ASDD System Architecture
This section is the visual architecture reference for ASDD v5.0/6.0. All diagrams are rendered from Mermaid source.
1. The Big Picture: ASDD Master System
The complete operating model showing how product strategy, specifications, AI-augmented squads, and the State Manifest backbone interact.
2. Architectural Layers
The six layers that define the ASDD structural hierarchy.
| Layer | What it contains | Human or Agent owned? |
|---|---|---|
| Product | Strategy, product intent, MVP scope | Human (PO) |
| Specification | EARS requirements, domain contracts | Human-approved, Agent-assisted |
| State | manifest.json — pipeline heartbeat | Knowledge Agent (maintained) |
| Architecture | Design, ADRs, component maps | Agent-synthesized, Human-approved |
| Agent Execution | Code generation, testing, security scans | Agent (with human phase gates) |
| Platform | CI/CD pipelines, observability, telemetry | Agent-automated, Human-governed |
3. Organizational Architecture: Tribes and Squads
A single Global Knowledge Agent accumulates learning across all squads in the tribe — surfacing cross-squad patterns that individual squads cannot see.
4. AI Agent Orchestration Pipeline
The high-velocity pipeline showing CCS gates and parallel wave execution.
5. Lifecycle: Behavioral Slicing
The lifecycle is slice-based, not monolithic. Each slice (feature, bug, improvement) flows through the pipeline independently.
Behavioral Slicing means the team does not wait for all features to be specified before any implementation begins. Slices flow through the pipeline in parallel — a feature in Wave Implementation while a bug fix is in Spec Validation.
6. Runtime Architecture: Workflow-Driven Execution
Agents are not just prompts — they are Workflow Executors that interact with the system via deterministic tools.
Agents must call asdd-tools.js to update manifest state — they cannot edit manifest.json directly. This ensures every state transition is validated and logged.
7. Specification-to-Code Traceability
Full-spectrum traceability from product intent to individual lines of code and production metrics.
Every line of production code traces back to a specific requirement. Every requirement traces back to the approved intent. This traceability chain is what makes ASDD auditable.
8. Autonomous Delivery Loop: The Harmonizer
The Harmonizer maintains system health by detecting conflicts early. Discovery Spike Agents resolve uncertainty automatically when possible — only escalating to humans when no Steering Rule covers the conflict.
9. Repository Structure
See Repository Structure for the complete directory reference.
10. AI Governance: The CCS Model
The Product Law of Confidence ensures AI autonomy is earned through verified quality.
| Metric | Rule |
|---|---|
| Individual threshold | Per-agent minimum (0.75–0.95 depending on agent) |
| CCS threshold | 0.65 — product of all agent scores in the pipeline path |
| Dynamic gating | If a preceding agent scores low (but above threshold), the next agent's minimum increases by +0.05 |
| Uncertainty factors | Required when score < 0.95 — agents must list what they are uncertain about |
Architecture summary
| Diagram | Key concept |
|---|---|
| 1 — Master System | State Manifest Backbone as the coordination hub |
| 2 — Layers | Six-layer structural hierarchy |
| 3 — Organization | Tribes, squads, and Global Knowledge Agent |
| 4 — Agent Pipeline | CCS gates and parallel wave execution |
| 5 — Lifecycle | Behavioral Slicing and JIT Validation |
| 6 — Runtime | Workflow-driven orchestration via asdd-tools |
| 7 — Traceability | Intent → code → production metrics |
| 8 — Delivery Loop | Harmonizer and Discovery Spike Agents |
| 9 — Repository | .asdd/ (workflows, tooling, state, specs, steering) |
| 10 — Governance | Product Law of Confidence (CCS > 0.65) |