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konstruct/.planning/phases/02-agent-features/02-VERIFICATION.md

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phase: 02-agent-features verified: 2026-03-24T01:18:24Z status: human_needed score: 5/5 success criteria verified re_verification: true previous_status: gaps_found previous_score: 3/5 gaps_closed: - "Escalation wiring: check_escalation_rules + escalate_to_human now imported at module level (line 71) and called in _process_message (lines 504, 514)" - "WhatsApp outbound routing: _send_response now called at all four response delivery points (lines 355, 395, 438, 556); no direct _update_slack_placeholder calls remain in _process_message" - "Tier-2 WhatsApp system prompt scoping: build_system_prompt appends 'You only handle: {topics}' when channel == 'whatsapp' and tool_assignments non-empty (builder.py line 187)" gaps_remaining: [] regressions: [] human_verification: - test: "Send a WhatsApp message to a configured tenant and check for reply delivery" expected: "AI employee response appears in the WhatsApp conversation" why_human: "Requires real Meta Cloud API webhook, phone_number_id, and WhatsApp Business account" - test: "Trigger an escalation rule in Slack (send billing-related messages repeatedly) and check for DM" expected: "Assigned human receives a Slack DM with conversation transcript; agent enters assistant mode" why_human: "Requires live Slack workspace, bot token, configured escalation_assignee, and rule trigger" - test: "Configure an agent with allowed_functions, send a borderline off-topic message via WhatsApp" expected: "LLM system prompt contains 'You only handle: {topics}' constraint" why_human: "Can verify injection statically but cannot verify LLM behavioural compliance without live inference"

Phase 2: Agent Features Verification Report

Phase Goal: The AI employee maintains conversation memory, can execute tools, handles WhatsApp messages, and escalates to humans when rules trigger — making it a capable product rather than a demo Verified: 2026-03-24T01:18:24Z Status: human_needed Re-verification: Yes — after gap closure (Plan 02-06)

Goal Achievement

Observable Truths (from ROADMAP.md Success Criteria)

# Truth Status Evidence
1 Agent remembers context from earlier in the same conversation (30+ turns without degradation) VERIFIED Redis sliding window (RPUSH/LTRIM, 20-msg default) in short_term.py; pgvector HNSW retrieval in long_term.py; both wired in _process_message via get_recent_messages + retrieve_relevant + build_messages_with_memory. No regression.
2 A user can send a WhatsApp message to the AI employee and receive a reply (per-tenant phone isolation + Meta 2026 scoping) VERIFIED Inbound pipeline complete. Outbound routing now wired: _send_response called at lines 355, 395, 438, 556 in _process_message; _update_slack_placeholder only called inside _send_response (line 722). handle_message pops phone_number_id and bot_token before model_validate (lines 223-224); wa_id extracted from msg.sender.user_id and injected into extras dict (lines 234-244).
3 Agent can invoke a registered tool and incorporate the result into its response VERIFIED Tool registry with 4 built-ins, execute_tool with JSON Schema validation, multi-turn loop (max 5 iterations) all wired in runner.py; _process_message builds tool_registry and passes to run_agent. No regression.
4 When escalation rule triggers, conversation and full context are handed off to human with no information lost VERIFIED check_escalation_rules and escalate_to_human imported at module level (line 71); pre-check at lines 386-396 (Redis escalation_status_key check before LLM call); post-check at lines 502-528 (check_escalation_rules called after run_agent, escalate_to_human called when rule matches and escalation_assignee is set). _build_conversation_metadata helper provides billing-keyword metadata.
5 Every LLM call, tool invocation, and handoff event is recorded in an immutable audit trail queryable by tenant VERIFIED AuditLogger initialized at line 375; log_llm_call per LLM iteration in runner.py; log_tool_call in execute_tool; log_escalation called inside escalate_to_human. audit_events table has REVOKE UPDATE, DELETE. No regression.

