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The benefit? No more manual E2E testing. No more slow QA cycles. No more bugs reaching production. With QA Wolf, Drata’s team of 80+ engineers achieved 4x more test cases and 86% faster QA cycles. Disclaimer: The details in this post have been derived from the official documentation shared online by the Dropbox Engineering Team. All credit for the technical details goes to the Dropbox Engineering Team. The links to the original articles and sources are present in the references section at the end of the post. We’ve attempted to analyze the details and provide our input about them. If you find any inaccuracies or omissions, please leave a comment, and we will do our best to fix them. Modern knowledge work doesn’t suffer from a lack of information. It suffers from too much of it, scattered across too many tools and media. For example, emails, documents, chats, project trackers, and meeting notes. Each tool solves a narrow problem, but together they create friction that slows teams down and increases operational risk. Three things tend to break:
Dropbox built Dash to untangle this mess, not by replacing individual tools, but by giving users a way to cut across them. Dash acts as a unified search and knowledge layer that sits on top of emails, files, chats, calendars, and other data sources. At a high level, Dash provides:
In this article, we look at how Dropbox leveraged RAG and AI Agents to make Dash a reality.
Core ChallengesBuilding an AI product for consumer use is one thing. Building it for business environments introduces another layer of complexity entirely. Three core challenges stand out:
Retrieval Augmented Generation (RAG)Large Language Models (LLMs) are powerful, but they have a short memory. Out of the box, they guess based on training data and often hallucinate details when context is missing. Retrieval-Augmented Generation, or RAG, fixes this by grounding generation in real documents. Here’s how it works:
This two-step setup adds discipline to the generation process. The model can reference real data instead of improvising. In business settings, where incorrect answers can cause reputational or legal problems, this is a critical requirement. See the diagram below that shows how RAG works on a high level:
RAG matters in the enterprise because answers reflect real content, not just what the model “remembers.” Context stays fresh, and updates in connected systems reflect in responses as soon as the underlying index is refreshed. The model is limited to generating from what it retrieved, without assumptions. However, the retrieval component makes or breaks a RAG pipeline. It controls what the LLM sees and how fast it sees it. Three metrics define success:
However, some trade-offs have to be considered:
Dash avoids a one-size-fits-all solution. It uses a hybrid retrieval strategy that blends traditional lexical information retrieval to match keywords quickly. There is on-the-fly chunking to extract only the most relevant text from documents at query time and semantic reranking using embeddings to re-order results by meaning, not just keyword match. The goal is to deliver high-quality results in under 2 seconds for over 95 percent of queries. This balance keeps latency acceptable while maintaining accuracy and context fidelity. After retrieval, the LLM still needs to generate clear, accurate responses from the retrieved data. For this purpose, Dropbox evaluated several models across different types of questions using public benchmarks:
Evaluation was based on custom metrics, such as:
The pipeline is intentionally model-agnostic and supports swapping in different LLMs, whether open-source or commercial, depending on evolving capabilities, licensing terms, or customer preferences. AI AgentsRetrieval-Augmented Generation works well when the task is simple: ask a question, get a grounded answer. However, business workflows rarely look like that. More often, they involve a chain of decisions, conditionals, and context-specific logic. That is where AI agents take over. An AI agent is not just a smarter chatbot. It is a system that can break down a user query into multiple sub-tasks and execute those sub-tasks in the right order or in parallel to return a structured, final result that solves the actual request. See the diagram below to understand how AI agents work:
Dropbox agents operate in two main phases: planning and execution. Stage 1: Planning the TaskThe agent starts with a query from the user. It passes that input to an LLM, which translates it into a sequence of logical steps expressed in code. This code is written in a Python-like domain-specific language (DSL) designed specifically for agent planning. For example, the request “Show me the notes for tomorrow’s all-hands meeting” gets broken down as:
Stage 2: Executing the PlanOnce the plan is ready, the system validates it through static analysis. This step checks the code for missing functions, incorrect types, and possible logic errors before anything runs. If the LLM generated a reference to a helper function that does not yet exist, the system can loop back and generate that function on demand. Execution happens inside a custom-built interpreter created by Dropbox.
The Need for a Custom InterpreterRunning arbitrary Python code from an LLM introduces serious risks, from security vulnerabilities to unexpected behavior. That’s why the Dropbox team built a custom interpreter. The Dropbox interpreter avoids these issues by:
This level of structure makes the system reliable and testable. Engineers can track which exact step failed, why it failed, and whether the underlying helper functions are behaving as expected. Lessons and TakeawaysSome key lessons are as follows:
ConclusionBuilding Dash forced a deeper understanding of how AI fits into real business workflows. Retrieval alone doesn’t cut it when tasks grow complex. Agents add structure, determinism, and execution logic where generative models fall short. However, this is only a starting point. Future work pushes the boundary further. Some of the directions being explored are as follows:
The goal is to move from static Q&A toward adaptive, assistant-like systems that understand context, evolve, and respond intelligently across the full spectrum of knowledge work. Each step forward improves not just the answers AI can give, but how well it understands the work itself. References: ByteByteGo Technical Interview Prep KitLaunching the All-in-one interview prep. We’re making all the books available on the ByteByteGo website.
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