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+Enforce marketplace seller compliance with GPT-4o, Gmail and Slack
+
+https://n8nworkflows.xyz/workflows/enforce-marketplace-seller-compliance-with-gpt-4o--gmail-and-slack-13913
+
+
+# Enforce marketplace seller compliance with GPT-4o, Gmail and Slack
+
+# 1. Workflow Overview
+
+This workflow automates seller compliance enforcement for a marketplace. It receives seller case data through a webhook, uses a central AI governance agent with several specialized AI sub-agents to assess policy violations and appeals, produces a structured enforcement decision, logs the result for auditability, notifies internal stakeholders, sends external seller communications, and updates seller compliance records.
+
+Typical use cases include:
+- Reviewing seller behavior for policy violations
+- Evaluating appeals against prior enforcement
+- Determining appropriate actions such as warnings or suspensions
+- Maintaining an auditable compliance history
+- Notifying compliance teams and sellers consistently
+
+## 1.1 Input Reception
+
+The workflow starts from an HTTP webhook that accepts seller compliance payloads via `POST`.
+
+## 1.2 Governance Orchestration and AI Decisioning
+
+A central Governance Agent receives the seller payload and coordinates multiple AI tools:
+- Policy Monitoring Agent
+- Appeals Review Agent
+- Enforcement Decision Agent
+
+It also uses:
+- A memory node for conversational context
+- A structured output parser to enforce a strict JSON schema
+- OpenAI chat models for all agent/tool reasoning
+
+## 1.3 Policy and Violation Analysis
+
+The Policy Monitoring Agent analyzes seller behavior and compliance signals. It has access to:
+- A calculator tool
+- A custom code tool that computes a severity score and classifies risk
+
+This block supports objective scoring and evidence-based policy assessment.
+
+## 1.4 Appeals and Enforcement Decision
+
+Two specialized agents support the orchestration:
+- Appeals Review Agent evaluates appeal fairness and new evidence
+- Enforcement Decision Agent recommends a proportional action
+
+These are exposed as tools to the Governance Agent.
+
+## 1.5 Routing and Audit Logging
+
+Once the Governance Agent emits structured output, a Switch node routes all supported enforcement actions to a common audit preparation step. The resulting record is written to a data table for traceability.
+
+## 1.6 Notifications and Seller Record Update
+
+After the audit entry is created:
+- Slack notifies the internal compliance team
+- A second Switch routes seller-facing email notifications
+- The workflow updates seller compliance records in a data table
+
+---
+
+# 2. Block-by-Block Analysis
+
+## 2.1 Input Reception
+
+### Overview
+This block receives seller compliance case data from an external system. It is the single entry point of the workflow and passes the inbound payload to the central governance logic.
+
+### Nodes Involved
+- Receive Seller Data
+
+### Node Details
+
+#### Receive Seller Data
+- **Type and role:** `n8n-nodes-base.webhook`; entry-point webhook for incoming seller compliance events.
+- **Configuration choices:**
+  - HTTP method: `POST`
+  - Path: `governance/seller-compliance`
+  - No additional options configured
+- **Key expressions or variables used:**
+  - Downstream nodes expect the request body under `{{$json.body}}`
+- **Input and output connections:**
+  - No input node; this is the workflow trigger
+  - Outputs to: `Governance Agent`
+- **Version-specific requirements:**
+  - Uses webhook node type version `2.1`
+- **Edge cases / failures:**
+  - Payload may not be in the format expected by downstream AI nodes
+  - Missing or malformed `body` may cause poor model performance or parser failures
+  - No authentication is configured in the node JSON; this is a security gap if exposed publicly
+- **Sub-workflow reference:** None
+
+---
+
+## 2.2 Governance Orchestration and Structured AI Output
+
+### Overview
+This block is the core reasoning engine. The Governance Agent consumes the seller payload, can call specialized sub-agents as tools, uses memory for context, and is forced to emit a validated structured enforcement object.
+
+### Nodes Involved
+- Governance Agent
+- Governance Model
+- Governance Memory
+- Structured Enforcement Output
+- Output Parser Model
+
+### Node Details
+
+#### Governance Agent
+- **Type and role:** `@n8n/n8n-nodes-langchain.agent`; orchestrating AI agent that coordinates all compliance decisioning.
