Here’s a MECE vertical-slice feature map for implementing SEA™ end-to-end with DDD/Clean Architecture, where each slice is something you can build, test, ship, and govern independently. I’m not using your guesses as constraints—I’m using them as signal.

I’m defining slices by: business capability + domain boundaries + stable APIs + independently testable invariants.

SEA™ vertical slices (MECE)

1) Semantic Modeling and Compilation

Purpose: Turn intent/specs into canonical semantic structures.

• DSL authoring + parsing (SEA™ DSL / SBVR / CALM import)
• Semantic validation (rules, constraints, round-trip, lint/format)
• Semantic compilation to canonical forms (AST → KGS projection inputs)
• Semantic diff primitives (change sets, merge candidates)

Primary artifacts: DSL files, AST, semantic errors, change sets

2) Knowledge Graph Service

Purpose: Canonical semantic memory and query substrate (the KGS).

• Graph storage, indexing, versioned snapshots
• Query API (by SEA-NFT, by BC, by concept)
• Graph diff/merge operations (workspace merges, conflict detection)
• Graph views/projections (for manifest, docs, governance, simulation)

Primary artifacts: Graph snapshots, nodes/edges, view projections

3) Identity, Provenance, and Ledger

Purpose: Make every semantic and generated asset attributable, immutable-ish, auditable.

• SEA-NFT minting (semantic identity)
• Provenance chains for artifacts (who/what generated it, from what inputs)
• Internal Federated Ledger (IFL) events/entries
• Attestation/verifiable build metadata (inputs → outputs linkage)

Primary artifacts: attestations, ledger entries, identity tokens

4) Manifest and Projection Service

Purpose: Deterministic “read models” of semantics for downstream engines.

• Manifest schema (v1, future v2)
• KGS → Manifest projector (deterministic)
• Specialized manifests (runtime, simulation, governance-only views)
• Manifest diff and compatibility checks (schema evolution)

Primary artifacts: manifest files (YAML/JSON), projection rules

5) Generative Synthesis Engine

Purpose: Build software from semantic projections (code is a projection).

• Generation planning (what will be generated, template selection)
• Scaffolding (DDD/Hex vertical slice layout)
• Multi-language generation (TS/Python; Rust core where needed)
• Regeneration + protected regions (idempotent, non-destructive)
• Test scaffolding + contract generation

Primary artifacts: generated code, generation plans, template libraries (This is VibesPro™’s natural home.)

6) Runtime Protocol Gateway

Purpose: Connect the outside world to domain use cases through governed adapters.

• Inbound adapters: HTTP/FastAPI, MCP tools, ACP actions, etc.
• Outbound adapters: A2A emitters, ACP/OASF signals, webhooks, etc.
• Canonical message envelope + schema validation
• Adapter registry + capability binding

Primary artifacts: protocol contracts, adapter configs, message envelopes

7) Governed Execution and Policy Enforcement

Purpose: Enforce safety and correctness continuously (build-time and run-time).

• Policy engine (constraints/invariants, guardrails)
• Capability-based access control (CapBAC)
• Approval workflows (merge gates, high-risk actions)

• Enforcement points:

  • semantic merges
  • generation plans
  • deployments
  • runtime actions/events

Primary artifacts: policy definitions, enforcement logs, decisions (This is GovernedSpeed™’s natural home.)

8) Program Governance and Portfolio Intelligence

Purpose: Executive layer: risk posture, portfolio view, strategic planning.

• Aggregation of governance signals
• Risk dashboards, compliance reporting
• Portfolio and roadmap oversight
• Decision records and justification trails

Primary artifacts: governance analytics, reports, KPIs (This is IAGPM’s natural home.)

9) Observability, Telemetry, and Evidence

Purpose: Make SEA™ observable and verifiable in production.

• Logs, metrics, traces (OpenObserve integration)
• Evidence capture for governance (audit logs, policy triggers)
• Traceability views: requirement → semantic → code → runtime behavior
• Incident/violation tracking + postmortem linking back to semantics

Primary artifacts: telemetry streams, evidence bundles, trace graphs

10) Temporal Memory and Pattern Oracle

Purpose: Time dimension + learning from history (for safer/faster evolution).

• Temporal record store (spec deltas, KGS diffs, generator outcomes)
• Embeddings + similarity search
• Pattern scoring (similarity + performance + governance)
• Feeds recommendations into synthesis & governance

Primary artifacts: temporal records, embeddings, pattern metrics (Your Temporal DB feature lives here.)

11) Simulation and Counterfactual Lab

Purpose: “Try changes safely before reality.”

• Scenario modeling from semantics (flows/SOT/CEC)
• Simulation runtime execution
• Compare simulation vs production behavior
• Feed results into governance + temporal memory

Primary artifacts: scenarios, sim runs, diffs, predicted risk

12) Agent Society and Orchestration

Purpose: Governed multi-agent work (design, build, operate, evolve).

