Establish a shared, enterprise method for specifying AI agents - ensuring consistent decision logic, clear engineering handoffs, and scalable governance.
Course Overview & What You Will LearnProcess Decomposition and Agent-Assignable Work UnitsCovers the disciplined breakdown of business processes into discrete, well-bounded tasks at the appropriate granularity for agent assignment, using five decomposition principles: decision boundary alignment, atomic decision unit identification, data dependency mapping, exception boundary definition, and parallelization potential assessment.
Agent Persona, Role, and Decision Logic SpecificationThe core specification work that translates a validated opportunity into a precise, implementable agent definition - including the seven-dimension Agent Identity Model, decision authority matrices, and rigorous documentation of agent decision logic using decision tables, decision trees, policy matrices, and weighted scoring models across decision and judgment categories. Prompt Specification and Communication DesignFocuses on the business analyst’s contribution to agent prompt design: creating the seven-component Prompt Specification Package that engineering teams use to configure agent behavior, including role definitions, decision rules catalogues, domain terminology glossaries, output format specifications, example libraries, constraint catalogues, and acceptance criteria. Includes agent communication and tone specification across audiences, channels, and situations. Human-Agent Interaction Design and Trust CalibrationCovers the analysis and design of all touchpoints where humans and AI agents interact, treating the human-agent boundary as a first-class design surface. Encompasses the six HAID dimensions: interaction initiation, information exchange and transparency, trust calibration, override and intervention mechanisms, feedback and learning integration, and handoff design protocols. Exception Handling, Escalation, and Context ManagementAddresses the systematic identification of all scenarios where agents cannot continue autonomously, covering six exception categories and five handling strategies. Includes escalation context package design, four-tier memory specification (ephemeral, session, persistent, shared), context handoff protocols, and memory governance frameworks. Data Readiness and Tool Capability AssessmentCovers comprehensive evaluation of data environments across eight readiness dimensions (availability, quality, freshness, accessibility, governance, semantic clarity, security, and agent output data), plus specification of every tool, API, and system resource the agent requires with contracts, error handling, fallback strategies, and capability gap analysis. Integration Architecture and Knowledge GroundingAddresses analysis of how agents connect to enterprise technology ecosystems, covering five integration patterns and integration touchpoint specifications. Includes agent knowledge and domain grounding requirements: knowledge base design, RAG architecture specifications, source authority hierarchies, knowledge gap handling protocols, and hallucination prevention frameworks. Specification Integration, Anti-Patterns, and Engineering HandoffCumulative hands-on exercises building the complete Agent Requirements Package, identification and prevention of specification anti-patterns across all domains, engineering handoff simulation and gap analysis, and the assembly of the integrated specification package comprising persona, decision logic, prompt specification, interaction design, exception handling, memory management, data readiness, tool specifications, integration architecture, and knowledge grounding deliverables. |
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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