369 lines
17 KiB
Markdown
369 lines
17 KiB
Markdown
---
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phase: 02-agent-features
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plan: 06
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type: execute
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wave: 1
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depends_on: []
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files_modified:
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- packages/orchestrator/orchestrator/tasks.py
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- packages/orchestrator/orchestrator/agents/builder.py
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- tests/unit/test_pipeline_wiring.py
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autonomous: true
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gap_closure: true
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requirements: [AGNT-05, AGNT-06, CHAN-03, CHAN-04]
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must_haves:
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truths:
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- "When a configured escalation rule triggers, the conversation is handed off to a human"
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- "A user can send a WhatsApp message and receive a reply (outbound routing works)"
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- "WhatsApp messages get business-function scoping in the LLM system prompt (tier 2)"
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artifacts:
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- path: "packages/orchestrator/orchestrator/tasks.py"
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provides: "Escalation wiring and channel-aware outbound routing"
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contains: "check_escalation_rules"
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- path: "packages/orchestrator/orchestrator/agents/builder.py"
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provides: "Tier-2 WhatsApp system prompt scoping"
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contains: "allowed_functions"
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- path: "tests/unit/test_pipeline_wiring.py"
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provides: "Tests verifying escalation and outbound routing in _process_message"
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key_links:
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- from: "packages/orchestrator/orchestrator/tasks.py"
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to: "packages/orchestrator/orchestrator/escalation/handler.py"
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via: "import and call check_escalation_rules + escalate_to_human in _process_message"
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pattern: "check_escalation_rules|escalate_to_human"
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- from: "packages/orchestrator/orchestrator/tasks.py"
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to: "_send_response"
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via: "Replace direct _update_slack_placeholder calls with _send_response"
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pattern: "_send_response\\("
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- from: "packages/orchestrator/orchestrator/agents/builder.py"
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to: "Agent.tool_assignments"
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via: "Append allowed_functions constraint to system prompt when channel is whatsapp"
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pattern: "You only handle"
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---
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<objective>
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Re-wire escalation handler and WhatsApp outbound routing into the orchestrator pipeline, and add tier-2 business-function scoping to the system prompt builder.
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Purpose: Plans 02-02 and 02-05 rewrote tasks.py and dropped integrations from earlier plans. The escalation handler is orphaned (never called) and WhatsApp replies are silently lost (all responses go to Slack's chat.update). Tier-2 system prompt scoping for WhatsApp was never implemented.
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Output: tasks.py calls escalation pre/post-checks and uses _send_response for all outbound; builder.py appends business-function constraint for WhatsApp channel; tests verify both wirings.
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</objective>
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<execution_context>
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@/home/adelorenzo/.claude/get-shit-done/workflows/execute-plan.md
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@/home/adelorenzo/.claude/get-shit-done/templates/summary.md
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</execution_context>
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<context>
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@.planning/PROJECT.md
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@.planning/ROADMAP.md
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@.planning/STATE.md
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@.planning/phases/02-agent-features/02-VERIFICATION.md
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<interfaces>
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<!-- Key types and contracts the executor needs. Extracted from codebase. -->
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From packages/orchestrator/orchestrator/escalation/handler.py:
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```python
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def check_escalation_rules(
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agent: Any,
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message_text: str,
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conversation_metadata: dict[str, Any],
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natural_lang_enabled: bool = False,
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) -> dict[str, Any] | None:
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"""Returns first matching rule dict or None."""
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async def escalate_to_human(
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tenant_id: str,
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agent: Any,
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thread_id: str,
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trigger_reason: str,
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recent_messages: list[dict[str, str]],
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assignee_slack_user_id: str,
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bot_token: str,
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redis: Any,
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audit_logger: Any,
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user_id: str = "",
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agent_id: str = "",
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) -> str:
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"""Returns user-facing escalation confirmation message."""
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```
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From packages/orchestrator/orchestrator/tasks.py (current state):
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```python
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async def _process_message(
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msg: KonstructMessage,
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placeholder_ts: str = "",
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channel_id: str = "",
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) -> dict:
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"""Lines 194-489. Three _update_slack_placeholder calls at lines 294, 366, 458."""
