docs(roadmaps): refine agent platform roadmap decisions
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@ -34,6 +34,25 @@ can be sequenced without mixing concerns.
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---
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## Default Platform Decisions
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To make the roadmap executable, these should be treated as the default assumptions
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unless a later ADR explicitly changes them:
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- **Control plane remains in `platform-service`.**
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- **`mcp-server` remains an execution and tooling surface, not the system of record.**
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- **`@bytelyst/queue` remains the immediate job primitive.**
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- **Redis is the first new infrastructure dependency to add** for durable eventing,
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coordination, leases, and future worker scale-out.
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- **Do not introduce Temporal in v1.** Design for Temporal compatibility, but do not
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pay the operational cost before the platform proves it needs workflow-native semantics.
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- **Do not force all new domains into Cosmos.** Relational domains should be allowed to
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move to PostgreSQL when they become awkward or risky in document storage.
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- **Human review, governance, and budget controls are mandatory platform capabilities,**
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not optional product features.
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---
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## Roadmap Set
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1. [Agent Runtime & Orchestration Roadmap](./platform_AGENT_RUNTIME_ORCHESTRATION_ROADMAP.md)
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@ -90,6 +109,44 @@ foundations and the missing layers:
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---
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## Dependency Graph
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The ten roadmaps are not independent. The strongest sequencing is:
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1. **Durable Event Bus & Worker Runtime**
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This provides the durable execution substrate for the rest.
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2. **Agent Runtime & Orchestration**
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Depends on durable workers and queue conventions.
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3. **Org, Workspace & RBAC**
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Required before tenant-safe approval, retrieval, and enterprise provisioning.
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4. **Human Review & Approval Queue**
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Depends on agent runs and tenant boundaries.
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5. **Agent Registry & Prompt Versioning**
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Depends on agent runs for production traceability and on governance for release gates.
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6. **AI Governance & Evaluation**
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Depends on registry/versioning to attach decisions to concrete artifacts.
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7. **AI Budget & Cost Governance**
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Depends on tenant model and run/request attribution.
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8. **Knowledge & RAG Service**
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Depends on tenant boundaries for safe retrieval.
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9. **Enterprise Provisioning & SCIM**
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Depends on org/workspace/team model.
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10. **Support Case Management**
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Depends on runs, reviews, tenanting, and diagnostics links to be genuinely useful.
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---
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## Success Criteria For The Roadmap Set
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This roadmap family should be considered successful only when it produces:
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- a clear execution order rather than ten disconnected ideas
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- a default architecture stance for this repo
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- explicit separation between **best industry-standard stack** and **best immediate fit**
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- enough detail to derive implementation epics without rewriting the docs from scratch
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---
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## Architectural Guidance
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These docs assume the current repo direction remains:
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@ -104,6 +161,8 @@ than the current Cosmos-first platform modules. Each roadmap therefore includes:
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- a **recommended stack** for long-term quality
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- a **repo-fit alternative** that stays closer to current conventions
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- a **default decision for this repo now**
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- explicit **dependencies** and **v1 non-goals**
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That is intentional. The best industry-standard choice is not always the same as
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the least disruptive repo-local choice.
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@ -31,6 +31,27 @@ and docs rather than treated as versioned platform data.
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- Prompt artifacts stored in blob storage
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- MCP server resolves active versions from `platform-service`
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### Default Decision For This Repo
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- Put the system of record in `platform-service`
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- Store large prompt assets in blob storage
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- Require `agentVersion` and `promptVersion` on every durable run
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---
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## Dependencies
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- Agent Runtime & Orchestration
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- AI Governance & Evaluation
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---
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## V1 Non-Goals
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- Non-technical prompt editing UI
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- Marketplace-style public agent publishing
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- Full GitOps-only release flow
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---
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## Phase 1 - Core Registry
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@ -47,6 +47,30 @@ That is enough for prototypes. It is not enough for:
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Start with the repo-fit option to get durable runs quickly, but design the run model
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so a later move to Temporal is possible without rewriting every agent contract.
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### Default Decision For This Repo
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- Build v1 in `platform-service`
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- Use `@bytelyst/queue` as the execution primitive
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- Use Redis later for leases and distributed coordination
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- Keep `mcp-server` as a client/executor, not the run system of record
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---
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## Dependencies
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- Durable Event Bus & Worker Runtime
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- Human Review & Approval Queue
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- Agent Registry & Prompt Versioning
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---
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## V1 Non-Goals
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- Full Temporal adoption
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- Visual workflow builders
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- Arbitrary user-authored workflow DSLs
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- Multi-region active-active workflow execution
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---
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## Phase 1 - Canonical Run Model
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@ -38,6 +38,27 @@ The repo has usage and billing modules, but not a dedicated AI cost governance l
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This fits naturally in `platform-service`. The key is not the datastore; it is the
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quality of attribution and enforcement.
