f93e8baef1
Smart Support - AI customer service action layer framework. Includes design doc, CEO plan, eng review, test plan, and README.
26 lines
2.4 KiB
Markdown
26 lines
2.4 KiB
Markdown
# TODOS
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## Before Phase 3
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- [ ] **Tool interface decision:** The tool layer should support multiple backends, not just MCP:
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1. **MCP tools** — for complex, stateful interactions via MCP protocol (stdio/SSE)
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2. **CLI tools** — wrap existing CLIs (Shopify CLI, AWS CLI, Stripe CLI, etc.). Parse stdout/stderr.
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3. **Direct API tools** — simple REST/GraphQL HTTP calls, no MCP overhead.
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LangChain tools are just Python functions with descriptions — the backend is an implementation detail. Research MCP Python SDK (`mcp` on PyPI) for the MCP path. Design the tool base class to abstract over all three backends. ~2-3 hours research. Flagged by eng review outside voice + user feedback.
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## Before Production Deployment (P1)
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- [ ] **Auth system:** API key auth for chat WebSocket, session-based auth for dashboard/replay/import. Rate limiting on all endpoints. Blocks any real client deployment. Effort: M (CC: ~2 days). Depends on: Phase 4 completion.
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## Before Phase 4 (Client Engagement)
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- [ ] **Checkpointer migration plan:** InMemorySaver → PostgresSaver (or SQLiteSaver as intermediate). InMemorySaver loses all state on restart/crash. PostgresSaver requires schema, connection pooling, serialization compatibility. Not a simple config swap. Plan the migration before any real client deployment.
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## Design Changes from Eng Review
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- [x] **Use LangGraph built-ins:** Checkpointers for session state, interrupt() for human-in-the-loop, supervisor pattern for multi-agent routing. Don't rebuild what LangGraph provides.
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- [x] **WebSocket for streaming:** Bidirectional connection for streaming tokens + interrupt flow.
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- [x] **Supervisor pattern:** Despite latency concern (8-15s per response), founder chose multi-agent supervisor over single-agent. Stream all tokens to mitigate perceived wait.
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- [x] **YAML agent registry:** Declarative agent definitions for client configurability.
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- [x] **Prompt caching:** Enabled from day one to reduce LLM costs.
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- [x] **Multi-LLM provider support:** Use LangChain's provider abstractions (ChatAnthropic, ChatOpenAI, ChatGoogleGenerativeAI). Provider configurable per deployment.
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- [x] **Multi-backend tool support:** Tool layer supports MCP servers, CLI wrappers, and direct API calls. LangChain tools abstract over all three backends.
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- [x] **Interrupt resume flow:** Design WebSocket reconnection + re-send interrupt prompt on reconnect.
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- [x] **Tests per phase:** 28 unit/integration + 4 E2E, written alongside each phase.
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