At many companies, resolving customer issues can be a time-consuming process. Delays often arise from waiting for responses or scheduling debugging sessions with experts. This impacts customer service level agreements and frustrates both support engineers and customers.
LitenAI is an Agentic framework designed to streamline technical support and debugging. It provides a conversational interface where support engineers can issue high-level instructions to receive expert responses or debug complex cases efficiently.
Driven by natural prompts from chat, notebook or slack, LitenAI master agent orchestrates intricate data and AI tasks, automating various engineering processes to boost productivity and customer satisfaction. By leveraging both best-in-class third-party and proprietary foundational models, its AI agents are fine-tuned to meet customer-specific needs, ensuring optimal performance.
LitenAI focuses on training its models to enhance comprehension of technical materials. Accelerated data agents observe and analyze data, while code generator agents produce Python and SQL code for customer-specific tasks. Reasoning and planning agents assist in defining and executing steps required to solve customer problems. The LitenAI execution engine is capable of running big data queries in Python and SQL, enabling seamless integration. Customers can orchestrate this entire process using a natural language interface.
AI Agents work closely with LitenAI Smart Data Lake to handle data engineering tasks associated with technical support. The data lake serves as a repository for knowledge documents and tables, enabling agents to access the necessary data and knowledge context. Additionally, these agents can connect to external data sources, such as Open Telemetry or relational databases, while the smart lake acts as a cache to manage data transitions.
Data Agents
LitenAI Agents replace outdated tools by automatically generating Python and SQL code for querying, plotting, and analyzing data. Rather than relying on manually written queries, LitenAI leverages AI engine to produce the code needed for various tasks. LitenAI accelerates queries using an open tensorized data store by more than 10 times.
Optimized to work with Smart Data Lake documents and tables, these agents are equipped with knowledge of popular Python libraries, enabling users to create data visualizations effortlessly. This reduces time and simplifies the data analysis process.
Sample chat session is shown below. Please contact us to try it out on our tools.
Planning and Reasoning Agents
Planning agents enable the creation of structured action plans for complex tasks by analyzing dependencies and prioritizing steps. They assist by generating solution guidelines and code derived from customer knowledge and LitenAI resources.
Reasoning agents use logical inference and contextual knowledge to diagnose problems and propose solutions. These agents excel in troubleshooting by identifying patterns and relationships in data, allowing for accurate root cause analysis.
See below an example chat with these capabilities.
Visualization Agents
Visualization agents simplify data representation by generating intuitive graphs and charts. They transform raw data into meaningful visual insights, making it easier for teams to interpret trends and anomalies. These visualizations enhance communication and decision-making. We do not need to write any rendering code, python libraries create interactive as well as graphic plots.
See an example chat below.
Custom Agents and Data Ingestion
LitenAI demonstrates adaptability through its ability to create tailored agents. For instance, a customer required a specific report structure, and LitenAI successfully developed a custom agent to meet their needs. This agent enables the customer to quickly generate reports in the desired format, showcasing its flexibility and customization capabilities.
Customers can also upload any documents or tables to the smart data lake. This can be done programmatically or through an interface. For structured data, the structures can be automatically inferred for easy search. Customers can modify it as needed to customize it to their needs.
The lake also stores all previous chats. Customers can load these chats and use them to guide their debugging sessions. LitenAI fine tuned planners also use this data to make more relevant plans.
A screen capture shows some of these options in today’s interface. It also has a chat interface if that is preferred,
Master Agent
The master agent has been trained to understand technical requirements for support and debugging. It coordinates the training and strategies of data and AI agents for seamless orchestration. LitenAI focuses on enhancing its models for engineering comprehension.
See the following prompt and a high level task execution to perform a high level engineering tasks. The Agent is general enough to perform customer specific asks using the customer documents.
Summary
The LitenAI framework equips customers with a robust tool for debugging and analyzing technical issues. Acting as a virtual expert, it aids in planning actions, reasoning through data, and effectively troubleshooting problems. By automating tasks and leveraging domain-specific AI, LitenAI significantly improves productivity and customer satisfaction.