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### Default Decision For This Repo
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- Implement this inside `platform-service`
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- Treat run/request attribution as mandatory
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- Make budget checks part of the request path for expensive AI calls, not reporting-only
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---
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## Dependencies
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- Org, Workspace & RBAC
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- Agent Runtime & Orchestration
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---
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## V1 Non-Goals
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- Full finance ERP integration
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- Provider contract optimization tooling
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- Perfect real-time cost accuracy across every vendor
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---
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## Phase 1 - Usage Ledger
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@ -36,6 +36,27 @@ What it does not have is a central AI governance surface that answers:
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- Promptfoo or a similar eval harness for offline regression
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- policy layer using code-first rules first, with optional Cedar or OPA later
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### Default Decision For This Repo
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- Use Promptfoo-style offline evals first
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- Keep policy enforcement in platform code before introducing a separate policy runtime
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- Require governance links to agent and prompt versions rather than free-floating eval runs
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---
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## Dependencies
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- Agent Registry & Prompt Versioning
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- Agent Runtime & Orchestration
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---
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## V1 Non-Goals
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- Universal automated red-teaming
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- Full legal/compliance policy engine
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- Replacing feature flags or experiments
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---
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## Phase 1 - Eval Registry
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@ -46,6 +46,28 @@ layer is still incomplete.
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Use `@bytelyst/events` as the interface, but add a durable Redis or NATS adapter.
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Do not let direct in-memory emitters remain the production default for critical flows.
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### Default Decision For This Repo
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- Keep `@bytelyst/events` as the public interface
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- Add Redis-backed durability first
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- Keep memory adapters for tests only
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- Standardize worker bootstrap across services before adding more background features
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---
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## Dependencies
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- None for the core substrate
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- Required by Agent Runtime, Delivery side effects, and future SCIM sync jobs
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---
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## V1 Non-Goals
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- Kafka-scale event streaming
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- Exactly-once delivery guarantees
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- Cross-region event replication
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---
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## Phase 1 - Event Abstraction
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@ -31,6 +31,27 @@ It does not solve enterprise lifecycle management:
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- org/workspace mapping from the tenant model
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- optional background sync jobs using `@bytelyst/queue`
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### Default Decision For This Repo
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- Keep enterprise provisioning in `platform-service`
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- Implement SCIM only after org/workspace/team exists
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- Use queued reconciliation jobs for drift repair and large sync batches
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---
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## Dependencies
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- Org, Workspace & RBAC
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- Durable Event Bus & Worker Runtime
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---
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## V1 Non-Goals
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- Supporting every optional SCIM feature extension
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- Bi-directional identity sync with every enterprise system
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- Full HRIS integration
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---
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## Phase 1 - SCIM Foundations
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@ -31,6 +31,27 @@ also needs a generic review queue for cases like:
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- Slack and Telegram delivery adapters for reviewer notifications
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- Optional policy engine later with OpenFGA or Cedar
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### Default Decision For This Repo
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- Build this inside `platform-service`
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- Use agent-run state transitions rather than bespoke approval callbacks per feature
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- Reuse existing delivery channels for reviewer notification fan-out
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---
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## Dependencies
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- Agent Runtime & Orchestration
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- Org, Workspace & RBAC
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---
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## V1 Non-Goals
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- Full BPM suite
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- Arbitrary multi-stage approval designers
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- End-user-facing case portals
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---
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## Phase 1 - Review Objects
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@ -47,6 +47,27 @@ Use PostgreSQL + pgvector if you want the strongest balance of flexibility,
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ownership, and industry-standard retrieval patterns. Azure AI Search is a valid
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alternative if deep Azure integration matters more than datastore simplicity.
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### Default Decision For This Repo
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- Keep ingestion orchestration in `platform-service`
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- Reuse `extraction-service` for parsing/chunk preparation where it makes sense
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- Do not use Cosmos as the long-term primary RAG engine
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---
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## Dependencies
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- Org, Workspace & RBAC
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- AI Budget & Cost Governance
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---
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## V1 Non-Goals
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- General web-scale crawling
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- Training or fine-tuning custom foundation models
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- Cross-tenant shared retrieval by default
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---
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## Phase 1 - Knowledge Objects
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@ -45,6 +45,28 @@ If tenanting will be central to the business, PostgreSQL is the better long-term
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fit because org/workspace membership is relational by nature. If short-term
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consistency matters more, start in Cosmos but keep the permission model portable.
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### Default Decision For This Repo
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- Keep auth identity in `platform-service`
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- Add org/workspace/team as first-class platform modules
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- Start with service-level authorization helpers
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- Reserve OpenFGA for phase 2+ if sharing rules become too rich for code-level RBAC
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---
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## Dependencies
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- None for the first schema pass
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- Required by Knowledge & RAG, Human Review, AI Budgets, and SCIM
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---
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## V1 Non-Goals
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- Full ABAC across every existing module
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- End-user custom roles
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- Cross-product federation of every resource type on day one
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---
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## Phase 1 - Data Model
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@ -32,6 +32,28 @@ Without a case system, support work becomes fragmented across logs, chat, and ad
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This can live comfortably in `platform-service`. If the case domain becomes highly
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relational, PostgreSQL is better. Otherwise a Cosmos-backed module is acceptable.
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### Default Decision For This Repo
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- Build the case system in `platform-service`
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- Link cases directly to run IDs, review IDs, and diagnostics sessions
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- Treat external helpdesk sync as secondary, not the system of record
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---
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## Dependencies
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- Agent Runtime & Orchestration
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- Human Review & Approval Queue
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- Org, Workspace & RBAC
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---
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## V1 Non-Goals
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- Full CRM replacement
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- Multi-channel customer messaging inbox
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- External vendor-specific support automation
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---
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## Phase 1 - Core Case Model
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