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async def _send_response(channel: str, text: str, extras: dict) -> None:
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"""Line 528. Defined but never called. Routes to Slack or WhatsApp."""
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def handle_message(self, message_data: dict) -> dict:
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"""Line 147. Pops placeholder_ts and channel_id before model_validate.
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WhatsApp gateway injects phone_number_id and bot_token into task_payload
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but handle_message does NOT pop them — they are lost during model_validate."""
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```
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From packages/shared/shared/redis_keys.py:
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```python
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def escalation_status_key(tenant_id: str, thread_id: str) -> str:
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```
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From packages/shared/shared/models/tenant.py:
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```python
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class Agent(Base):
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escalation_rules: Mapped[list[Any]] # JSON list of rule dicts
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escalation_assignee: Mapped[str | None] # Slack user ID
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natural_language_escalation: Mapped[bool]
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tool_assignments: Mapped[list[Any]] # JSON list — used as allowed_functions proxy
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```
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From packages/gateway/gateway/channels/whatsapp.py:
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```python
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# Line 604: task_payload = msg.model_dump() | {"phone_number_id": phone_number_id, "bot_token": access_token or ""}
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# WhatsApp gateway injects phone_number_id and bot_token as extra keys in the Celery payload
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```
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</interfaces>
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</context>
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<tasks>
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<task type="auto" tdd="true">
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<name>Task 1: Re-wire escalation and outbound routing in tasks.py</name>
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<files>
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packages/orchestrator/orchestrator/tasks.py
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tests/unit/test_pipeline_wiring.py
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</files>
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<behavior>
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- Test: _process_message calls check_escalation_rules after LLM response and before memory persistence
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- Test: When check_escalation_rules returns a matching rule AND agent.escalation_assignee is set, escalate_to_human is called and its return value replaces the LLM response
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- Test: When escalation status is "escalated" in Redis (pre-check), _process_message returns assistant-mode reply without calling run_agent
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- Test: _process_message uses _send_response (not _update_slack_placeholder directly) for all three response delivery points
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- Test: For WhatsApp messages, _send_response receives extras with phone_number_id, bot_token, wa_id
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- Test: handle_message pops phone_number_id, bot_token (WhatsApp extras) and wa_id before model_validate and passes them through to _process_message
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</behavior>
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<action>
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**In handle_message (line ~179):**
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Add extraction of WhatsApp extras alongside the existing Slack extras:
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```python
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phone_number_id: str = message_data.pop("phone_number_id", "") or ""
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bot_token: str = message_data.pop("bot_token", "") or ""
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```
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Note: `channel_id` is already popped for Slack. `bot_token` here is the WhatsApp access_token injected by the gateway.
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Pass these to _process_message. Change _process_message signature to accept an `extras: dict` parameter instead of individual `placeholder_ts` and `channel_id` params. The extras dict holds all channel-specific metadata:
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- For Slack: `{"bot_token": slack_bot_token, "channel_id": channel_id, "placeholder_ts": placeholder_ts}`
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- For WhatsApp: `{"phone_number_id": phone_number_id, "bot_token": bot_token, "wa_id": wa_id}`
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In handle_message, build the extras dict from popped values:
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```python
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extras = {
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"placeholder_ts": placeholder_ts,
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"channel_id": channel_id,
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"phone_number_id": phone_number_id,
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"bot_token": bot_token,
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}
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```
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Extract `wa_id` from `msg.sender.user_id` after model_validate (since the WhatsApp normalizer sets sender.user_id to the wa_id) and add to extras.
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**In _process_message:**
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1. Change signature: `async def _process_message(msg, extras: dict | None = None) -> dict`
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2. Extract channel-specific values from extras at the top.
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3. Replace all three `_update_slack_placeholder(...)` calls (lines 294, 366, 458) with `_send_response(msg.channel, text, extras_with_bot_token)` where extras_with_bot_token merges the Slack bot_token loaded from DB with the incoming extras.
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- For the Slack path: the DB-loaded `slack_bot_token` should be added to extras if `msg.channel == "slack"`.
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- For WhatsApp: extras already contain `phone_number_id` and `bot_token` from handle_message; add `wa_id` from extras or `msg.sender.user_id`.
