Product Management — when AI Agents participate, only two paths exist.
100% full control
The AI Agent participates in the product team under complete oversight. Every prompt, every decision, every change is managed by the team.
We don't use it at all
The AI Agent does not participate in the process. The team makes all decisions independently; control remains entirely with us.
Without control, the system breaks down
When control is not in our hands, the CodeSpace — architecture, API, file structure — collapses. Whatever the AI Agent says, goes — we become its prisoner. Companies don't acknowledge this from a risk perspective.
Precise prompt — precise control.
The team that knows the integration between modules controls the AI agent. The team that doesn't becomes its prisoner.
Learn Agentic System Thinking
Deeply understand the integration between modules — API, DB, UI, and CodeSpace relationships.
Write precise prompts
Based on this knowledge, give the AI agent clear, structured, and targeted prompts.
Fully control the AI agent
With precise prompts, both communicate correctly with the AI agent and keep CodeSpace healthy.
AI Agent's thinking pattern
Key Insight
Having the Agentic System Thinking skill means knowing the integration between modules.
Without this knowledge, every prompt given to the AI agent is an uncontrolled decision.
The product team becomes fully AI-Ready.
The product team becomes fully AI-Ready when they master Agentic System Thinking. Because they can see the big picture while communicating with AI Agents at every step.
Product Manager
Manages roadmap and priorities together with AI
Business Analyst
Correctly converts business requirements into the system
Product Owner
Synchronizes the backlog with the AI agent
Developer
Writes precise code knowing architecture relationships
UX Designer
Structures the UI Canvas together with AI
Tech Lead
Maintains system architecture together with the AI agent
Framework
From Business Requirements to Code Delivery Alignment
Online. AI-based. In a real environment.
The lessons are designed so that everything is online — they learn by testing in a real environment on the online version of the AI-based DPS platform. Not theoretical knowledge, but practical connections.
This system currently exists with us.
100% Online
Every lesson can be conducted from anywhere, at any time. No physical location required.
AI-Based DPS Platform
Lessons are conducted on the online version of the real DPS platform — not a simulation, but a real work environment.
Agentic System Thinking
They learn the relationship between every module and practice writing precise prompts to the AI agent.
Practical Testing
By working with the AI agent in real project scenarios, they learn to see the map of the large system.
Explanations
Activity Explanations
Phase 1 — Business Inputs
User Stories
User-centered stories written in the format "As a [user], I want [goal] so that [reason]". They form the foundation of what the product should do and guide prioritization.
Business Requirements
High-level objectives and constraints defined by stakeholders. Explains the product's "why" — which business problem is solved, which KPIs measure success.
Product Monetization
The revenue model and commercial strategy tied to the product. Early monetization definition ensures feature decisions align with business value.
Phase 2 — Product Definition
Data Flow
A structured map of how data moves through the system. Uncovers hidden dependencies and integration risks before a single line of code is written.
UI Canvas
A visual workspace encompassing user interface structure, screen layouts, and component hierarchy. The single living document ensuring all team members share the same visual model.
Phase 3 — Technical Design & Planning
API
REST or GraphQL endpoints, request/response schemas, status codes, versioning strategy, and authentication patterns. An agreed API contract prevents integration friction.
Database
Entity-relationship modeling, schema structure, indexing strategy, and storage engine selection. Decisions here determine query performance and data integrity.
UI/UX Design
High-fidelity design artifacts — wireframes, component-level mockups, interactive prototypes. Converts the UI Canvas into pixel-perfect specifications for engineers.
Component Relation Diagram
A dependency graph showing how UI components, services, hooks, and modules relate to each other. Prevents circular dependencies and duplication.
System Architecture
The technical blueprint of the entire product — infrastructure topology, service boundaries, deployment pipeline, CDN, caching strategy, and disaster recovery.
User Acceptance Criteria
Measurable conditions every feature must meet before being considered complete. UAC converts ambiguous requirements into concrete, testable scenarios.
Backlogs / Tasks
A prioritized queue of work items derived from requirements, architecture decisions, and UAC. The single source of truth for sprint planning.
Phase 4 — Build Structure
CodeSpace / File Folder Structure
Organizing the codebase into logical folders, modules, and feature slices before coding begins. Consistent structure reduces cognitive load and speeds up onboarding.
Phase 5 — Delivery Execution
Code Line Development
The active implementation phase where engineers write code, conduct peer reviews, and iteratively improve. Proceeds with minimal rework thanks to the preceding phases.
Git Repo
Version-controlled source of truth for the entire codebase. Every commit is traceable to a backlog item. CI/CD hooks enforce quality gates.
Phase 6 — Executive Control / Alignment
Product Requirements & Code Line Alignment
The final verification loop comparing what was actually built against what was originally required. Identifies gaps, confirms acceptance, and feeds insights into the next planning cycle.
Agentic System Thinking — a skill every product team member must have in the AI era.