Introduction
In today’s fast-paced business environment, executives and CXO’s are faced with an overwhelming volume of data scattered across various systems: dashboards, ticket-management platforms, customer relationship management tools, and document repositories. These leaders are tasked with making strategic decisions quickly, but the challenge lies in synthesizing insights from these disparate data sources without dedicating countless hours to manually searching, correlating, and analyzing them.
Traditionally, senior executives would need to sift through reports and dashboards, or rely on data teams to provide them with tailored insights. However, this manual process is time-consuming, error-prone, and often too slow to meet the demands of dynamic business environments. CXOs need data-driven decisions, but they lack the time to wade through mountains of raw data to extract actionable insights.
Enter the CXO Agent: a solution designed to allow executives to ask questions in plain language and receive precise, actionable answers, without having to navigate the complexities of underlying data sources. By querying across a wide variety of integrated systems—from databases and CRMs to ticketing tools and documents—the agent intelligently sources and correlates the right information, delivering responses that are both relevant and contextually rich. The only caveat is that the agent ensures secure access to data by respecting existing role-based access control (RBAC) protocols. This means that users only see the data they are authorized to view, maintaining data privacy and compliance at all times.
This solution takes full advantage of cloud-native technologies and AWS services, leveraging the scalability, security, and flexibility of the cloud to ensure that the agent can handle vast data sets and integrate seamlessly with existing enterprise infrastructure. By democratizing access to actionable insights, this agent empowers CXOs to make data-driven decisions with confidence and agility, all while ensuring that security and data integrity are upheld.
In this blog, we will walk through the architecture and functionality of this intelligent agent, discuss the AWS services that power it, explore the technical challenges faced during its development, and highlight a real-world case study that showcases the tangible benefits of the solution for senior decision-makers.
How the Agent Works
At its core, the agent serves as an intelligent interface between executive intent and enterprise data. It transforms natural language queries into data-driven, actionable insights by dynamically accessing and correlating information across a wide array of internal systems. This includes structured sources such as ticketing platforms and business dashboards, as well as unstructured data housed in document repositories.
The process begins when a user—typically an executive—poses a high-level question in natural language. This query is first interpreted by an LLM, which decomposes it into an execution plan. This plan identifies the types of data required to answer the question, as well as the specific sources to retrieve that data from.
The agent then performs a permissions check to determine which of the required data sources the user is authorized to access. This check leverages existing enterprise RBAC policies, ensuring that the agent never returns information from unauthorized systems. This RBAC-driven access control model is critical in preserving organizational data boundaries and complying with internal security protocols.
Once access is confirmed, the agent retrieves relevant information using two main methods.
- Retrieval-Augmented Generation (RAG) is used to query document-based or semi-structured data sources. This allows the agent to pull contextually relevant excerpts from documents such as business reports, meeting minutes, and technical specifications.
- Tool-based API calls are used for structured systems such as ticketing platforms, analytics dashboards, and customer support tools. These tools provide up-to-date, system-generated data that can be incorporated into the final answer.
The outputs are then synthesized into a unified response. Depending on how the question is phrased, the agent can return:
- concise text summaries for quick insights;
- charts or visualizations embedded in the interface;
- strategic recommendations where applicable; or
- downloadable reports in PDF format for sharing or record-keeping.
This architecture ensures that users can move seamlessly from question to insight without needing to understand where the data comes from or how it is queried, all while staying within the bounds of their access permissions. The result is a highly efficient decision-support tool that meets executives where they are, in both language and context.
The Technology
The architecture behind the agent is designed for scalability, modularity, and secure data access across heterogeneous enterprise systems. Built entirely on AWS, the solution leverages managed services to reduce operational overhead and ensure performance at scale, while retaining the flexibility to orchestrate complex, cross-system queries through a custom logic layer.
At a high level, the system comprises the following core components.
- A frontend interface, where executives input natural language questions and view results — including summaries, visualizations, and downloadable reports.
