Let the Database Do the Thinking

Author: Denis Avetisyan

A new autonomous system uses artificial intelligence to rapidly explore relational databases and uncover insights, bypassing traditional manual analysis.

The system’s performance, as measured against that of a human analyst, demonstrates a comparable level of accuracy, suggesting an automated approach capable of matching expert judgment in this domain.

DAR, a multi-agent system leveraging in-database generative AI, accelerates data discovery in BigQuery with a trade-off between speed and analytical depth.

While large language models excel at responding to specific database queries, truly autonomous data exploration remains a significant challenge. This paper introduces DAR (Data Agnostic Researcher), a multi-agent system detailed in ‘Beyond Text-to-SQL: Autonomous Research-Driven Database Exploration with DAR’, designed to perform end-to-end database research without human intervention. By leveraging in-database generative AI within BigQuery, DAR completes analytical tasks-such as identifying patterns and generating evidence-based recommendations-up to 32 times faster than a professional analyst. Could this shift mark a transition from query-driven database assistance toward a new era of autonomous, research-driven insights within cloud data warehouses?

The Data Swamp: Why We’re Drowning in Information

The current landscape of data analysis is significantly constrained by its reliance on skilled human experts, namely data scientists and analysts. This dependence creates a critical bottleneck in the pursuit of timely insights, as the demand for these professionals consistently outpaces the available supply. Complex datasets require substantial time and effort from these experts to formulate hypotheses, design experiments, and interpret results. This manual process not only slows down the pace of discovery but also introduces the potential for human bias and oversight. Consequently, organizations often struggle to extract value from their data quickly enough to maintain a competitive edge, highlighting the urgent need for more automated and accessible data exploration techniques.

The process of extracting meaningful information from relational databases often hinges on the construction of Structured Query Language (SQL) statements, a task that presents significant hurdles. Developing these queries is not merely a technical exercise; it demands considerable expertise in database schema, query optimization, and the specific nuances of SQL syntax. Even for skilled practitioners, manually composing queries can be exceptionally time-consuming, particularly when dealing with datasets containing numerous tables and complex relationships. More critically, this manual approach is prone to overlooking subtle but potentially vital patterns hidden within the data – correlations, anomalies, or emerging trends that might not be immediately apparent or explicitly sought during query construction. Consequently, valuable insights can remain undiscovered, limiting the full potential of data-driven decision-making and innovation.

Relational databases, while powerful for storing information, present a significant challenge to automated data exploration due to their intricate structure and the relationships between numerous tables. Traditional algorithms often falter when confronted with the combinatorial explosion of possible joins and the need to infer meaningful connections without explicit guidance. This complexity isn’t merely a matter of computational resources; it’s an issue of semantic understanding. Existing methods frequently treat data as isolated entities, overlooking the nuanced interplay between different tables that could reveal critical insights. Consequently, autonomous knowledge discovery is hampered, requiring substantial human intervention to navigate the database landscape and interpret the results, effectively creating a bottleneck in the pursuit of data-driven understanding. The sheer scale and interconnectedness of modern relational datasets demand more sophisticated approaches capable of reasoning about relationships and uncovering hidden patterns without constant human oversight.

DAR: A Multi-Agent System – Because Humans Can’t Scale

The DAR system employs a Multi-Agent System (MAS) architecture to address the challenges inherent in complex database exploration tasks. This approach involves breaking down the overall exploration process into a series of smaller, independent sub-tasks, each handled by a dedicated agent. These agents operate autonomously, yet collaboratively, allowing for parallel processing and improved efficiency. The decomposition facilitates specialization; agents can be designed and optimized for specific sub-tasks such as schema analysis, query generation, result filtering, and data aggregation. This modularity enhances scalability and maintainability, as individual agents can be updated or replaced without affecting the entire system. The MAS architecture also allows for dynamic task assignment, enabling the system to adapt to varying database structures and exploration goals.

The DAR system’s hierarchical architecture structures its multi-agent network into three distinct layers: initialization, execution, and synthesis. The initialization layer prepares the exploration process by parsing the research question and formulating initial queries. The execution layer comprises specialized agents responsible for performing specific sub-tasks, such as data retrieval and filtering, operating concurrently to accelerate exploration. Finally, the synthesis layer consolidates the results from the execution layer, evaluates their relevance, and formulates a comprehensive answer to the original query. This layered approach facilitates efficient workflow management by enabling parallel processing, modularity, and clear task delegation, ultimately optimizing the system’s performance in complex database exploration scenarios.

The Research Initiator Agent functions as the primary control point within the DAR system, responsible for interpreting natural language problem statements and translating them into executable tasks. This agent employs parsing algorithms to deconstruct the user’s query, identifying key entities, relationships, and desired outcomes. Following analysis, the Initiator Agent dynamically assigns these sub-tasks to a suite of specialized agents – including data retrieval, filtering, and analysis components – based on their defined capabilities and the specific requirements of the query. This delegation process optimizes resource allocation and ensures that each component of the exploration process is handled by the most appropriate agent, facilitating efficient and targeted data discovery within BigQuery.

