docs(roadmaps): refine agent platform roadmap decisions

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@ -34,6 +34,25 @@ can be sequenced without mixing concerns.
---
## Default Platform Decisions
To make the roadmap executable, these should be treated as the default assumptions
unless a later ADR explicitly changes them:
- **Control plane remains in `platform-service`.**
- **`mcp-server` remains an execution and tooling surface, not the system of record.**
- **`@bytelyst/queue` remains the immediate job primitive.**
- **Redis is the first new infrastructure dependency to add** for durable eventing,
coordination, leases, and future worker scale-out.
- **Do not introduce Temporal in v1.** Design for Temporal compatibility, but do not
pay the operational cost before the platform proves it needs workflow-native semantics.
- **Do not force all new domains into Cosmos.** Relational domains should be allowed to
move to PostgreSQL when they become awkward or risky in document storage.
- **Human review, governance, and budget controls are mandatory platform capabilities,**
not optional product features.
---
## Roadmap Set
1. [Agent Runtime & Orchestration Roadmap](./platform_AGENT_RUNTIME_ORCHESTRATION_ROADMAP.md)
@ -90,6 +109,44 @@ foundations and the missing layers:
---
## Dependency Graph
The ten roadmaps are not independent. The strongest sequencing is:
1. **Durable Event Bus & Worker Runtime**
This provides the durable execution substrate for the rest.
2. **Agent Runtime & Orchestration**
Depends on durable workers and queue conventions.
3. **Org, Workspace & RBAC**
Required before tenant-safe approval, retrieval, and enterprise provisioning.
4. **Human Review & Approval Queue**
Depends on agent runs and tenant boundaries.
5. **Agent Registry & Prompt Versioning**
Depends on agent runs for production traceability and on governance for release gates.
6. **AI Governance & Evaluation**
Depends on registry/versioning to attach decisions to concrete artifacts.
7. **AI Budget & Cost Governance**
Depends on tenant model and run/request attribution.
8. **Knowledge & RAG Service**
Depends on tenant boundaries for safe retrieval.
9. **Enterprise Provisioning & SCIM**
Depends on org/workspace/team model.
10. **Support Case Management**
Depends on runs, reviews, tenanting, and diagnostics links to be genuinely useful.
---
## Success Criteria For The Roadmap Set
This roadmap family should be considered successful only when it produces:
- a clear execution order rather than ten disconnected ideas
- a default architecture stance for this repo
- explicit separation between **best industry-standard stack** and **best immediate fit**
- enough detail to derive implementation epics without rewriting the docs from scratch
---
## Architectural Guidance
These docs assume the current repo direction remains:
@ -104,6 +161,8 @@ than the current Cosmos-first platform modules. Each roadmap therefore includes:
- a **recommended stack** for long-term quality
- a **repo-fit alternative** that stays closer to current conventions
- a **default decision for this repo now**
- explicit **dependencies** and **v1 non-goals**
That is intentional. The best industry-standard choice is not always the same as
the least disruptive repo-local choice.

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@ -31,6 +31,27 @@ and docs rather than treated as versioned platform data.
- Prompt artifacts stored in blob storage
- MCP server resolves active versions from `platform-service`
### Default Decision For This Repo
- Put the system of record in `platform-service`
- Store large prompt assets in blob storage
- Require `agentVersion` and `promptVersion` on every durable run
---
## Dependencies
- Agent Runtime & Orchestration
- AI Governance & Evaluation
---
## V1 Non-Goals
- Non-technical prompt editing UI
- Marketplace-style public agent publishing
- Full GitOps-only release flow
---
## Phase 1 - Core Registry

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@ -47,6 +47,30 @@ That is enough for prototypes. It is not enough for:
Start with the repo-fit option to get durable runs quickly, but design the run model
so a later move to Temporal is possible without rewriting every agent contract.
### Default Decision For This Repo
- Build v1 in `platform-service`
- Use `@bytelyst/queue` as the execution primitive
- Use Redis later for leases and distributed coordination
- Keep `mcp-server` as a client/executor, not the run system of record
---
## Dependencies
- Durable Event Bus & Worker Runtime
- Human Review & Approval Queue
- Agent Registry & Prompt Versioning
---
## V1 Non-Goals
- Full Temporal adoption
- Visual workflow builders
- Arbitrary user-authored workflow DSLs
- Multi-region active-active workflow execution
---
## 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
This fits naturally in `platform-service`. The key is not the datastore; it is the
quality of attribution and enforcement.
### Default Decision For This Repo
- Implement this inside `platform-service`
- Treat run/request attribution as mandatory
- Make budget checks part of the request path for expensive AI calls, not reporting-only
---
## Dependencies
- Org, Workspace & RBAC
- Agent Runtime & Orchestration
---
## V1 Non-Goals
- Full finance ERP integration
- Provider contract optimization tooling
- Perfect real-time cost accuracy across every vendor
---
## 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:
- Promptfoo or a similar eval harness for offline regression
- policy layer using code-first rules first, with optional Cedar or OPA later
### Default Decision For This Repo
- Use Promptfoo-style offline evals first
- Keep policy enforcement in platform code before introducing a separate policy runtime
- Require governance links to agent and prompt versions rather than free-floating eval runs
---
## Dependencies
- Agent Registry & Prompt Versioning
- Agent Runtime & Orchestration
---
## V1 Non-Goals
- Universal automated red-teaming
- Full legal/compliance policy engine
- Replacing feature flags or experiments
---
## Phase 1 - Eval Registry