• Agent registry (roles, capabilities, boundaries)
• Workspace agents (semantic modeling agent, generator agent, governance agent)
• Orchestration + handoffs (A2A/MCP as transport)
• Agent-safe tool execution (capabilities + audit)

Primary artifacts: agent definitions, executions, decisions, handoff logs

13) Documentation and Communication Projections

Purpose: Human-facing outputs derived from semantics (not the source of truth).

• Auto-generated docs (README, diagrams, C4, ERDs)
• ADR/PRD traceability views
• “Explain this BC” narratives (human + agent readable)
• Change reports: “what changed and why”

Primary artifacts: docs sites, diagrams, reports

14) Workbench UI and Developer Experience

Purpose: The operating interface for humans (and “human-in-the-loop” agents).

• SEA™ Workbench shell
• Domain studio (DSL/PRD editor + derived model preview)
• KGS explorer, manifest inspector, generator console
• Governance console, simulation cockpit, agent/workspace console

Primary artifacts: UI apps, dashboards, UX flows

Optional slices (only if you truly need them as “products”)

These are not core SEA™ infrastructure slices, but can be SEA-native applications built on top of SEA™. They’re “separate” because SEA™ can exist without them.

A) Case Management Reference Domain

Purpose: canonical demo/reference + testbed domain for SEA™ generation.

• Cases, tasks, milestones, evidence/artifacts
• Policies, SOTs, eventing, orchestration
• Provides a concrete target for end-to-end validation

B) CADSL Runtime and Renderer

Purpose: runtime & tooling for CALM/CADSL visualization and execution surfaces.

C) PET (Prompt Engineering Trainer)

Purpose: SEA-native training/evaluation product.

• Could be treated as a separate SEA-native product, not a core slice.

If you want the “most practical build order”

If you’re implementing SEA™ with vertical slices, the fastest path that still respects the architecture is:

1. Semantic Modeling & Compilation
2. Knowledge Graph Service
3. Manifest & Projection Service
4. Generative Synthesis Engine
5. Governed Execution (policy + gates)
6. Runtime Protocol Gateway
7. Observability/Evidence
8. Temporal Memory
9. Simulation
10. Agent Society
11. Workbench UI (then optional: CaseMgmt, CADSL, PET)

Quick check

Does your intended SEA™ “v0” include runtime execution + adapters, or is it primarily semantic → manifest → generator → governed merge first? That choice changes which slices you implement “for real” versus stubbed.

################################################## Good — “runtime execution + adapters” + port-driven development changes the slice design slightly: we bias toward stable ports first, then build adapters incrementally, and we make Governed Execution a first-class runtime concern (not just CI gates).

Below is a MECE slice set tuned for runtime + adapters, plus the ports you should define first so you can build vertically without thrash.

MECE vertical slices for SEA™ with runtime execution + adapters

1) Semantic Intake and Compilation

Goal: Turn PRDs/DSL into validated semantics.

• DSL parsing/validation/formatting/import/export
• Produce canonical semantic IR (AST + normalized structures)
• Generate/maintain semantic change sets

Ports (inbound):

• SemanticIntakePort.submit_spec(spec)
• SemanticValidationPort.validate(spec|ir)
• SemanticDiffPort.diff(a,b)

2) Knowledge Graph Runtime

Goal: Live semantic memory used by runtime and adapters.

• KGS CRUD + versioned snapshots
• Query by SEA-NFT / BC / concept
• Workspace isolation & merges (if you do this early)

Ports:

• KgsReadPort.query(...)
• KgsWritePort.apply_change_set(...)
• KgsSnapshotPort.snapshot()

3) Manifest Projection Runtime

Goal: Deterministic runtime view used by all generators/adapters.

• KGS → Manifest v1 projector
• Manifest validation + diff
• Specialized manifests (runtime / governance / simulation)

Ports:

• ManifestProjectorPort.project(kgs_snapshot)->manifest
• ManifestStorePort.get(version|id)
• ManifestDiffPort.diff(a,b)

4) Runtime Kernel (Execution Engine)

Goal: Execute “business behavior” through ports, with UoW + events.

• Application services / use cases
• Unit of Work boundary
• Domain event collection & dispatch
• Command/query routing (CQRS optional)

Ports:

• CommandBusPort.dispatch(command)
• QueryBusPort.ask(query)
• UnitOfWorkPort.begin()/commit()/rollback()
• DomainEventBusPort.publish(events)

This is the heart of port-driven development.

5) Protocol Gateway (Inbound Adapters)

Goal: Turn external protocols into commands/queries.