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4. **Escalation pre-check** (add BEFORE the pending tool confirmation block, after agent is loaded):
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```python
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# Escalation pre-check: if conversation is already escalated, respond in assistant mode
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from shared.redis_keys import escalation_status_key
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esc_key = escalation_status_key(msg.tenant_id, msg.thread_id or user_id)
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esc_status = await redis_client.get(esc_key)
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if esc_status == b"escalated":
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assistant_reply = f"I've already connected you with a team member. They'll continue assisting you."
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await _send_response(msg.channel, assistant_reply, response_extras)
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return {"message_id": msg.id, "response": assistant_reply, "tenant_id": msg.tenant_id}
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```
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Use a single Redis client created before this block (reuse the one already created for pending_confirm_key). Close it in the finally block.
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5. **Escalation post-check** (add AFTER the run_agent call and BEFORE the is_confirmation_request check):
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```python
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from orchestrator.escalation.handler import check_escalation_rules, escalate_to_human
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# Build conversation metadata from sliding window for rule evaluation
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conversation_metadata = _build_conversation_metadata(recent_messages, user_text)
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triggered_rule = check_escalation_rules(
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agent=agent,
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message_text=user_text,
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conversation_metadata=conversation_metadata,
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natural_lang_enabled=getattr(agent, "natural_language_escalation", False),
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)
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if triggered_rule and getattr(agent, "escalation_assignee", None):
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escalation_redis = aioredis.from_url(settings.redis_url)
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try:
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response_text = await escalate_to_human(
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tenant_id=msg.tenant_id,
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agent=agent,
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thread_id=msg.thread_id or user_id,
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trigger_reason=triggered_rule.get("condition", "rule triggered"),
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recent_messages=recent_messages,
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assignee_slack_user_id=agent.escalation_assignee,
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bot_token=slack_bot_token,
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redis=escalation_redis,
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audit_logger=audit_logger,
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user_id=user_id,
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agent_id=agent_id_str,
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)
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finally:
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await escalation_redis.aclose()
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```
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6. **Add _build_conversation_metadata helper** (new function):
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```python
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def _build_conversation_metadata(recent_messages: list[dict], current_text: str) -> dict[str, Any]:
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"""Build conversation metadata dict for escalation rule evaluation.
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Scans recent messages for billing keywords and counts attempts.
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"""
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billing_keywords = {"billing", "invoice", "charge", "refund", "payment", "subscription"}
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all_texts = [m.get("content", "") for m in recent_messages] + [current_text]
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billing_count = sum(1 for t in all_texts if any(kw in t.lower() for kw in billing_keywords))
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return {
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"billing_dispute": billing_count > 0,
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"attempts": billing_count,
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}
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```
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This matches the v1 keyword-based metadata detection described in STATE.md decisions.
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**Important constraints:**
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- Celery tasks MUST remain sync def with asyncio.run() — never async def
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- Import escalation functions inside _process_message (local import, matching existing pattern)
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- Use `aioredis.from_url(settings.redis_url)` for new Redis clients (matching existing pattern in tasks.py)
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- The Slack DB bot_token loading (lines 269-281) must be preserved — it's needed for escalation DM delivery even on WhatsApp messages
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</action>
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<verify>
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<automated>cd /home/adelorenzo/repos/konstruct && python -m pytest tests/unit/test_pipeline_wiring.py -x -v</automated>
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</verify>
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<done>
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- handle_message pops phone_number_id, bot_token from message_data before model_validate
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- _process_message accepts extras dict and uses _send_response for ALL outbound delivery
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- Escalation pre-check: already-escalated conversations get assistant-mode reply without LLM call
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- Escalation post-check: check_escalation_rules called after LLM response; escalate_to_human called when rule matches and assignee is configured
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- _build_conversation_metadata extracts billing keywords from sliding window
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- All existing functionality preserved (memory pipeline, tool confirmation, audit logging)
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</done>
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</task>
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<task type="auto" tdd="true">
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<name>Task 2: Add tier-2 WhatsApp business-function scoping to system prompt builder</name>
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<files>
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packages/orchestrator/orchestrator/agents/builder.py
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tests/unit/test_pipeline_wiring.py
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</files>
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<behavior>
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- Test: build_system_prompt(agent, channel="whatsapp") appends "You only handle: {topics}" when agent.tool_assignments is non-empty
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- Test: build_system_prompt(agent, channel="slack") does NOT append business-function scoping
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- Test: build_system_prompt(agent, channel="whatsapp") with empty tool_assignments does NOT append scoping
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- Test: build_messages_with_memory passes channel through to build_system_prompt
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</behavior>
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<action>
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**In builder.py build_system_prompt:**
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Add an optional `channel: str = ""` parameter:
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```python
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def build_system_prompt(agent: Agent, channel: str = "") -> str:
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```
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After the AI transparency clause (step 4), add step 5 — WhatsApp business-function scoping:
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```python
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# 5. WhatsApp tier-2 scoping — constrain LLM to declared business functions
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if channel == "whatsapp":
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functions: list[str] = getattr(agent, "tool_assignments", []) or []
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if functions:
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topics = ", ".join(functions)
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parts.append(
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f"You are responding on WhatsApp. You only handle: {topics}. "
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f"If the user asks about something outside these topics, "
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f"politely redirect them to the allowed topics."
