Team & Squad Model
ASDD adopts the squad-based structure pioneered by Spotify engineering. Each squad owns a business capability end-to-end and operates autonomously within shared architectural governance. The key evolution: AI agents are first-class squad members, not tools.
Organizational hierarchy
Tribe (Product Area)
A Tribe groups squads working within a related domain. Tribes share architectural standards, tooling, and a global Knowledge Agent that accumulates learning across squads.
Squads
Each squad owns a specific business capability from product intent to production operation. Squads are autonomous — they do not hand off work to other teams for implementation or QA.
Squad composition
| Role | Headcount | FTE | Primary Accountability |
|---|---|---|---|
| Product Owner | 1 shared across 2–3 squads | 0.5 | Business intent, spec approval, outcome acceptance |
| Tech Lead | 1 | 1.0 | Technical integrity, ASDD lifecycle enforcement, agent governance |
| Engineers | 1–3 | 1.0 each | Spec fidelity, agent output review, dissent notices |
| AI Agent System | 6–10 agents | — | Discovery, spec validation, design, implementation, QA, security, CI/CD |
A squad of 3 humans + 8 AI agents has the implementation throughput of a much larger traditional team, while retaining formal human authority over every architectural and business decision.
Role definitions
Product Owner
Accountability: Meaning and Business Value
The PO in ASDD is responsible for clarity of intent, not granularity of tasks. The shift from traditional product management is significant:
The PO does:
- Owns Capability Specs — structured descriptions of what the system should do, not how
- Defines business rules, constraints, and measurable success criteria
- Prioritizes the spec backlog and determines slice sequencing
- Validates and approves
intent.mdbefore Phase 1 begins - Accepts sprint outcomes based on spec compliance, not demo performance
- Exercises formal override authority when agent outputs conflict with business intent
The PO no longer does:
- Write detailed user stories mid-sprint
- Clarify requirements during implementation
- Negotiate scope after sprint start
One PO may serve 2–3 squads, depending on domain complexity. The time savings come from the elimination of mid-sprint clarification — specs must be approved before sprint start.
Tech Lead
Accountability: Flow, Quality, and Technical Integrity
The Tech Lead is the most critical role in the ASDD framework. All agent escalations route to the TL. All phase gates require TL sign-off.
Responsibilities:
- Enforces the ASDD lifecycle and phase gate discipline
- Facilitates sprint ceremonies and spec readiness reviews
- Ensures all specs pass the Validation Gate before sprint start
- Owns technical coherence: architecture, domain model, and steering rules
- Resolves all agent escalations requiring human judgment
- Maintains the Agent Failure Log (see Governance)
- Reviews and acknowledges Reasoning Traces at each phase gate
- Coaches the squad in ASDD practices
Override authority: Full, at every phase. The TL's decision on agent escalations is final within the squad.
Engineers
Accountability: Execution and Spec Fidelity
Engineers in ASDD are agent orchestrators and quality reviewers, not primary code authors. The work shifts from writing to directing and validating.
Responsibilities:
- Implement exactly what is defined in the spec — no undocumented behavior
- Identify spec gaps before sprint start (not during implementation)
- Write tests mapped to spec behaviors
- Emit events, logs, and metrics as defined in the spec
- Reject undocumented behavior — if it isn't in the spec, it doesn't ship
- File formal Dissent Notices when agent output is technically unsafe or spec-noncompliant
Engineer core principles:
- Specs before sprints — No work enters a sprint without an approved spec
- Single source of truth — The spec replaces scattered user stories and acceptance criteria
- Autonomous squads — Own the capability end-to-end; no cross-team handoffs
- Role clarity — Clear accountability eliminates ambiguous handoffs
- Flow efficiency — Optimize for fast, stable delivery over resource utilization
Your primary learning path in ASDD is through spec-fidelity review — understanding why an agent's output does or does not match the spec teaches both domain modeling and system design faster than writing implementation code directly.
The AI Agent System
The AI Agent System is not a single tool — it is a coordinated pipeline of 10 specialized agents, each with a defined role and confidence threshold. See the Agent Catalog for full specifications.
At the squad level, the agent system functions as a highly capable, always-available execution partner that:
- Works in parallel across multiple slices simultaneously
- Never blocks on human coordination (within its authority boundaries)
- Reports confidence scores, not just outputs
- Escalates automatically when confidence falls below threshold
Decision rights
Clear decision ownership is what makes autonomous squads safe. The following matrix defines who decides what:
| Decision | Owner |
|---|---|
| Business priority and spec sequencing | Product Owner |
| Spec approval (Phase 0 → 1) | PO + TL |
| Technical approach and architecture | Tech Lead |
| Implementation details within spec | Engineers |
| Agent output rejection (Dissent Notice) | Any team member |
| Agent escalation resolution | Tech Lead |
| Self-Healing PR approval | TL + at least one Engineer |
| Security gate bypass (emergency only) | TL (logged immutably) |
Any decision not on this table defaults to the Tech Lead. Ambiguous decision ownership is the most common cause of governance gaps.
The Knowledge Agent: shared intelligence
One Knowledge Agent operates at the Tribe level, accumulating learning across all squads. This is the system memory:
- Maintains the State Manifest for each squad's slices
- Detects conflicts when two squads modify the same shared domain entity
- Proposes steering rule updates based on cross-squad failure patterns
- Ensures context-fresh sub-agents receive only relevant domain terms
A single Knowledge Agent across 3–5 squads builds institutional knowledge faster than any human retrospective process.
Next
- Maturity Model — the L1–L6 progression for phased ASDD adoption
- Change Management — role transition guidance and resistance patterns
- Governance — the technical details of confidence scoring and dissent protocols