f93e8baef1
Smart Support - AI customer service action layer framework. Includes design doc, CEO plan, eng review, test plan, and README.
3.7 KiB
status
| status |
|---|
| ACTIVE |
CEO Plan: Smart Support — AI Customer Service Action Layer Framework
Generated by /plan-ceo-review on 2026-03-29 Branch: unknown | Mode: SCOPE EXPANSION Repo: smart-support
Vision
10x Check
A framework that comes alive the moment a client connects their system. Client pastes an OpenAPI spec URL, the framework auto-generates MCP tool wrappers and agent definitions, and a working chatbot appears in minutes. Every conversation is logged with full replay capability. Analytics dashboard shows ROI in real-time. The client doesn't configure anything. They paste a URL.
Platonic Ideal
Open smart-support.com. Paste your Shopify store URL. In 90 seconds, a chat widget appears connected to your store. Type "cancel order #1042" and watch the agent look up the order, ask for confirmation, and cancel it. No setup. No config. No code. Deploy to Zendesk with one click. Dashboard shows 60% automated resolution rate and $4,200/month savings. Sleep at night knowing every destructive action requires human approval, every action is logged, and the system pages you if something unusual happens.
Scope Decisions
| # | Proposal | Effort | Decision | Reasoning |
|---|---|---|---|---|
| 1 | Auto-discovery from OpenAPI specs | L (CC: ~3-4 days) | ACCEPTED | 10x differentiator. "Paste URL, get chatbot." |
| 2 | Conversation analytics dashboard | M (CC: ~2-3 days) | ACCEPTED | Proves ROI. Makes the product sticky. |
| 3 | Agent personality config (YAML) | S (CC: ~1 hour) | ACCEPTED | Near-zero cost, customizable feel. |
| 4 | Conversation replay / debugger | M (CC: ~2 days) | ACCEPTED | Trust = adoption. Clients need to see WHY. |
| 5 | Webhook escalation to Slack/email | S (CC: ~1 hour) | ACCEPTED | Bridge between AI-only and human-in-the-loop. |
| 6 | Quick-start vertical templates | S (CC: ~30 min) | ACCEPTED | First 5 minutes feel magical. |
Accepted Scope (added to this plan)
- Auto-discovery: parse OpenAPI/Swagger specs, generate MCP tool wrappers + agent YAML
- Analytics dashboard: resolution rate, turns, agent usage, common intents, escalation %
- Agent personality: tone/greeting/escalation style configurable in YAML
- Conversation replay: step-by-step replay of agent decisions, tool calls, results
- Webhook escalation: HTTP POST with full conversation context on escalation
- Vertical templates: pre-built YAML for e-commerce, SaaS, fintech
Revised Phasing (with expansions)
- Phase 1 (Week 1): Chat UI (React) + FastAPI + LangGraph graph with PostgresSaver checkpointer + agent registry from YAML + single mock agent + agent personality config + tests
- Phase 2 (Week 2): Multi-agent supervisor (uses registry from Phase 1) + vertical templates (e-commerce, SaaS, fintech) + interrupt() for write ops + webhook escalation + tests
- Phase 3 (Week 2-3): OpenAPI auto-discovery: parse spec (REST, OpenAPI 3.0+), generate tool wrappers + agent YAML. SSRF protection on URL import. Pluggable tool interface (MCP/CLI/API backends).
- Phase 4 (Week 3-4): Conversation replay UI (browsable checkpointer state) + analytics dashboard (resolution rate, agent usage, escalation %, conversation count). PostgreSQL schema for conversation data locked in Phase 1.
- Phase 5 (client engagement): Real connectors for first client's systems.
Dependencies resolved: Agent registry ships in Phase 1 (before supervisor in Phase 2). Conversation data schema locked in Phase 1 so Phase 4 analytics can query it without migration.
Deferred to TODOS.md
- (none — all proposals accepted)
Effort Estimate
- Original: 3 weeks (1 engineer)
- With expansions: 4-5 weeks (1 engineer). The expansions add ~1.5-2 weeks.
- With CC+gstack: 2-3 weeks realistic.