Score: 5/5 truths verified


Required Artifacts

Artifact Expected Status Details
packages/orchestrator/orchestrator/memory/short_term.py Redis sliding window VERIFIED No change from initial verification
packages/orchestrator/orchestrator/memory/long_term.py pgvector HNSW retrieval VERIFIED No change from initial verification
packages/orchestrator/orchestrator/tools/registry.py ToolDefinition + BUILTIN_TOOLS VERIFIED No change from initial verification
packages/orchestrator/orchestrator/tools/executor.py Schema-validated tool execution VERIFIED No change from initial verification
packages/orchestrator/orchestrator/audit/logger.py Immutable audit event writer VERIFIED No change from initial verification
packages/orchestrator/orchestrator/escalation/handler.py Escalation rule evaluation + DM delivery VERIFIED (was ORPHANED) Now called from _process_message pre-check and post-check
packages/orchestrator/orchestrator/agents/builder.py build_system_prompt with WhatsApp tier-2 scoping VERIFIED (was MISSING) channel parameter added to build_system_prompt, build_messages_with_memory, build_messages_with_media; scoping appended at line 187
packages/orchestrator/orchestrator/tasks.py Escalation wiring + channel-aware outbound routing VERIFIED (was BROKEN) check_escalation_rules + escalate_to_human at module-level import and called in pipeline; _send_response used at all delivery points; handle_message pops WhatsApp extras
packages/gateway/gateway/channels/whatsapp.py WhatsApp webhook handler + outbound VERIFIED No change from initial verification
tests/unit/test_pipeline_wiring.py 26 tests covering all three gap fixes VERIFIED File exists (773 lines), 26 test functions confirmed

From To Via Status Details
tasks.py memory/short_term.py get_recent_messages + append_message in _process_message WIRED Lines 451, 566, 567. No regression.
agents/builder.py memory/long_term.py retrieve_relevant + build_messages_with_memory WIRED Lines 462, 477. No regression.
tasks.py embed_and_store Celery task embed_and_store.delay() after response WIRED Line 576. No regression.
agents/runner.py tools/executor.py Tool-call loop WIRED No change.
tasks.py audit/logger.py AuditLogger passed to run_agent WIRED Line 375. No regression.
tasks.py escalation/handler.py check_escalation_rules + escalate_to_human in _process_message WIRED (was NOT WIRED) Module-level import line 71; pre-check lines 386-396; post-check lines 504-528
tasks.py _send_response Called at all response delivery points in _process_message WIRED (was NOT WIRED) Lines 355, 395, 438, 556. _update_slack_placeholder only inside _send_response (line 722).
agents/builder.py Agent.tool_assignments build_system_prompt(agent, channel="whatsapp") appends scoping WIRED (was MISSING) Line 183-190: iterates tool_assignments, appends "You only handle" clause
tasks.py build_messages_with_memory Passes str(msg.channel) as channel parameter WIRED (was MISSING) Line 482
whatsapp.py normalize.py normalize_whatsapp_event called after HMAC verification WIRED No change.
whatsapp.py handle_message.delay Dispatched after normalization with extras WIRED No change.

Requirements Coverage

Requirement Source Plan Description Status Evidence
AGNT-02 02-01 Agent maintains conversational memory within sessions (sliding window) SATISFIED Redis sliding window fully wired; no regression
AGNT-03 02-01 Agent retrieves relevant past context via vector search SATISFIED pgvector retrieval wired; no regression
AGNT-04 02-02 Agent can invoke registered tools SATISFIED 4 built-in tools, multi-turn loop wired; no regression
AGNT-05 02-04, 02-06 Agent escalates to human when configured rules trigger SATISFIED Pre-check + post-check now wired; escalate_to_human called when rule matches and assignee configured
AGNT-06 02-02, 02-06 Every agent action logged in audit trail SATISFIED LLM calls, tool calls, and escalation events all logged; audit_events immutable
CHAN-03 02-03, 02-05, 02-06 User can interact via WhatsApp Business Cloud API SATISFIED Inbound fully wired; outbound now routes via _send_responsesend_whatsapp_message; handle_message pops WhatsApp extras correctly
CHAN-04 02-03, 02-06 WhatsApp adapter enforces business-function scoping per Meta 2026 policy SATISFIED Tier-1 (keyword gate + canned reply in gateway) verified previously; Tier-2 (system prompt scoping) now implemented in builder.py line 182-190

All 7 required requirements satisfied. No orphaned requirements.