- A load balancer and API server that routes incoming requests, manages authentication, and enforces request limits and timeouts.
- MCP servers (Model Context Protocol), which handle tool-specific logic, including integration with third-party APIs and custom connectors for enterprise systems such as dashboards and ticketing platforms.
- Bedrock runtime, which is accessed via boto3 to run the large language model responsible for interpreting queries, generating data source access plans, and synthesizing final responses.
- A database for storing metadata, audit logs, user preferences, and response history.
- Blob storage for storing document extracts, generated PDFs, and intermediate results.
AWS Services
The deployment is centered around a tightly integrated AWS architecture.
- Amazon EKS provides the orchestration layer for hosting the API server, MCP servers, and related microservices. EKS enables autoscaling, containerized deployments, and efficient allocation.
- Amazon S3 is used as the primary object storage layer—both for ingesting documents for RAG queries and for serving generated assets (charts, reports, PDFs).
- Amazon Bedrock serves as the LLM backbone. Through direct boto3 integration, the API server sends structured prompts to Bedrock for query decomposition, summarization, and content synthesis. Using Bedrock allows the system to scale AI workloads without managing infrastructure or finetuning models in-house.
- Amazon OpenSearch Service supports fast document retrieval for RAG queries. Indexed documents are pre-processed and chunked with vector embeddings to enable semantic search during runtime.
LLM Integration
The LLM workflow is orchestrated entirely through native code. When a question is submitted, the agent passes the prompt to Bedrock, where the LLM performs the following.
- Intent parsing and data source planning: Identifying which MCPs or document indices are required.
- Access verification: checking if the user, authenticated via SSO, is authorized to query those sources.
- Execution planning: Choosing between API-based data retrieval (via MCPs) or document-based RAG retrieval from OpenSearch and Bedrock Knowledge Bases.
Once the data is gathered, the LLM composes a human-readable response, optionally including charts or strategic recommendations based on the nature of the query.
Data Access and Security
All users authenticate via enterprise SSO, and their roles are managed by an internal RBAC system. Before any data access is granted, the agent performs a real-time role check to ensure that the user is authorized to access the requested systems and data types. This RBAC check is enforced both at the orchestration level and within individual MCPs.
Credentials for third-party systems are stored securely, and access tokens are managed via short-lived secrets or scoped API keys, depending on the integration. No data is ever accessed or shown unless the user has explicit permissions, maintaining strict adherence to data governance and privacy requirements.
Scalability and Fault Tolerance
The system is designed to be serverless and autoscaling. All workloads—including LLM calls, MCP-execution, and API-handling—are horizontally scalable. Failures (e.g., timeouts, tool unavailability, permission denials) are caught and reported clearly to the user, ensuring transparency without breaking the experience. Retry logic with upper limits is built in for transient errors, and results are cached where possible to reduce response times and avoid redundant calls.
Challenges Faced
1. Data Integration Across Heterogeneous Sources
Integrating data from a variety of sources—including documents, databases, APIs, and ticket-management systems—posed one of the first challenges. Each source comes with its own data format, access method, and structure, ranging from structured, tabular data to unstructured text documents. This diversity meant that we had to design a solution capable of handling these various types of data seamlessly. In addition, real-time data access and retrieval added another layer of complexity, as many systems required on-the-fly queries or interactions.
To tackle this, we introduced MCP servers as a centralized integration layer. Each MCP server was designed to interface with specific data sources, such as CRMs, ticketing platforms, or document repositories. These servers provided a common, standardized interface for data retrieval and processing, which simplified the overall integration process. By consolidating these connections through MCPs, we ensured that data from diverse sources could be accessed and processed in a consistent manner, making it easier to generate relevant insights on demand.
2. Natural Language Processing (NLP) and Intent Recognition
Understanding and interpreting the variety of queries posed by executives was another significant challenge. The queries were often high-level, multi-faceted, and phrased in different ways, making it difficult to parse and correctly interpret user intent. Executives may ask vague or complex questions that require a deep understanding of the context, often in the form of natural language with ambiguous phrasing. The challenge was not just interpreting the question but ensuring that the response was both relevant and actionable.