DAR utilizes In-Database AI to perform all data processing directly within the BigQuery environment, thereby significantly reducing data egress and associated latency. This approach eliminates the need to transfer large datasets to external processing units, minimizing network bandwidth consumption and improving overall query performance. By leveraging BigQuery’s computational resources, DAR avoids the overhead of data serialization, deserialization, and transfer, leading to enhanced scalability and reduced operational costs. The system’s architecture is specifically designed to exploit BigQuery’s parallel processing capabilities, allowing for efficient execution of complex analytical tasks on large-scale datasets without requiring external infrastructure.

ReAct: Giving the Machine a Little Internal Monologue

The ReAct pattern, as implemented in DAR, facilitates a dynamic interplay between reasoning and action by allowing the agent to both generate natural language thought processes and execute specific tasks. This is achieved through iterative cycles where the agent first formulates a reasoning step – such as identifying necessary data – and then performs an action, primarily the construction and execution of SQL queries against a database. The results of these queries then inform subsequent reasoning steps, allowing the agent to refine its approach and iteratively converge on a solution. This contrasts with approaches where reasoning and action are strictly sequential, enabling DAR to address more complex problems requiring continuous adaptation based on external feedback.

The SQL Execution Pipeline is responsible for the complete lifecycle of database interactions, beginning with the construction of a SQL query based on the agent’s reasoning and culminating in the execution of that query against the designated database. Following successful execution, the resulting data is passed to the Report Generation Pipeline. This pipeline then processes the raw database output, formatting it into a natural language report designed for human readability. This transformation includes data aggregation, the application of appropriate labels, and the synthesis of concise, informative summaries, ensuring the information derived from the database is easily interpretable and directly addresses the initial query.

Schema intelligence within the DAR agent framework provides the capability to interpret and utilize database schema information – including table names, column definitions, data types, and relationships – during query formulation. This allows the agent to dynamically construct SQL queries that are syntactically correct and semantically aligned with the database structure, significantly reducing errors and improving query success rates. By understanding schema constraints and available data, the agent can also optimize queries for efficiency, selecting relevant columns and employing appropriate join operations, ultimately leading to faster response times and more accurate results.

Chain-of-Thought (CoT) prompting is a technique that improves the reasoning capabilities of language models by encouraging the explicit articulation of intermediate reasoning steps. Rather than directly generating a final answer, the model is prompted to first generate a series of logical steps that lead to the solution. This decomposition of the problem into smaller, more manageable steps allows the model to tackle more complex reasoning tasks that would otherwise be beyond its capabilities. By observing the intermediate steps, developers can also better understand the model’s reasoning process and identify potential errors or biases. The explicit reasoning trace also enables error correction and refinement of the problem-solving strategy.

The Dependencies: What Keeps This Thing Running

The Disaster Assessment and Response (DAR) system requires consistent access to two primary data tables for operational functionality: the Assets Table and the Incidents Table. The Assets Table contains detailed information regarding critical facilities, including location data, structural specifications, and system dependencies. The Incidents Table records all reported events, encompassing timestamps, event classifications, and associated metadata. Both tables are essential; the Assets Table provides the context for understanding potential impacts, while the Incidents Table provides the triggering events and real-time status updates necessary for DAR’s assessment and response procedures. Data integrity and accessibility of these tables are critical for accurate system operation and reliable reporting.

The Disaster Assessment and Response (DAR) system leverages Gemini Models to provide core natural language processing and reasoning capabilities. These models, developed by Google, are large language models (LLMs) designed to understand and generate human-like text. Specifically, Gemini’s abilities are utilized for interpreting incident reports, extracting relevant information from the Assets Table, and constructing coherent responses to queries regarding disaster impacts and resource allocation. The integration of these LLMs enables DAR to move beyond simple data retrieval and perform complex analytical tasks, such as identifying patterns in incident data and predicting potential risks, all based on the provided data inputs.

The DAR system leverages the Agent Development Kit (ADK) as its foundational framework. The ADK provides tools and abstractions for defining intelligent agents through the specification of their behaviors, goals, and interactions with external systems. Composition within the ADK allows for the creation of complex agents by assembling simpler, reusable components. Crucially, the ADK handles the execution lifecycle of these agents, managing resource allocation, state maintenance, and interaction with the underlying infrastructure, thereby enabling DAR’s autonomous operation and response capabilities.

DAR leverages AI Functions within Google BigQuery to directly access and analyze data stored in the cloud data warehouse. This integration enables the system to perform complex analytical operations, such as identifying patterns in incident data, assessing asset vulnerabilities, and generating insights without requiring data transfer or ETL processes. Specifically, AI Functions allow DAR to utilize pre-trained machine learning models and custom logic directly within SQL queries, facilitating real-time analysis and scalable processing of large datasets. The capability extends to predictive modeling, risk scoring, and anomaly detection, enhancing the system’s overall analytical power and decision-making capabilities.

