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Journey

Building and Operating AI and Agentic Products

A vertical Engineering Track journey for designing, building, evaluating, securing, and operating a bounded AI or agentic product capability.

The workplace problem

Why this learning matters

Teams can assemble an AI or agentic demonstration without designing the specifications, evaluations, authority boundaries, managed context, security, resilience, and operating ownership needed for a dependable product.

Intended outcomes

What participants will learn to do

  • Define a bounded AI or agentic product capability, its users, context, decisions, and operating constraints.
  • Design specifications, evaluation evidence, authority boundaries, managed context, and Human + AI interaction deliberately.
  • Connect architecture and implementation with security, safety, cost, observability, resilience, and recovery needs.
  • Establish accountable release, operation, incident, and learning practices for the capability.

Application at work

How participants can apply it at work

  • A bounded AI or agentic capability brief with explicit authority and context boundaries.
  • An engineering evidence map spanning specification, evaluation, security, safety, operations, and recovery.
  • A build-and-operate increment plan with accountable gates and owners.

Shaped to context

Shaped around your context

Flow Cracker composes this journey around the customer's product opportunity, architecture, tools, data and context, controls, operating environment, and consequence profile rather than prescribing one agent framework or fixed technical syllabus.

Entry context: Participants should bring a bounded AI or agentic product opportunity and practical product-engineering experience; specialist depth is selected to fit the capability and risk context.

Who it is for

Who this is for

  • AI product and engineering teams
  • Software, platform, architecture, quality, security, and operations practitioners
  • Technical and product leaders accountable for an AI or agentic capability

Opportunity lenses

  • AI in the product
  • AI in work and workflow
  • AI to build the product

Capabilities strengthened

  • Product Discovery & Decision Practice
  • Engineering, Architecture & Verification
  • Team Collaboration, Facilitation & Flow
  • Enterprise, Portfolio & Investment Flow

Connected learning

Discuss the context

Shape the learning around the work that needs to change

Share the audience, workplace problem, constraints, and evidence you want the learning to address.

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