To overcome this, we implemented a feedback loop that allowed the system to clarify ambiguous queries. When the LLM was uncertain about the user’s intent, it would ask follow-up questions to narrow down the scope and ensure that the query was properly understood. By presenting multiple interpretations of the question, the system could offer the user an opportunity to confirm or refine their request. This approach helped mitigate the risk of providing inaccurate or incomplete answers, improving the accuracy of responses over time.
3. Role-Based Access Control (RBAC) and Data Security
Data security and access control were critical concerns, especially given the sensitive nature of the data involved. Executives and other users required access to different sets of data based on their roles within the organization. However, ensuring that only authorized users could query and view specific data sources without compromising security posed a significant challenge. We needed a dynamic system that could evaluate the user’s permissions in real-time, preventing unauthorized data access.
To address this, we implemented a robust RBAC system that dynamically assessed each user’s role and determined their data access rights before any query was executed. We relied on AWS Cognito for authentication and IAM roles to enforce access policies across various data sources. This approach ensured that only authorized users could access certain data sets, while unapproved requests were blocked automatically. Additionally, by using scoped, time-limited access tokens for each data source, we maintained security without introducing performance bottlenecks or unnecessary delays.
4. Query Understanding and Multi-Source Data Synthesis
The need to synthesize information from multiple data sources in response to a single query presented another significant challenge. Executives often ask questions that span several systems, such as combining ticketing data with CRM insights or integrating document-based information with real-time analytics. In these cases, the system needed to merge data from disparate sources into a single, coherent response. This was difficult because the data was often formatted differently, came from multiple platforms, and required complex aggregation to generate meaningful insights.
We solved this by implementing context-aware algorithms that intelligently identified which data sources were relevant based on the context of the user’s question. These algorithms allowed the agent to pull in data from multiple sources and combine it in a way that made sense. For example, if a user asked about the status of a project, the system could simultaneously pull ticket data, CRM information, and relevant documents to generate a comprehensive response. This approach ensured that even when dealing with complex, multi-source queries, the agent could provide a unified and actionable answer.
A Case Study: the CXO Agent for a Multinational Manufacturing Company
To demonstrate the power and versatility of our data insight agent, we implemented a specialized version of the solution for a large, multinational manufacturing company. This company faced significant challenges when it came to managing and interpreting data from several key sources that directly impacted operations and decision-making. These included observability data (from Service Now), cloud spend data (tracked through our own tool, Kiosk), and high-performance computing (HPC) data (monitored by our product, Tachyon).
The company’s executives and senior management needed a seamless way to get answers to complex questions that spanned across these multiple data sources. Previously, manually correlating data from Service Now, Kiosk, and Tachyon was a cumbersome and time-consuming process. To empower the company’s leadership with faster, more actionable insights, we created a custom version of our agent that could synthesize information from these three distinct sources, offering both text-based summaries and visual reports.
Data Sources and Their Roles
Service Now (Observability Data)
Observability data provided by Service Now was critical for understanding IT operations, service disruptions, and infrastructure health. The company needed a way to quickly identify issues in real time and assess their impact on ongoing projects. With the agent, executives could query Service Now data for insights on system outages, incident timelines, and root cause analysis.
Kiosk (Cloud Spend Data)
Managing cloud spend was an ongoing concern for the company. With the growing scale of their cloud infrastructure, keeping track of costs in real-time was essential for budget management. Kiosk, our product for tracking cloud expenditures, was integrated into the agent to give leaders a clear, real-time overview of their cloud spend, broken down by department, service, or region. This allowed executives to quickly pinpoint areas where cloud costs were increasing and take corrective actions.
