AI agents operate very differently from traditional systems or automation. They apply judgment, interpret context, and influence outcomes. As a result, specifying requirements for AI agents is fundamentally more complex than writing software or automation requirements.
Most AI agent failures occur because organizations underestimate this complexity. Requirements are vague, decision authority is implicit, and boundaries between human judgment and agent autonomy are unclear.
Inteq’s AI Agent Requirements and Retrieval Augmented Generation (RAG) analysis focuses on the hard work of deep business analysis – identifying, analyzing, and precisely specifying what an AI agent is responsible for, what decisions it may make or recommend, when humans must intervene, and how accountability is maintained.
Through disciplined requirements analysis, decision-rights design, and knowledge access definition, we blueprint agents that are explainable, controllable, and aligned with business risk tolerance – before development begins.
Services Provided
- AI agent scope, role, and boundary definition
- Detailed business requirements analysis and specification
- Decision decomposition and authority analysis
- Autonomy levels and human-in-the-loop (HITL) design
- RAG knowledge domain identification and access rules
- Knowledge quality, ownership, and governance analysis
- Non-functional requirements definition (explainability, auditability, risk)
Key Deliverables
- AI Agent Business Requirements Specification (BRS)
- Agent decision rights and autonomy matrix
- RAG business requirements and knowledge architecture
- Human-in-the-loop (HITL) escalation and override model
- Non-functional requirements
Business Value
- Prevents uncontrolled or unsafe agent behavior
- Dramatically improves agent accuracy and trust
- Aligns business, IT, and AI teams around a shared blueprint
- Reduces development rework and implementation risk
When to Engage Inteq
- You are ready to begin AI agent requirements analysis
- AI agent requirements are incomplete and/or ambiguous
- You are unsure what decisions or authority agents should make
- Risk, explainability, or auditability are concerns
- You want agents to use trusted enterprise knowledge – not assumptions
- Governance, auditability, explainability, or compliance concerns
Many AI agents fail long before they’re built. Inteq’s AI Agent Requirements & RAG analysis services ensure your agents are analyzed, specified, and designed with clarity, authority, and trust from day one.
This work establishes the business blueprint before development begins. A short call can determine whether clearer requirements would reduce delivery risk.
