feat: add aiops payload query augmentation

interview/aiops-query-augmentation.md

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+# AIOps Query Augmentation
+
+## What Changed
+
+Payload-targeted AIOps prompts now include a deterministic recommended knowledge query.
+
+The query is built from the non-blank payload fields:
+
+```text
+alertName service severity description timeRange userRequest
+```
+
+Example:
+
+```text
+HighCPUUsage payment-service P1 CPU usage is above 80% last_15m
+```
+
+## Why This Matters
+
+AIOps payload fields contain high-value retrieval terms:
+
+- alert name
+- service name
+- severity
+- symptom description
+- time range
+- operator request
+
+Before this change, the Agent still had to invent its own `lookup_knowledge` query from the full prompt. That can work, but it may omit important terms such as the service name or alert name.
+
+The new prompt makes the retrieval seed explicit:
+
+```text
+Recommended lookup_knowledge query: ...
+```
+
+## Design Choice
+
+This is prompt-level query augmentation, not hidden retrieval.
+
+I intentionally did not call `lookup_knowledge` automatically before the Agent runs. The project values traceability: tool calls should appear as Agent actions, with their inputs and outputs recorded in `tool_invocation`.
+
+So the design is:
+
+```text
+AIOps payload
+  -> deterministic recommended retrieval query
+  -> Agent prompt
+  -> Agent may call lookup_knowledge explicitly
+  -> tool_invocation records the real retrieval action
+```
+
+## Interview Answer
+
+If asked how AIOps payload improves RAG retrieval:
+
+> I do not replace the user query with a broad domain. I extract the high-signal alert terms from the payload, such as alertName, service, severity, symptom, and time range, and put them into a compact recommended lookup query. The Agent still calls `lookup_knowledge` explicitly, so the trace remains auditable, but the retrieval query is less dependent on model improvisation.
+
+If asked why not auto-call retrieval:
+
+> Auto-calling retrieval would create hidden evidence before the Agent actually decides to use a tool. For this project, explicit tool invocation is more important because the interview story is about observable Agent execution. Prompt-level augmentation gives the Agent a better query seed without changing the trace contract.