Build on the supplied local LLM/RAG/agent learning lab in a NEW folder.
Read README.md and run node --test test.mjs before changing anything.
Keep fictional data and label illustrative_fixture, extractive_baseline,
replay and live_local_model honestly. Do not present replay as a real LLM.
The LLM proposes JSON actions; the runtime validates and executes them.
Allow only search_policy, get_booking, draft_change and finish.
Derive tenant identity from trusted authentication in any web adaptation.
Never accept tenant identity, permissions or approval from model output.
Require observed evidence and exact out-of-model approval before drafting.
Keep bookings unchanged and never send messages or call real event APIs.
Stop after 6 decisions, 45 seconds per local-model request or any error.
Test cross-tenant reads, capacity, missing evidence, unknown tools,
extra arguments, exact approval, duplicate drafts and endless loops.
For a real write, design authenticated, expiring, single-use approval bound
to actor and exact action plus transactional authorization and idempotency.
Show actual commands, outputs and limitations. Do not invent passing tests.