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)
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```
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**In builder.py build_messages_with_memory:**
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Add optional `channel: str = ""` parameter and pass through:
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```python
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def build_messages_with_memory(agent, current_message, recent_messages, relevant_context, channel: str = "") -> list[dict]:
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system_prompt = build_system_prompt(agent, channel=channel)
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...
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```
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**In builder.py build_messages_with_media:**
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Same change — add `channel: str = ""` parameter and pass to build_messages_with_memory.
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**In tasks.py _process_message:**
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Pass `msg.channel` to `build_messages_with_memory`:
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```python
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enriched_messages = build_messages_with_memory(
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agent=agent,
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current_message=user_text,
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recent_messages=recent_messages,
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relevant_context=relevant_context,
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channel=msg.channel,
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)
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```
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And similarly for the build_messages_with_media call if present.
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Add tests for tier-2 scoping to the same test_pipeline_wiring.py file created in Task 1.
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</action>
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<verify>
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<automated>cd /home/adelorenzo/repos/konstruct && python -m pytest tests/unit/test_pipeline_wiring.py -x -v</automated>
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</verify>
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<done>
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- build_system_prompt appends business-function scoping when channel == "whatsapp" and tool_assignments is non-empty
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- build_system_prompt does NOT append scoping for Slack or when tool_assignments is empty
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- build_messages_with_memory and build_messages_with_media pass channel through
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- _process_message passes msg.channel to builder functions
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</done>
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</task>
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</tasks>
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<verification>
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After both tasks complete, run the full verification:
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```bash
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# Unit tests for new wiring
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cd /home/adelorenzo/repos/konstruct && python -m pytest tests/unit/test_pipeline_wiring.py -x -v
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# Existing escalation tests still pass
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python -m pytest tests/unit/test_escalation.py -x -v
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# Existing WhatsApp tests still pass
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python -m pytest tests/unit/test_whatsapp_scoping.py tests/unit/test_whatsapp_normalize.py tests/unit/test_whatsapp_verify.py -x -v
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# Grep verification: escalation is wired
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grep -n "check_escalation_rules\|escalate_to_human" packages/orchestrator/orchestrator/tasks.py
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# Grep verification: _send_response is called (not _update_slack_placeholder directly in _process_message)
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grep -n "_send_response\|_update_slack_placeholder" packages/orchestrator/orchestrator/tasks.py
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# Grep verification: tier-2 scoping exists
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grep -n "You only handle" packages/orchestrator/orchestrator/agents/builder.py
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```
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</verification>
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<success_criteria>
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1. `check_escalation_rules` and `escalate_to_human` are imported and called in `_process_message`
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2. `_send_response` is called at all response delivery points in `_process_message` (no direct `_update_slack_placeholder` calls remain in that function)
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3. `build_system_prompt` appends business-function scoping for WhatsApp channel
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4. All existing unit tests pass
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5. New wiring tests pass
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</success_criteria>
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<output>
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After completion, create `.planning/phases/02-agent-features/02-06-SUMMARY.md`
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</output>
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