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Fully-native LangGraph example app exercising multi-agent orchestration with SDOT auto-instrumentation. Replaces the previous ad-hoc CRM demo with proper LangGraph patterns: TypedDict state, @tool-decorated functions, ChatOpenAI.bind_tools(), ToolNode, and conditional edges. - Records Agent: MongoDB Atlas vector search for orders, tickets, refunds - Policy Agent: RAG-based policy retrieval with drift mode support - Action Agent: LLM-driven tool selection via bind_tools (no manual SDOT) - Audit Agent: Rationale generation with citations - 7 scenarios covering refunds, escalations, order inquiries, hallucination
The action_agent → tool_executor → action_summarise loop created multiple invoke_agent spans. Collapse the tool-calling loop into a single action_node so the trace shape matches the original CRM app: exactly one invoke_agent span per agent (Records, Policy, Action, Audit).
Close behavioral gaps identified by side-by-side review: - Orders vector search: limit 3 → 1 (match old atlas_client.py) - Policy region detection: add missing country codes (USA, ITALY, SPAIN) and default-to-EU for unknown countries - create_ticket / update_ticket: restore `comments` field - Cost model: restore per-tool variable costs instead of flat rate - Resolution logic: add "no_action_required", "refund_state_explained", and "action_failed" to match old _determine_resolution() - AuditOutput: restore `span_ids` field
Extract the monolithic app.py into app/ package matching the original CRM demo repo structure: agents/, models/, rag/, tools/, graph.py. No logic changes — just reorganization for maintainability.
- Pass RunnableConfig to action_node and summarize_node so graph-level callbacks propagate to inner LLM and tool calls - Tools now return JSON strings for reliable output capture - Simplified explain_refund_state and explain_order_state DB lookups - Improved Action Agent prompt for better context adherence
Add scenarios designed to exercise all 15 Galileo eval metrics: - pii_leak_refund: input_pii + output_pii (PII in query, tool echoes it) - prompt_injection_attempt: prompt_injection, context_adherence - toxic_abusive_customer: input_toxicity, input_tone - incomplete_multi_request: completeness, action_completion - tool_failure_scenario: tool_error_rate, action_advancement - vague_rambling_query: agent_efficiency, tool_selection_quality - hostile_context_leakage: output_tone, output_toxicity Add run-sdot-batch.sh for batch execution with mixed scenario plan (30% baseline, 70% metric triggers) and random delay between runs.
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feat: add CRM Ops Desk LangGraph multi-agent demo
Fully-native LangGraph example app exercising multi-agent orchestration
with SDOT auto-instrumentation. Replaces the previous ad-hoc CRM demo
with proper LangGraph patterns: TypedDict state, @tool-decorated
functions, ChatOpenAI.bind_tools(), ToolNode, and conditional edges.