Course Overview & What You Will LearnAI Agent Foundations and Shared VocabularyEstablishes a precise definition of AI agents, distinguishes agents from traditional automation, RPA, conventional AI, and copilots, and introduces the shared vocabulary - including autonomy, delegated authority, guardrails, escalation, orchestration, and agentic process — that forms the conceptual foundation Agentic AI and AI agents.
Agent Architecture and the Five Core ComponentsExplains the five core components of every AI agent - perception, reasoning, planning, action, and memory - and how each component creates specific business analysis requirements for agent specification, design, and deployment across enterprise processes. Agentic Process Discovery and the Five Discovery LensesCovers the systematic practice of identifying where AI agents create value using five analytical lenses: Cognitive Task Analysis, Decision Authority Mapping, Information Flow Analysis, Tool and System Interaction Analysis, and Waste and Friction Analysis — moving beyond traditional automation assessment to discover judgment-intensive, delegable opportunities. Agent Opportunity Assessment and Portfolio PrioritizationAddresses the structured evaluation of candidate processes across four weighted dimensions — Business Value, Technical Feasibility, Risk Tolerance, and Organizational Readiness — to produce composite scores, value-feasibility portfolio matrices, and sequenced deployment roadmaps with dependency management. Task Autonomy Classification and the Five-Tier TaxonomyIntroduces the five-tier autonomy taxonomy - Fully Autonomous, Human-on-the-Loop, Human-in-the-Loop, Human-Initiated, and Human-Only - with structured classification methodology, stakeholder validation, and phased autonomy evolution planning from supervised pilot to autonomous operation. Agent Value Stream Mapping and Economic Impact VisualizationApplies an agent-adapted Lean value stream mapping technique to visualize end-to-end processes with agent opportunity overlays, autonomy tier annotations, queue elimination modeling, bottleneck migration analysis, and comprehensive current-state to future-state economic impact quantification. Case Study and Applied FrameworksProvides hands-on workshop exercises including agent component mapping, agentic process discovery, opportunity assessment scoring, task autonomy classification, and collaborative value stream mapping - building practical competency through structured templates applied to real-world business processes.. Discovery Anti-Patterns and Practical GuidanceFocuses on recognizing and avoiding common discovery pitfalls — automation bias, technology push, volume obsession, perfect process fallacy, scope creep optimism, and assessment pitfalls — and provides practical guidance on deployment sequencing, dependency management, and building the business case for agent investment. |
Inteq’s Discovering Agentic AI Opportunities course provides a structured, business-oriented approach to identifying, evaluating, and prioritizing where AI agents create measurable value in enterprise business processes. Over two immersive days, participants learn to define AI agents with precision, apply five analytical discovery lenses, assess opportunities across value, feasibility, risk, and readiness dimensions, classify tasks by appropriate autonomy levels, and visualize end-to-end economic impact using agent value stream mapping. Through extensive hands-on exercises, facilitated discussions, and applied case work using professional frameworks and templates, individuals and teams develop practical skills to build prioritized agent opportunity portfolios with sequenced deployment roadmaps. The result is a disciplined, repeatable methodology for discovering high-value agent opportunities that goes far beyond traditional business process improvement and automation design thinking.
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