<Workshop Hosts>
Building Agentic AI Applications
Zsuzsanna Tamas
Amazon, CoachHub, DKB Code Factory, Lesara, Runtastic, GlobeAir
Learn to decide what AI agents can do with your product—and where human oversight remains essential. Explore how product architecture, APIs, and constraints determine whether AI systems can safely use your product in automated workflows. Move beyond Answer Engine Optimization to focus on agent readiness and system reliability as core PM responsibilities.
Through conceptual inputs, discussions, and hands-on exercises, you'll apply agentic patterns to realistic scenarios and balance automation, safety, and accountability.
<Morning Session>
The focus of the workshop is on intentional automation and delegation: deciding what responsibilities can be entrusted to AI agents, how agent-driven interactions are coordinated across time and channels, and where human-in-the-loop or human-on-the-loop oversight remains essential.
The workshop blends concise conceptual inputs with facilitated discussions and hands-on design exercises, enabling participants to apply agentic patterns to realistic product scenarios.
<What you’ll learn>
Establish a strong mental model for AI-enabled product, journey, and system design
Build practical literacy in LLMs, including model types, selection tradeoffs, prompting, and retrieval techniques
Apply decision frameworks to determine when to automate, augment, or retain human-led workflows, including agentic system design and oversight
Design for reliability at scale by anticipating risks and implementing observability, evaluations, hallucination management, and graceful failure mechanisms
<Afternoon Session>
The workshop focuses on the product decisions that determine whether AI systems can correctly understand a product, use it safely, and rely on it in automated workflows. It introduces Answer Engine Optimization (AEO) as an important but limited context for how products are discovered and described by AI systems, and then shifts to agent readiness and system reliability as core responsibilities for Product Managers across industries.
Participants will learn how choices around product architecture, exposed capabilities, APIs, constraints, and failure handling affect whether a product can be used by AI agents.
The workshop combines short conceptual inputs with facilitated discussions and hands-on design exercises, ensuring participants actively apply concepts to realistic product scenarios. The focus is on practical decision-making, trade-offs, and design patterns that help PMs balance automation, safety, and accountability.
<What you’ll learn>
Answer Engine Optimization (AEO) from a scoped product perspective
Product legibility and semantic clarity for AI systems
Capability-first product design (actions, APIs, skills)
Agent readiness: constraints, contracts, and safe delegation
<Best for>
Experienced Product Managers building AI-enabled products and customer experiences
PMs who want to upskill from foundational AI concepts to applied agent design
Product Managers working in environments where discovery, decision-making, and execution increasingly happen through answer engines, AI assistants, and automated workflows
PMs looking to build confidence and practical judgment in agentic product development
<Meet your Host>
Zsuzsanna Tamás is a Senior AI Product Manager at Amazon with 10+ years of experience building recommender systems, AI platforms, and matching algorithms. She’s led product teams at Amazon, CoachHub, DKB, and Adidas Runtastic, and currently drives personalization initiatives at Amazon Music for over 80 million customers. Known for bridging strategy and execution, Zsuzsanna helps teams cut through complexity and adopt AI in ways that are practical, customer- focused, and impactful.
< Tickets >
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