Agent Requirements

Agent Objective:

What is the agent designed to accomplish? Define the core task and success criteria...

The agent is designed to answer user questions over a large enterprise knowledge base by retrieving the most relevant internal documents, reasoning across them, and generating accurate, concise answers with source citations.

The core task is not simply to chat with the user, but to provide grounded enterprise knowledge answers. For every user query, the agent should understand the question intent, retrieve relevant evidence from approved knowledge sources, synthesize the answer, cite the supporting documents or passages, and clearly communicate uncertainty when the available evidence is insufficient.

Success criteria include:

• Answer accuracy: The response should be factually correct and grounded in retrieved enterprise documents.
• Faithfulness: The answer should only make claims supported by retrieved sources.
• Citation quality: Important claims should include citations to the specific documents or chunks used.
• Retrieval relevance: The system should retrieve the most useful passages using hybrid search, semantic search, keyword search, metadata filters, and reranking.
• Coverage: For questions that span multiple documents, the agent should identify and combine all necessary evidence instead of relying on a single source.
• Freshness: When multiple versions of a document exist, the agent should prioritize the latest authoritative source.
• User clarity: The answer should be easy to understand, directly address the question, and call out any ambiguity or missing information.
• Safety and compliance: The agent should respect enterprise access control, data sensitivity, and privacy rules.

Example: If a user asks, “What is our current PTO carryover policy for California employees?”, the agent should retrieve the latest HR policy documents, filter by region and employee type, cite the relevant policy section, and avoid answering from outdated or unauthorized documents.

Autonomy & Constraints:

What level of autonomy does the agent have? What are the boundaries and constraints it must operate within?

The agent has bounded autonomy. It can independently perform retrieval, query decomposition, reranking, evidence selection, answer generation, citation generation, and self-verification. However, it must operate within strict enterprise boundaries.

The agent can autonomously:

• Interpret the user’s question and identify whether it is simple, ambiguous, or multi-hop.
• Rewrite or expand the query to improve retrieval quality.
• Break complex questions into sub-questions when multiple documents or concepts are involved.
• Search across approved enterprise knowledge sources using hybrid retrieval.
• Apply metadata filters such as document type, department, date, region, product, or access level.
• Rerank retrieved chunks based on relevance, authority, freshness, and user intent.
• Generate an answer with citations.
• Run a hallucination or grounding check before returning the final response.
• Ask a clarifying question when the original question is too ambiguous to answer reliably.
• Say “I don’t have enough information” when retrieved evidence is insufficient.

The agent cannot:

• Access documents the user is not authorized to see.
• Bypass role-based access control, document permissions, or data classification policies.
• Modify, delete, or publish enterprise documents.
• Take external actions such as sending emails, filing tickets, or updating systems unless explicitly designed and authorized to do so.
• Invent facts, policies, numbers, dates, or decisions that are not supported by retrieved evidence.
• Use stale documents when a newer authoritative version is available.
• Expose sensitive information such as PII, confidential strategy, legal documents, financial data, or security details unless the user has permission.
• Provide legal, financial, HR, or compliance guidance beyond what is supported by approved source documents.
• Treat retrieved content as correct without considering document authority, freshness, and conflicts.

When evidence conflicts across documents, the agent should prefer the most recent and authoritative source, explain the conflict if needed, and cite both sources. When confidence is low, the agent should avoid over-answering and instead provide a qualified response or ask for clarification.

Overall, the agent should behave like a trusted enterprise research assistant: proactive in finding and synthesizing information, but conservative about claims, permissions, and unsupported conclusions.

Tool & Action Design

Define the tools and actions available to the agent. Specify tool interfaces, input/output schemas, and how the agent selects which tool to use...

Agent Architecture

Design the overall agent architecture. Choose an orchestration pattern (single agent, multi-agent, hierarchical). Identify key components and how they interact. Use the diagramming tool to illustrate the architecture.

Memory & Context Design

How does the agent manage memory and context? Define short-term (conversation) and long-term (persistent) memory strategies. How is context window managed? What retrieval mechanisms are used (RAG, vector stores, summarization)?

Core Reasoning

Deep dive into the agent's reasoning strategy. How does it plan, decide, and self-correct? Discuss prompt engineering, chain-of-thought, reflection loops, failure handling, and tradeoffs.

Safety & Permissions

What guardrails and safety mechanisms does the agent have? Define permission boundaries, human-in-the-loop checkpoints, content filtering, rate limiting, and how the agent handles adversarial inputs or unexpected states.