which exposes retrieval through one lean tool to applications such as Claude, Cursor, and Goose.

Filtering Context for Relevance

• Retrieved information is not automatically useful for the task at hand.
• Dash combines data from multiple sources in a unified index and uses a knowledge graph to connect people, activity, and content.
• These relationships help rank results according to the query and the user’s context.
• By filtering results before presenting them to the model, Dash ensures that each piece of supplied context is relevant.
• Precomputing the index and graph allows runtime retrieval to remain fast and focused.

Using Specialized Agents for Complex Tasks

• Some tools require substantial instructions and examples to use correctly.
• Dash Search became complex because query construction involves:
  • Understanding user intent
  • Mapping intent to index fields
  • Rewriting queries for semantic matching
  • Handling typos, synonyms, and implicit context
• Adding these instructions directly to the main planning agent consumed context that could otherwise support broader reasoning.
• Dash therefore moved search into a specialized agent:
  • The main agent decides when searching is necessary.
  • The search agent independently constructs the query using its dedicated prompt.
  • This division lets the main agent focus on the overall task while the specialist handles search details.

Dash’s approach recommends treating context as a limited engineering resource. Use a small number of well-designed tools, pre-filter information for relevance, and delegate technically demanding subtasks to specialized agents rather than overwhelming one general-purpose model.