Course Overview
Process Decomposition and Agent-Assignable Work Units
- Decompose business processes into discrete, well-bounded tasks at the appropriate granularity for agent assignment
- Align task boundaries with decision boundaries and identify atomic decision units
- Map data dependencies and define exception boundaries
- Assess parallelization potential across agent-assignable work units
Agent Persona, Role, and Decision Logic Specification
- Translate validated AI agent opportunities into precise, implementable agent definitions
- Define agent persona and role using the seven-dimension Agent Identity Model
- Establish decision authority using structured decision authority matrices
- Specify agent decision logic using decision tables, decision trees, policy matrices, and weighted scoring models across decision and judgment categories
Prompt Specification and Communication Design
- Define the business analyst's contribution to agent prompt design
- Create the seven-component Prompt Specification Package used by engineering teams to configure agent behavior
- Specify roles, decision rules, domain terminology, output formats, examples, constraints, and acceptance criteria
- Define agent communication and tone requirements across audiences, channels, and situations
Human-Agent Interaction Design and Trust Calibration
- Analyze and design human-agent touchpoints as a first-class design surface
- Define interaction initiation, information exchange, and transparency requirements
- Specify trust calibration and human override and intervention mechanisms
- Design feedback, learning integration, and human-agent handoff protocols
Exception Handling, Escalation, and Context Management
- Identify scenarios in which agents cannot continue autonomously and apply six exception categories and five handling strategies
- Design escalation context packages that preserve the information humans need to intervene effectively
- Specify four tiers of agent memory: ephemeral, session, persistent, and shared
- Define context handoff protocols and memory governance requirements
Data Readiness and Tool Capability Assessment
- Evaluate data environments across eight readiness dimensions: availability, quality, freshness, accessibility, governance, semantic clarity, security, and agent output data
- Identify data conditions and gaps that could prevent reliable agent operation
- Specify the tools, APIs, and enterprise systems an agent requires
- Define contracts, error handling, fallback strategies, and capability gaps for required tools and systems
Integration Architecture and Knowledge Grounding
- Analyze how AI agents connect to enterprise technology ecosystems using five integration patterns and defined integration touchpoints
- Specify agent knowledge and domain grounding requirements
- Define knowledge base and RAG architecture requirements
- Establish source authority hierarchies, knowledge-gap handling protocols, and hallucination prevention frameworks
Specification Integration, Anti-Patterns, and Engineering Handoff
- Build the complete Agent Requirements Package through cumulative hands-on exercises
- Identify and prevent specification anti-patterns across agent requirements domains
- Conduct engineering handoff simulation and gap analysis
- Assemble persona, decision logic, prompt, interaction, exception, memory, data, tool, integration, and knowledge-grounding specifications into one authoritative engineering-ready package
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AI agents represent a fundamental shift in how organizations deploy intelligent systems. Unlike traditional automation, AI agents exercise delegated judgment, make context-dependent decisions, and interact dynamically with humans and enterprise systems.
Yet most organizations lack a structured methodology for specifying what these agents should do, how they should decide, and how they should collaborate with human colleagues.
Inteq's Analyzing & Specifying AI Agent Business Requirements training course provides the disciplined methodology for translating validated AI agent opportunities into engineering-ready specification packages - the authoritative artifacts from which technical teams build and governance teams audit.
The course addresses the critical handoff gap that derails most agent initiatives: business teams that cannot specify requirements with sufficient precision, and engineering teams forced to make business decisions that should have been resolved during business analysis.
When agents make autonomous decisions based on ambiguous specifications, the consequences range from costly inefficiency to compliance failure.
Based on deep business analysis experience, Inteq has uncovered and refined the foundational patterns of AI agent specification. Participants utilize these patterns to rapidly discover, critically analyze, and precisely specify AI agent business requirements via comprehensive Agent Requirements Packages.
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