Knowledge

What is Knowledge in Agent?

In the context of AI agents on UPTIQ, Knowledge refers to the repository of structured and unstructured information that the agent relies on to better understand user queries and provide accurate responses. This knowledge is typically stored in a RAG (Retrieval-Augmented Generation) container, which combines traditional information retrieval techniques with generative AI to enhance the agent's reasoning and intent classification capabilities.

• RAG Container: A hybrid system where relevant data is retrieved from the knowledge base and passed to the reasoning engine for context-aware responses.

• Role in Reasoning: Knowledge is not just static data but a dynamic resource that informs the Reasoning Engine when interpreting or classifying user intents. It allows the agent to understand nuanced queries, connect them with relevant information, and generate meaningful responses.

How Does it Work?

1. Data Storage:

   • Knowledge is preloaded into the RAG container.

   • It may consist of documents, FAQs, databases, product manuals, or any domain-specific resources.

   • Information can be structured (e.g., tabular data) or unstructured (e.g., natural language text).

2. Query Interpretation:

   • When a user query is received, the Reasoning Engine first attempts to classify the intent.

   • To refine the classification or respond accurately, it retrieves contextually relevant information from the RAG container.

   • This ensures that responses are both intent-driven and knowledge-informed.

3. Retrieval-Augmented Generation:

   • Relevant data is fetched from the knowledge base using sophisticated search algorithms, embeddings, or semantic similarity techniques.

   • The retrieved data is used to inform the generative AI model, ensuring responses are precise and fact-based.

4. Real-Time Execution:

   • Knowledge is dynamically accessed during the workflow execution to enrich outputs, adapt responses, or resolve ambiguities in user queries.

   • For example, a workflow node might explicitly call for retrieving data from the RAG container as part of its process.

Examples

1. Customer Support Agent:

   • Knowledge: A collection of FAQs, product manuals, and troubleshooting guides.

   • Functionality: When a customer asks about a specific product feature, the RAG container retrieves the relevant section of the manual, which the reasoning engine uses to generate a response.

2. Loan Origination AI Agent:

   • Knowledge: Bank policies, loan application criteria, and documentation templates.

   • Functionality: If a user asks about the eligibility criteria for a loan, the agent retrieves the relevant policy details and provides a tailored explanation.

3. Healthcare Assistant:

   • Knowledge: Medical guidelines, patient records, and drug interaction databases.

   • Functionality: When a user inquires about a possible drug interaction, the agent pulls information from its knowledge repository and advises accordingly.

4. Document QA Workflow

   • Knowledge: A repository of documents (e.g., PDFs) stored in the RAG container.

   • Functionality: A query like "What is the interest rate on this loan?" would trigger the workflow to:

     • Fetch the document using the external database node.

     • Extract relevant sections using the reasoning engine.

     • Deliver an accurate response informed by the document's content.

Key takeaway for developers

In UPTIQ, Knowledge serves as the foundation for intelligent reasoning in AI agents. It equips agents with the ability to:

✅ Contextualize user queries.

✅ Refine intent classification.

✅ Provide factually correct and relevant responses.

Developers should focus on curating high-quality, domain-specific knowledge bases to maximize the accuracy and utility of their AI agents.