AI Software Factory Mastery Curriculum

3. Operating Model

Explain how humans and agents divide responsibility across intent, planning, execution, validation, approval, recovery, and learning. Examine governance, progressive autonomy, organizational transformation, human attention, and factory economics. Distinguish factory-owned deployment governance from deployment execution delegated to CI/CD systems.

Chapters:

• The Human-Agent Operating Model
• Factory Economics and Operating Metrics
• Governed Continuous Learning and Recursive Improvement
• Enterprise Adoption and Factory Maturity Model
• Compounding Engineering and Human Attention
• Enterprise Governance Operating Model and Decision Rights

4. Domain Model

Develop the authoritative chain from organizational scope to accepted outcome:

Company -> Workspace -> Repository -> Factory Configuration -> Mission -> Plan -> WorkOrder -> Task -> Attempt -> Evidence -> Pull Request -> Release

Each concept must explain the decision it owns, what it does not own, its lifecycle, its relationships, and the failure caused when layers are collapsed.

Chapters:

• The Authoritative Delivery Hierarchy
• Factory Configuration, Workflow Contracts, and Execution Manifests
• Specification Engineering, Executable Requirements, and Plan Assurance
• Multi-Repository Development and Coordinated Delivery
• Factory System Inventory, Classification, and Lifecycle

6. Runtime Architecture

Study the systems that turn authorized work into durable execution. Topics include React, Convex, Hono, executors, worktrees, GitHub, queues, state machines, concurrency, retries, cancellation, recovery, and orchestration.

Implementation-specific material must remain clearly separated from enduring runtime principles.

Chapters:

• Control Plane and Execution Plane
• Runtime Orchestration and State Machines
• Tasks, Attempts, Leases, Idempotency, and Recovery
• Sandboxed Execution, Isolation, and Publication
• Factory Observability and Agent Runtime Telemetry
• AI Software Factory Reference Architecture
• Development Environments, Compute, and Composable Infrastructure
• Coding Harnesses, Adapters, and Agent Protocols
• Orchestration Component Model and Runtime Contracts

8. Autonomous Engineering Workflows

Turn general agent capability into explicit workflow products. Onboard repositories before granting authority, then distinguish feature, defect, test, dependency, security, incident, production, modernization, and knowledge work by trigger, evidence, risk, recovery, and accepted outcome.

Chapters:

• Repository Onboarding and Codebase Intelligence
• Autonomous Engineering Workflow Catalog
• Change Workflows — Features, Defects, Tests, and Modernization
• Operational Workflows — Security, Incidents, Production, and Knowledge

Supplemental: Mission Control Case Studies

Use Mission Control to examine real architectural decisions, implementation tradeoffs, failures, and lessons. Every case study cites the exact product source and commit while preserving the distinction between product documentation and personal learning.

Current case studies:

• Mission Control Implementation Maturity and Evidence Map
• Verification-First Software Factory — Mission Control Case Study — explains the assurance architecture, traces the implemented P0 at an exact Mission Control commit, distinguishes proposed completion work, and provides interview questions, whiteboard exercises, and hands-on mastery labs.
• Mission Control Capability, Workflow, and Admission Map — maps the current checked-out implementation across the complete Intent-to-Delivery Lifecycle, explains production execution admission, identifies exact capability boundaries, and distinguishes qualified code from configured production operation.