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@ -46,6 +46,28 @@ layer is still incomplete.
Use `@bytelyst/events` as the interface, but add a durable Redis or NATS adapter.
Do not let direct in-memory emitters remain the production default for critical flows.
### Default Decision For This Repo
- Keep `@bytelyst/events` as the public interface
- Add Redis-backed durability first
- Keep memory adapters for tests only
- Standardize worker bootstrap across services before adding more background features
---
## Dependencies
- None for the core substrate
- Required by Agent Runtime, Delivery side effects, and future SCIM sync jobs
---
## V1 Non-Goals
- Kafka-scale event streaming
- Exactly-once delivery guarantees
- Cross-region event replication
---
## Phase 1 - Event Abstraction

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@ -31,6 +31,27 @@ It does not solve enterprise lifecycle management:
- org/workspace mapping from the tenant model
- optional background sync jobs using `@bytelyst/queue`
### Default Decision For This Repo
- Keep enterprise provisioning in `platform-service`
- Implement SCIM only after org/workspace/team exists
- Use queued reconciliation jobs for drift repair and large sync batches
---
## Dependencies
- Org, Workspace & RBAC
- Durable Event Bus & Worker Runtime
---
## V1 Non-Goals
- Supporting every optional SCIM feature extension
- Bi-directional identity sync with every enterprise system
- Full HRIS integration
---
## Phase 1 - SCIM Foundations

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@ -31,6 +31,27 @@ also needs a generic review queue for cases like:
- Slack and Telegram delivery adapters for reviewer notifications
- Optional policy engine later with OpenFGA or Cedar
### Default Decision For This Repo
- Build this inside `platform-service`
- Use agent-run state transitions rather than bespoke approval callbacks per feature
- Reuse existing delivery channels for reviewer notification fan-out
---
## Dependencies
- Agent Runtime & Orchestration
- Org, Workspace & RBAC
---
## V1 Non-Goals
- Full BPM suite
- Arbitrary multi-stage approval designers
- End-user-facing case portals
---
## Phase 1 - Review Objects

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@ -47,6 +47,27 @@ Use PostgreSQL + pgvector if you want the strongest balance of flexibility,
ownership, and industry-standard retrieval patterns. Azure AI Search is a valid
alternative if deep Azure integration matters more than datastore simplicity.
### Default Decision For This Repo
- Keep ingestion orchestration in `platform-service`
- Reuse `extraction-service` for parsing/chunk preparation where it makes sense
- Do not use Cosmos as the long-term primary RAG engine
---
## Dependencies
- Org, Workspace & RBAC
- AI Budget & Cost Governance
---
## V1 Non-Goals
- General web-scale crawling
- Training or fine-tuning custom foundation models
- Cross-tenant shared retrieval by default
---
## 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
fit because org/workspace membership is relational by nature. If short-term
consistency matters more, start in Cosmos but keep the permission model portable.
### Default Decision For This Repo
- Keep auth identity in `platform-service`
- Add org/workspace/team as first-class platform modules
- Start with service-level authorization helpers
- Reserve OpenFGA for phase 2+ if sharing rules become too rich for code-level RBAC
---
## Dependencies
- None for the first schema pass
- Required by Knowledge & RAG, Human Review, AI Budgets, and SCIM
---
## V1 Non-Goals
- Full ABAC across every existing module
- End-user custom roles
- Cross-product federation of every resource type on day one
---
## 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
This can live comfortably in `platform-service`. If the case domain becomes highly
relational, PostgreSQL is better. Otherwise a Cosmos-backed module is acceptable.
### Default Decision For This Repo
- Build the case system in `platform-service`
- Link cases directly to run IDs, review IDs, and diagnostics sessions
- Treat external helpdesk sync as secondary, not the system of record
---
## Dependencies
- Agent Runtime & Orchestration
- Human Review & Approval Queue
- Org, Workspace & RBAC
---
## V1 Non-Goals
- Full CRM replacement
- Multi-channel customer messaging inbox
- External vendor-specific support automation
---
## Phase 1 - Core Case Model