Coding agents have shown that a powerful LLM can do incredible things autonomously when equipped with tools that help it understand the code context. While coding agents operate effectively in static, deterministic environments like a disk's file system, data agents introduce an entirely new paradigm. Data agents work within a dynamic, constantly evolving data lakehouse that encompasses a wealth of semantic context across hundreds of thousands of tables, notebooks, dashboards, and documents.

For example, consider a real (anonymized) query asked by an internal user in Figure 2: the user notices that two enterprise dashboards reporting the same product's revenue show contradictory spikes on different dates and asks the agent to explain why. This reasonable question is deceptively hard because no single data source contains the answer and resolving the question requires cross-system discovery across tables, internal documents, and dashboards, and reasoning about how multi-day reports are set up. Additionally, it requires the agent to dig into enterprise pricing details to find contract rates. Finally, it requires the agent to have an ability to automatically correct itself when intermediate calculations reveal incorrect initial assumptions. The figure shows how the agent is able to successfully solve the task by proceeding in different phases: (1) parallel multi-agent data discovery, (2) data investigation, (3) self-correction loop, and (4) verification.

Figure 3 shows some of the key technical innovations in Genie that enable it to perform significantly better than generic coding agents, namely: i) Specialized Knowledge Search, ii) Parallel Thinking, and iii) Multi-LLM. Specialized knowledge search uses semantic contextual data to ground the asset discovery sub-agents and significantly improve the search quality. Parallel thinking allows the agent to sample multiple different trajectories and then aggregate the findings across trajectories to compute the final answer. Finally, Multi-LLM allows the agent to use different LLMs for each of the different sub-agents together with their optimized prompts to further improve the overall accuracy and latency.

Genie uses the existing data assets such as workspace tables, notebooks, dashboards, documents, and files to derive a rich semantic enterprise context and then uses this context to construct a search index. It uses multiple search indices in parallel together with rich metadata signals to efficiently discover most relevant assets for a user query. Figure 4 demonstrates how leveraging the specialized knowledge search helps Genie improve table search performance by up to 40% on our table discovery benchmarks.

Unlike software engineering tasks, where coding agents can first write tests to verify the desired functionality and then iterate on code generation until the tests pass, the open-ended data queries don't have such corresponding unit tests. In the absence of tests, it becomes challenging for data agents to know if the generated answer is correct or needs more refinement. To address this challenge, we leverage parallel thinking by sampling multiple trajectories and aggregating relevant information across the trajectories to compute the final answer. Figure 5 shows how parallel thinking can significantly improve the answer accuracy, although with some additional latency and token costs. Furthermore, as shown in Figure 1, combining Multi-LLM and further optimizations can further significantly reduce costs and latency.