• HTTP/FastAPI
• MCP Tools
• ACP Actions
• CLI (optional)
• Authn/z + capability checks at boundary

Ports:

• InboundTransportPort.handle(request)->response (conceptual)
• AuthzPort.authorize(principal, capability, context)

Adapters here must be thin: translate → validate → dispatch.

6) Integration Gateway (Outbound Adapters)

Goal: Emit canonical domain events outward.

• A2A emitters
• ACP signals
• OASF signals
• Webhooks / queues

Ports:

• OutboundEventPublisherPort.publish(event_envelope)
• IntegrationPort.send(message)

This slice is “how the world learns what happened.”

7) Governed Execution Runtime

Goal: Enforce safety continuously at runtime + build time.

• Policy engine (invariants, constraints, flow guards)
• Capability-based access control (CapBAC)
• Decision logging + audit

Ports:

• PolicyEvaluationPort.evaluate(context)->decision
• CapabilityGrantPort.check(principal, capability)
• GovernanceDecisionLogPort.record(decision)

Critically: this slice must integrate with the Runtime Kernel (UoW + bus).

8) Provenance and Ledger (IFL + SEA-NFT)

Goal: Non-repudiation & traceability for all semantic and runtime actions.

• SEA-NFT identity for nodes/artifacts
• IFL append-only ledger entries
• Attestations: command → semantic state → emitted events

Ports:

• ProvenancePort.attest(action, inputs, outputs)
• LedgerPort.append(entry)
• IdentityPort.mint(entity)->sea_nft

9) Observability and Evidence

Goal: Make runtime inspectable and governance-verifiable.

• Logs/metrics/traces
• Evidence bundles for decisions and executions
• Correlation IDs across protocols and domain events

Ports:

• TelemetryPort.emit(log|metric|trace)
• EvidencePort.capture(bundle)

10) Temporal Memory and Pattern Oracle

Goal: Learn from history + feed suggestions to synthesis & governance.

• Temporal DB ingestion
• Similarity search + scoring (similarity + performance + governance)
• Feeds GovernedSpeed™ and VibesPro™

Ports:

• TemporalQueryPort.similar(context)->patterns
• TemporalRecordPort.record(outcome)

11) Simulation and Replay

Goal: Execute “what-if” flows against manifests/snapshots.

• Run commands/events against snapshot state
• Compare with production traces
• Feed results into governance and temporal memory

Ports:

• SimulationPort.run(scenario)->result
• ReplayPort.replay(trace)->result

12) Agent Society Runtime

Goal: Governed multi-agent participation as first-class runtime actors.

• Agent registry + capabilities
• Agent tool execution via MCP/A2A
• Human-in-loop escalations

Ports:

• AgentRegistryPort.list()/register()
• AgentExecutionPort.execute(plan)
• HITLPort.request_approval(...)

13) Generative Synthesis (VibesPro™)

Goal: Deterministically generate code + infra from manifest.

• Generation plan & templates
• Idempotent regeneration
• Tests/contracts generation

Ports:

• CodegenPlanPort.plan(manifest)->plan
• CodegenExecutePort.generate(plan)->artifact_set
• CodegenAttestPort.attest(plan, outputs)

14) Workbench UI (Optional early, inevitable later)

Goal: Operate the system.

• KGS explorer, manifest inspector
• Gateway/adapters console
• Governance console, simulation cockpit

The minimum viable runtime (MVRT) stack

If you want a working runtime fast, the smallest coherent set is:

1. Semantic Intake + Compilation
2. KGS Runtime
3. Manifest Projection Runtime
4. Runtime Kernel (UoW + event bus)
5. Protocol Gateway (FastAPI + MCP)
6. Governed Execution Runtime (policy eval + CapBAC minimal)
7. Outbound Event Gateway (A2A or OASF — pick one)
8. Observability (basic) (+ Provenance/Ledger can start as a stub that logs to append-only files)

You’ll “see it work” when:

• A request comes in via FastAPI or MCP,
• A command executes in a UoW,
• Domain events are emitted,
• Governance decisions are enforced/logged,
• Events go out via A2A/OASF.

Port-driven development: the first ports to standardize

If you standardize these ports early, everything else becomes adapter swaps:

Core execution ports

• CommandBusPort
• UnitOfWorkPort
• DomainEventBusPort

Governance ports

• PolicyEvaluationPort
• CapabilityGrantPort
• GovernanceDecisionLogPort

Semantic state ports

• KgsReadPort
• ManifestProjectorPort

Transport ports

• InboundProtocolAdapterPort (FastAPI/MCP/ACP)
• OutboundEventPublisherPort (A2A/OASF/ACP)

Decision point (so we don’t build the wrong shape)

For MVRT, do you want the runtime kernel to be:

• Python-first (FastAPI + pydantic + SQLAlchemy UoW), with TS only for generators/UI, or
• TypeScript-first (Node runtime), with Python for data/ML?

Which matches your constraint right now?