Tachyon (High-Performance Computing Data)
Tachyon, our HPC product, was used to monitor and track workloads in the company’s high-performance computing environment. The data generated by Tachyon was crucial for understanding the utilization of computing resources and ensuring that workloads were balanced efficiently across the system. Executives could use the agent to query information about the current load, bottlenecks, and resource utilization patterns, helping them make informed decisions about capacity planning and resource allocation.
Query Examples and Outputs
One of the key benefits of the agent was its ability to handle complex, multi-source queries. For example, an executive might ask, “What is the current impact of the ongoing IT outage on our cloud spend and HPC resource utilization?”
This query would require the agent to pull real-time observability data from Service Now, cloud cost data from Kiosk, and HPC performance data from Tachyon. The agent would then synthesize this information into a single, coherent response.
Alternatively, the agent could also be used for regular performance checks, such as, “What was the total cloud spend this week, and how does it compare to last week’s cloud usage?”
This query would involve pulling data from Kiosk for cost analysis and comparing it with historical trends. The output would consist of a text summary highlighting key trends, along with a bar graph that compares this week’s spending to previous periods.
Another example could be a question like, “What are the current bottlenecks in our HPC environment, and how are they affecting ongoing workloads?”
For this, the agent would pull performance data from Tachyon to assess resource usage and identify any inefficiencies. The output would include a detailed summary, possibly complemented by a line graph showing resource utilization over time.
Challenges Overcome in This Implementation
Despite the success of the agent, this specific implementation also presented unique challenges. Integrating data from such varied sources required ensuring that the agent could interpret and correlate data across different domains—service management, cloud economics, and high-performance computing. In addition, the diverse formats and access methods for these systems required us to ensure seamless interoperability.
To meet these challenges, we relied heavily on the MCP servers for handling different data source formats. The MCPs allowed us to build connectors for each system (Service Now, Kiosk, and Tachyon) while keeping the agent’s interface uniform. This ensured that queries could be routed to the appropriate data sources and processed efficiently, without the end-user needing to know the underlying complexity.
Additionally, the need to maintain data security and role-based access control (RBAC) for sensitive information was paramount. We implemented fine-grained access controls, ensuring that the system respected the company’s internal security policies, allowing executives to access only the data they were authorized to view.
Results and Impact
The deployment of the customized agent led to significant improvements in the company’s ability to quickly derive insights from multiple data sources. Executives were able to make more informed decisions by accessing real-time information on service disruptions, cloud costs, and HPC utilization, all within the same interface. Moreover, the system’s ability to generate both text summaries and graphs made the insights more digestible and actionable.
By reducing the time spent manually correlating data across systems, the company was able to improve operational efficiency and better manage both cloud spend and HPC resources. The executive team now has the ability to quickly assess critical business operations and make decisions based on accurate, real-time data.
Conclusion
In today’s data-driven world, executives need timely, actionable insights to drive informed decisions—but accessing, analyzing, and correlating data from multiple disparate sources remains a significant challenge. Our agent solves this by intelligently synthesizing data across a wide variety of systems, including observability tools, cloud spend trackers, and high-performance computing environments. By enabling CXOs to query multiple data sources with a single question and receive contextual, relevant responses, the agent removes the complexity traditionally involved in managing large-scale data environments.
The case study with the multinational manufacturing company serves as a clear example of how this solution can be adapted to meet the specific needs of diverse industries. Whether it’s monitoring IT service disruptions, managing cloud costs, or optimizing HPC workloads, the agent can provide insights that are both accurate and immediately actionable. Through the use of powerful technologies like MCP servers, LLMs, and role-based access control, the agent ensures that sensitive data is handled securely, while maintaining the flexibility and scalability required for large enterprises.
As organizations continue to generate vast amounts of data across a multitude of systems, the demand for intelligent agents capable of navigating and analyzing this information will only grow. By leveraging advanced data integration techniques and natural language processing, our solution empowers CXOs and decision-makers to stay ahead of the curve, make informed choices, and ultimately drive business success.
The future of enterprise decision-making is one where data can be effortlessly queried and turned into meaningful insights. With our agent, that future is already here.