konstruct/.planning/phases/02-agent-features/02-04-SUMMARY.md

phase, plan, subsystem, tags, requires, provides, affects, tech-stack, key-files, key-decisions, patterns-established, requirements-completed, duration, completed

phase	plan	subsystem	tags	requires	provides	affects	tech-stack	key-files	key-decisions	patterns-established	requirements-completed	duration	completed
02-agent-features	04	orchestrator	escalation handoff slack-api redis celery pydantic postgres alembic	phase provides 02-01 get_recent_messages for transcript assembly; Redis short-term memory infrastructure	Escalation rule evaluator: 'keyword AND count > N' condition parser + natural language phrase detection Conversation transcript packager: Slack mrkdwn format with 3000-char truncation Human DM delivery: Slack conversations.open + chat.postMessage via httpx Escalation status tracking in Redis: escalation_status_key sets 'escalated' flag Post-escalation assistant mode: end-user messages to escalated threads get auto-reply, skipping LLM Agent model fields: escalation_assignee (Slack user ID), natural_language_escalation (bool) Alembic migration 003: adds escalation_assignee and natural_language_escalation to agents table No-op audit logger stub for escalation events (replaced when Plan 02 audit module ships)	02-02 (audit) — escalation events use no-op logger stub, ready for real AuditLogger swap tasks.py pipeline — escalation pre/post checks integrated around LLM call	added patterns Condition parsing: 'keyword AND count_field > N' format, regex-based, no eval() TDD pattern: RED (failing tests committed) then GREEN (implementation committed) Escalation pre-check before LLM: Redis flag gates whether LLM is called at all No-op logger stub: allows feature to work before audit plan is implemented	created modified packages/orchestrator/orchestrator/escalation/__init__.py packages/orchestrator/orchestrator/escalation/handler.py migrations/versions/003_escalation_fields.py tests/unit/test_escalation.py tests/integration/test_escalation.py packages/shared/shared/models/tenant.py packages/orchestrator/orchestrator/tasks.py	Keyword-based conversation metadata detection (v1): billing keywords + attempt counter from sliding window — simple and sufficient for initial rules Natural language escalation condition uses literal string 'natural_language_escalation' in escalation_rules config — matches plan spec Bot token loaded unconditionally in _process_message (not gated on placeholder_ts) — escalation DM needs it regardless of Slack placeholder presence No-op audit logger stub in tasks.py: escalation works independently of Plan 02 audit module; swap is a one-line change Condition parser uses regex (not eval): safe, deterministic, no code injection risk	Escalation check is two-phase: pre-LLM (assistant mode gate) and post-LLM (rule trigger) assistant mode: escalated thread + end user sender → skip LLM entirely, return static reply Escalation DM format follows employee metaphor: '{agent.name} needs human assistance'	AGNT-05	5min	2026-03-23

Phase 02 Plan 04: Human Escalation Handoff Summary

Rule-based and natural-language escalation with Slack DM delivery, Redis assistant-mode gate, and full transcript packaging

Performance

• Duration: 5 min
• Started: 2026-03-23T21:08:30Z
• Completed: 2026-03-23T21:13:12Z
• Tasks: 2
• Files modified: 7

Accomplishments

• Built complete escalation handler: condition evaluator, transcript builder, and Slack DM pipeline
• Wired escalation checks into the orchestrator message pipeline at both pre-LLM and post-LLM positions
• Added Agent model columns and Alembic migration for escalation configuration
• 28 tests passing (22 unit, 6 integration) covering all escalation behaviors

Task Commits

1. Task 1 (TDD RED): Failing tests for escalation handler - d489551 (test)
2. Task 1 (TDD GREEN): Escalation handler implementation - 4047b55 (feat)
3. Task 2: Wire escalation into orchestrator pipeline - a025cad (feat)

Files Created/Modified

• packages/orchestrator/orchestrator/escalation/__init__.py - Package init for escalation module
• packages/orchestrator/orchestrator/escalation/handler.py - check_escalation_rules, build_transcript, escalate_to_human
• packages/shared/shared/models/tenant.py - Added escalation_assignee and natural_language_escalation to Agent model
• migrations/versions/003_escalation_fields.py - Alembic migration for new Agent columns
• packages/orchestrator/orchestrator/tasks.py - Escalation pre/post checks in _process_message
• tests/unit/test_escalation.py - 22 unit tests (rule matching, NL phrases, transcript formatting)
• tests/integration/test_escalation.py - 6 integration tests (Slack API mocking, Redis, audit)

Decisions Made

• Keyword-based metadata detection (v1): Rather than LLM-structured output, detect billing keywords and count user turns as a proxy for attempts. Simple, zero-latency, sufficient for v1 escalation rules.
• Bot token loaded unconditionally: Changed from conditional load (only when placeholder_ts set) to always load from channel_connections. Escalation DM delivery requires it regardless.
• No-op audit logger stub: tasks.py includes a minimal no-op AuditLogger stub so escalation works before Plan 02 (audit) ships. Swap is one import change.
• Condition parser uses regex, not eval: Prevents code injection. Supports "X AND Y op Z" format with standard comparison operators.

Deviations from Plan

None - plan executed exactly as written. The no-op audit logger is specified in the plan's "CRITICAL constraints" section.

Issues Encountered

None.

Next Phase Readiness

• Escalation handler ready; can be tested end-to-end with a real Slack bot token in escalation_assignee
• When Plan 02 (audit) ships, replace _get_no_op_audit_logger() in tasks.py with the real AuditLogger import
• Conversation metadata detection is v1 keyword-based; can be upgraded to LLM-structured output in a future plan

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Phase: 02-agent-features Completed: 2026-03-23

Self-Check: PASSED

• All 7 files created/modified: FOUND
• All 3 task commits (d489551, 4047b55, a025cad): FOUND
• All 28 tests passing