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an agent that answers questions over a large enterprise knowledge base by retrieving relevant documents, reasoning over them, and generating accurate answers with source citations.
The autonomy of your RAG-based Q&A agent will primarily revolve around its ability to make decisions based on confidence thresholds and the types of queries it can handle. In this context, the agent will operate semi-autonomously, meaning it can autonomously retrieve and generate answers but will have predefined constraints that dictate when it should escalate to a human or fallback to a default response.
In your design, focus on establishing clear confidence thresholds and defining the criteria for when the agent should escalate or fallback. This will ensure a robust balance between autonomy and reliability, crucial for user trust in the system.
Tools
Document Parser: Extracts text, tables, and metadata from diverse enterprise formats (PDF, DOCX, TXT, HTML, CSV, Markdown) using OCR and layout-aware parsing.
Hybrid Search Engine: Combines a Vector Database tool (for semantic similarity) and a BM25 Search tool (for exact keyword matching) to ensure comprehensive document retrieval.
Re-ranker Tool: Evaluates and re-orders the initial retrieved chunks based on strict relevance to the user's specific query.
Citation Generator: Maps generated statements back to the specific source document chunks and metadata to append accurate citations.
Actions
Analyze_Query: Determines if a query is factual, multi-document, or out-of-scope.
Execute_Search: Formulates search queries and fetches data from the Hybrid Search Engine.
Evaluate_Confidence: Calculates the internal confidence score of the generated answer against the retrieved context.
Escalate_To_Human: Routes the user's query and the current context to a live support queue or ticketing system.
Orchestration Pattern: This system utilizes a Single-Agent with Tool-Routing pattern. This is highly effective for Q&A tasks where the primary goal is directed retrieval and reasoning rather than complex multi-agent negotiations.
Component Interaction: The Agent Orchestrator receives the query and performs an initial scope check. It then invokes the Hybrid Search Tool to query both the Vector Store and the BM25 index. The results are unified and scored by the Re-ranker.
Final Routing: The core LLM processes the top-ranked chunks, attempts to generate an answer, and runs a self-evaluation against the defined confidence threshold. Passing scores move to the Citation Generator for output, while failing scores route immediately to the Human Handoff module.
Short-Term (Conversation) Memory: * The agent maintains a sliding context window of the most recent conversational turns (e.g., the last 5 user-agent interactions).
To prevent token exhaustion and maintain low latency, older messages are dynamically summarized and stored as a compressed "conversation history" context block.
Long-Term (Persistent) Memory: * The enterprise knowledge base serves as the persistent memory.
Documents are processed via chunking strategies (e.g., semantic chunking or fixed-size with overlap) and embedded into the Vector Database alongside rich metadata tags (date, author, department) to aid in precise retrieval.
Context Window Management: * Context is strictly managed via "Top-K" retrieval limits enforced by the re-ranker.
The agent dynamically calculates the remaining token budget after the system prompt and conversation history are loaded, filling the remainder only with the highest-scoring document chunks to prevent hallucination caused by context overflow.
Planning and Decision (Chain-of-Thought): * The agent uses a ReAct (Reason + Act) prompting framework.
When faced with a complex or multi-document query, it verbally breaks down the question into sub-queries, executes searches for each part, and aggregates the findings before drafting a response.
Reflection Loops & Hallucination Detection: * Before outputting an answer, the agent engages in a self-correction loop. It asks: "Is this answer fully supported by the retrieved context?" * If the agent detects leaps in logic or missing information, it triggers a secondary search or automatically lowers its confidence score.
Failure Handling & Tradeoffs: * The agent utilizes predefined confidence thresholds (e.g., an 85% certainty requirement based on logprobs or self-evaluation).
If the score falls below this threshold, the agent gracefully fails with, "I found some related information, but I'm not confident enough to provide a complete answer. Let me connect you with a human expert." * Tradeoff: This strict verification increases processing latency slightly but significantly boosts citation accuracy and user trust by virtually eliminating hallucinations.
Permission Boundaries (RBAC): * The agent implements strict Role-Based Access Control at the retrieval level.
User identity tokens are passed to the Vector and BM25 databases, ensuring the agent can only retrieve and reason over documents the user is explicitly authorized to view.
Guardrails & Content Filtering: * An input/output filtering layer sits in front of the agent to block prompt injection attacks and reject adversarial or off-topic queries (e.g., refusing to answer non-work-related questions).
Output filters ensure PII or sensitive system data is redacted if accidentally retrieved.
Rate Limiting & Stability: * The system employs user-level rate limiting and token-usage quotas to prevent abuse, manage cloud costs, and maintain consistent latency for all enterprise users.
Unexpected system states (e.g., database timeout) automatically trigger a polite default response, logging the error for engineering review while keeping the user informed.