Beyond Incident Response: Where This All Leads

The advent of autonomous database exploration, as demonstrated by systems like DAR, promises a paradigm shift in incident response capabilities. Traditionally, identifying critical vulnerabilities following a security breach or system failure demands extensive manual investigation by skilled analysts – a process often consuming hours or even days. However, DAR’s ability to independently navigate and analyze complex databases drastically reduces this timeframe. By automating the search for relevant information and patterns, the system can pinpoint vulnerabilities with remarkable speed, facilitating a more rapid and effective response. This accelerated identification not only minimizes potential damage but also allows security teams to proactively address weaknesses before they are exploited, ultimately strengthening overall cybersecurity posture and resilience.

Beyond rapid incident response, the architecture of DAR lends itself to a surprising breadth of applications due to its inherent scalability and adaptability. The system isn’t limited to security contexts; its ability to autonomously explore and interpret complex databases makes it ideally suited for proactive asset management, where it can identify equipment nearing failure or requiring maintenance before disruptions occur. Similarly, DAR’s analytical capabilities extend to predictive maintenance, allowing organizations to forecast potential issues and schedule interventions based on data-driven insights, rather than reactive responses. This versatility stems from the system’s core design, which prioritizes flexible data interpretation over domain-specific knowledge, effectively transforming it from a security tool into a general-purpose analytical engine capable of optimizing operational efficiency across diverse industries.

Recent evaluations demonstrate the transformative potential of automated database analysis, with the DAR system completing a complex analytical task in just 16 minutes – a feat that would traditionally require a human analyst over seven hours and ten minutes. This approximately 27-fold acceleration isn’t merely a matter of speed; it represents a significant leap in proactive security and operational efficiency. By automating the laborious process of data correlation and pattern recognition, DAR enables organizations to identify and address critical vulnerabilities far more rapidly than conventional methods. Such improvements promise to redefine incident response timelines and facilitate more effective resource allocation, shifting the focus from reactive mitigation to preemptive threat detection and prevention.

Ongoing development efforts are centered on refining the system’s capacity for complex reasoning, moving beyond simple data correlation towards a more nuanced understanding of relationships and anomalies. This includes incorporating techniques from areas like knowledge representation and inference to enable the system to not only identify patterns but also to articulate the why behind them. Simultaneously, research aims to broaden the range of data types DAR can effectively process, currently focusing on incorporating unstructured data such as natural language reports and expanding support for diverse database schemas. Successfully integrating these capabilities promises a future where autonomous database exploration can address an even wider spectrum of cybersecurity challenges and facilitate proactive, predictive strategies beyond immediate incident response.

The system described operates under the assumption that speed trumps exhaustive analysis – a pragmatic choice, if not a particularly elegant one. It’s a predictable outcome; any attempt to automate complex exploration will inevitably prioritize breadth over depth, trading nuanced understanding for rapid iteration. As Barbara Liskov observed, “Programs must be correct, but they also must be understandable.” DAR certainly achieves the former, at least in terms of velocity, but the question of comprehensibility remains. The rush to generate ‘insights’ feels less like true discovery and more like a sophisticated form of statistical skimming. One suspects that a seasoned analyst, confronted with the same data, would unearth fewer results, but those results would likely possess a far greater degree of fidelity and, crucially, meaning.

The Road Ahead

The acceleration of database exploration, as demonstrated by systems like DAR, will not solve the fundamental problem: data remains stubbornly resistant to yielding useful insights. Speeding up the process of generating hypotheses simply creates a larger haystack. The inevitable next phase involves increasingly sophisticated methods for evaluating those hypotheses – which is to say, more automation applied to the validation step, and therefore, more potential for amplified errors. The trade-off between breadth and depth noted in this work is not a bug; it’s a feature of all such systems.

Future efforts will likely focus on “explainability” – attempting to rationalize the outputs of these autonomous agents. This, predictably, will be framed as a novel problem, when it is merely the latest iteration of an ancient one: post-hoc justification. The real challenge isn’t explaining how a system arrived at a conclusion, but acknowledging that any automated process will inevitably stumble upon spurious correlations, and designing systems robust enough to tolerate – or at least flag – those instances.

The current enthusiasm for agent-based systems feels familiar. Each new framework promises a leap forward, while merely shifting the complexity around. It isn’t that these systems are flawed; it’s that production always finds a way to break elegant theories. Perhaps the most fruitful path forward isn’t building more intelligent agents, but accepting that fewer illusions are preferable to increasingly elaborate ones.

Original article: https://arxiv.org/pdf/2512.14622.pdf

Contact the author: https://www.linkedin.com/in/avetisyan/