- Real bug found by code review while implementing this cycle: "Code -
Reusar Classificacao em Cache" always hardcoded is_fallback: false when
reconstructing a classification from memory, even when the cached
classification was itself AI_FALLBACK. On a cache hit for a previously
fallback-classified ticket, this would have let it skip
applyFallbackPolicy's exclusion entirely (the policy checks
aiClassification.is_fallback first). Fixed to derive is_fallback from
the cached ai_classification value.
- select-top10.node.js now carries `resolucao` (ia | cache | fallback_cap)
through to the scored item, previously dropped -- needed to tell cache
hits apart from real AI calls in observability.
- Code - Comparar Legacy x V2 now computes the full observability summary
(candidate_count, cache_hits/misses, ai_requested/success/errors,
ai_fallback_count and by-reason breakdown, classification_counts,
insufficient_context_count, actionable_real_count,
critical_override_count, top10_count, blocked_count,
coordination_count) and the data-quality review (buildDataQualityReview
over INSUFFICIENT_CONTEXT tickets), both stored in the shadow_runs
`summary` JSON column -- no new Data Table needed.
- Removed the debug webhook entirely (Debug - Ver Painel, Data Table -
Buscar Shadow Runs Recentes debug branch, Code - Montar Painel Debug,
Debug - Responder) and the now-unused debug-panel.node.js driver.
Workflow went from 37 to 33 nodes. Debugging goes through n8n execution
data, the Data Tables, and the shadow diff instead, as requested.
- Fixed glpi_base_url: was still V1's own placeholder
(https://glpi.suaempresa.com.br, never a real value, not report
anonymization) -- confirmed the real domain (https://glpi.gtigrupo.com.br)
from the live, active GLPI x WhatsApp workflow's own GLPI API config in
the same n8n instance. SQL still only ever receives it through
{GLPI_BASE_URL} substitution from the Config node, never hardcoded.
Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>
Fallback (ai_disabled, ai_error, invalid_json, ai_max_candidates_cap --
any candidate the AI never actually analyzed) now produces classification
'AI_FALLBACK', an explicit technical state that is never part of the
11-class enum sent to OpenAI (the AI itself can never legitimately return
it). Previously it silently claimed ACTIONABLE_NOW with confidence 0,
which made unanalyzed tickets indistinguishable from genuinely confirmed
ones at the classification-label level.
Also normalizes blocker_type/waiting_for when the model returns the
literal string "null"/"none"/"n/a"/"" instead of JSON null -- done
once, in the validation layer (logic/classification.js), not scattered
around the workflow.
Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>
Ran the deployed workflow for real (temporary webhook trigger, briefly
activated, immediately deactivated and removed after each attempt) against
the real GLPI MySQL database and a real OpenAI credential. Found and fixed:
1. $env access is blocked on this n8n instance (N8N_BLOCK_ENV_ACCESS_IN_NODE).
parseConfig($env) threw "access to env vars denied" on the very first
Code node. Config now comes from the "Config - Coordenador GLPI v2" Set
node instead (same pattern the V1 workflow already used) -- expanded
that node with all 13 tunable parameters, and pointed the 3 affected
node drivers (build-sql-candidatos, decide-needs-ai, select-top10) at
it instead of $env.
2. Data Table "upsert" operation requires both `columns` (values) and a
separate `filters` (match condition) -- I had only set columns with
matchingColumns, which isn't enough; n8n refused to activate the
workflow ("Missing or invalid required parameters: filters"). Added
filters.conditions matching on ticket_id to both memory-writing Data
Table nodes.
3. Real architecture bug: "Data Table - Buscar Memoria" sat inline in the
41-candidate chain (Mesclar Contexto -> Buscar Memoria -> Decidir
Necessidade de IA). A bulk "get all rows" Data Table node run with 41
input items produced 41 identical fetches and its OWN output (the empty
memory table on a first run) replaced the 41 candidates entirely --
"Decidir Necessidade de IA" received 0 items and the whole main branch
silently skipped. Moved the memory fetch to a parallel branch off
Config (fires once) and added a real Merge node ("Merge - Aguardar
Memoria") to guarantee both the candidates and the memory rows have
executed before "Decidir Necessidade de IA" runs -- same synchronization
pattern already used for context and for the shadow comparison.
Also fixed a cosmetic bug found in the real message output: buildTopMessage
produced a quadruple newline between the header and the first ticket block
([header, '', ...blocks].join('\n\n') double-counts the blank line already
inserted by the '\n\n' join). Added a regression test.
Verified with a real execution (id 13599, 24/08/2026 19:25 UTC) against the
live GLPI database: 41 real candidates, 30 sent to OpenAI (capped), 11
fallback-capped, memory table populated with 41 real rows including
genuine WAITING_VENDOR/WAITING_REQUESTER classifications with real
blocker_type/waiting_for fields extracted from real ticket task content,
and a real shadow_runs row comparing Legacy x V2 (4 tickets removed by V2:
2 for INSUFFICIENT_CONTEXT, 1 WAITING_REQUESTER, 1 WAITING_VENDOR; 4 added).
Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>
- 14 new Code node drivers implementing the pipeline stages: normalize
candidates, build context SQL, merge context + hash, decide AI need,
prepare AI prompt, validate AI response, AI-disabled fallback, reuse
cached classification, compute score, select top 10, prepare memory/
envio records, build message/digest, compare legacy x V2, debug panel.
- Consolidated the two context queries (followups/tasks) into a single
UNION ALL query tagged by tipo, and added a "Merge - Unir Shadow"
synchronization node before comparing legacy x V2: n8n does not
guarantee relative execution order between sibling branches that only
share an early common ancestor, so cross-branch data was only safe to
read by name once a real Merge node forced both to complete first.
- Reordered score calculation before the memory upsert (score is needed
in the persisted row) and turned both Data Table writes into
side-effect branches that fan out from the main path instead of
gating it, so the Data Table node's own output shape never needs to
carry the full pipeline state forward.
- Fixed a duplicate-identifier bug in the build script: score.js's
require('./cooldown') survived inlining and collided with
cooldown.js's own computeCooldown function in the same Code node
scope. build-workflow.js now strips inter-module requires when
inlining, and a new test syntax-checks every generated jsCode string
plus asserts no leftover require() calls.
Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>
GustavoandClaude Sonnet 5