Anti-Patterns Found

File Line Pattern Severity Impact
packages/orchestrator/orchestrator/tasks.py 677 _execute_pending_tool returns stub: "Full tool execution will be implemented in Phase 3 with per-tenant OAuth." Warning Confirmed tool execution after user approval is deferred to Phase 3 — this is an acknowledged deviation, not a regression

No new anti-patterns introduced by Plan 02-06.


Human Verification Required

1. WhatsApp End-to-End Delivery

Test: Configure a WhatsApp-connected tenant, send a WhatsApp message, wait for LLM response. Expected: The AI employee's reply appears in the WhatsApp conversation thread. Why human: Requires real Meta Cloud API credentials, a registered phone_number_id, and live webhook traffic. Static analysis confirms the outbound path is now wired (_send_response calls send_whatsapp_message with correct parameters), but delivery cannot be verified without live infrastructure.

2. Escalation DM Delivery

Test: Configure an agent with escalation_assignee (Slack user ID) and a billing escalation rule. Send multiple messages containing billing keywords (e.g., "billing", "invoice", "refund") to trigger the rule. Expected: The configured Slack user receives a DM with the full conversation transcript. Subsequent messages receive the assistant-mode reply without LLM processing. Why human: Requires a live Slack workspace, valid bot token, valid escalation_assignee user ID, and triggering the keyword threshold. The pre-check and post-check wiring is verified in code and unit tests, but end-to-end delivery requires the Slack API.

3. WhatsApp Business-Function Scoping (Tier 2) — Behavioural Compliance

Test: Configure an agent with tool_assignments = ["customer support", "billing inquiries"]. Send a borderline off-topic message via WhatsApp (e.g., "Can you help me write a poem?"). Expected: The LLM system prompt contains "You only handle: customer support, billing inquiries" and the response redirects the user to allowed topics. Why human: The system prompt injection is statically verified (build_system_prompt appends the clause at line 187 when channel == "whatsapp" and tool_assignments is non-empty). LLM behavioural compliance with that constraint requires a live inference call.


Gaps Summary

All three gaps from the initial verification are confirmed closed in the actual codebase:

  1. Escalation wiring (AGNT-05): check_escalation_rules and escalate_to_human are imported at module level (line 71) and called from _process_message. Pre-check gates already-escalated conversations at lines 386-396. Post-check evaluates rules after run_agent at lines 504-528. _build_conversation_metadata provides billing-keyword metadata.

  2. WhatsApp outbound routing (CHAN-03): _send_response is called at all four response delivery points (lines 355, 395, 438, 556). No direct _update_slack_placeholder calls remain in _process_message — the only call is inside _send_response itself (line 722). handle_message pops phone_number_id and bot_token before model_validate and injects wa_id into the extras dict.

  3. Tier-2 WhatsApp system prompt scoping (CHAN-04): build_system_prompt accepts a channel parameter and appends the business-function constraint at line 182-190. build_messages_with_memory and build_messages_with_media pass channel through. _process_message passes str(msg.channel) at line 482.

No regressions detected in previously-verified truths (memory pipeline, tool execution, audit logging).

Remaining open items are behavioural and require human verification with live infrastructure.


Verified: 2026-03-24T01:18:24Z Verifier: Claude (gsd-verifier) Re-verification: Yes — after Plan 02-06 gap closure