+- **Configuration choices:**
+  - Prompt source: defined directly in the node
+  - Input text: `={{ $json.body }}`
+  - System message defines governance responsibilities:
+    - coordinate sub-agents
+    - ensure fairness and non-discrimination
+    - maintain auditability
+    - escalate high-stakes cases
+    - balance seller rights and marketplace integrity
+  - Output parser enabled
+- **Key expressions or variables used:**
+  - `{{$json.body}}` as main case input
+- **Input and output connections:**
+  - Main input from: `Receive Seller Data`
+  - AI language model input from: `Governance Model`
+  - AI memory input from: `Governance Memory`
+  - AI tools from:
+    - `Policy Monitoring Agent`
+    - `Appeals Review Agent`
+    - `Enforcement Decision Agent`
+  - AI output parser from: `Structured Enforcement Output`
+  - Main output to: `Route Enforcement Action`
+- **Version-specific requirements:**
+  - Uses type version `3.1`
+  - Requires n8n LangChain-compatible AI agent support
+- **Edge cases / failures:**
+  - If incoming body is unstructured, tool invocation may be inconsistent
+  - Model may omit required schema fields without parser correction
+  - High token usage possible if payload is large
+  - Any failure in connected AI tool/model/parser nodes will break orchestration
+- **Sub-workflow reference:** None
+
+#### Governance Model
+- **Type and role:** `@n8n/n8n-nodes-langchain.lmChatOpenAi`; primary LLM for the Governance Agent.
+- **Configuration choices:**
+  - Model: `gpt-4o`
+  - Temperature: `0.2`
+  - No built-in tools enabled
+- **Key expressions or variables used:** None
+- **Input and output connections:**
+  - Outputs via AI language model connection to: `Governance Agent`
+- **Version-specific requirements:**
+  - Uses type version `1.3`
+  - Requires valid OpenAI credentials
+- **Edge cases / failures:**
+  - OpenAI auth or quota failure
+  - Model availability changes
+  - Large prompts may hit token or cost constraints
+- **Sub-workflow reference:** None
+
+#### Governance Memory
+- **Type and role:** `@n8n/n8n-nodes-langchain.memoryBufferWindow`; conversational memory for the governance agent.
+- **Configuration choices:**
+  - Defaults used; no custom parameters set
+- **Key expressions or variables used:** None
+- **Input and output connections:**
+  - Outputs via AI memory connection to: `Governance Agent`
+- **Version-specific requirements:**
+  - Type version `1.3`
+- **Edge cases / failures:**
+  - Since no explicit memory settings are shown, actual retention behavior depends on defaults
+  - Memory may not provide durable long-term state across separate executions
+- **Sub-workflow reference:** None
+
+#### Structured Enforcement Output
+- **Type and role:** `@n8n/n8n-nodes-langchain.outputParserStructured`; validates and normalizes the Governance Agent response into a strict schema.
+- **Configuration choices:**
+  - Manual JSON schema
+  - Auto-fix enabled
+  - Required output fields:
+    - `seller_id`
+    - `violation_type`
+    - `severity`
+    - `enforcement_action`
+    - `reasoning`
+    - `requires_human_review`
+  - Optional fields include `evidence` and `timestamp`
+  - Allowed `severity` values:
+    - `low`
+    - `medium`
+    - `high`
+    - `critical`
+  - Allowed `enforcement_action` values:
+    - `warning`
+    - `review`
+    - `suspension`
+    - `termination`
+    - `appeal_approved`
+    - `appeal_denied`
+- **Key expressions or variables used:** Manual schema only
+- **Input and output connections:**
+  - AI language model input from: `Output Parser Model`
+  - Outputs via AI output parser connection to: `Governance Agent`
+- **Version-specific requirements:**
+  - Type version `1.3`
+- **Edge cases / failures:**
+  - Parser may still fail if agent output is too malformed
+  - `autoFix` can repair minor formatting issues but not missing reasoning context
+  - If agent returns unsupported enum values, routing later will fail or return no branch
+- **Sub-workflow reference:** None
+
+#### Output Parser Model
+- **Type and role:** `@n8n/n8n-nodes-langchain.lmChatOpenAi`; lightweight LLM used by the structured output parser.
+- **Configuration choices:**
+  - Model: `gpt-4o-mini`
+  - Temperature: `0`
+- **Key expressions or variables used:** None
+- **Input and output connections:**
+  - Outputs via AI language model connection to: `Structured Enforcement Output`
+- **Version-specific requirements:**
+  - Type version `1.3`
+  - Requires OpenAI credentials
+- **Edge cases / failures:**
+  - Auth/quota failures
+  - Parser model may still fail on extremely inconsistent agent output
+- **Sub-workflow reference:** None
+
+---
+
+## 2.3 Policy and Violation Analysis
+
+### Overview
+This block provides policy assessment support to the Governance Agent. The Policy Monitoring Agent can analyze seller metrics and invoke quantitative tools to compute severity and validate compliance signals.
+
+### Nodes Involved
+- Policy Monitoring Agent
+- Policy Monitoring Model
+- Compliance Calculator
+- Violation Severity Scorer
+
+### Node Details
+
+#### Policy Monitoring Agent
+- **Type and role:** `@n8n/n8n-nodes-langchain.agentTool`; specialized AI tool agent focused on policy validation and compliance analysis.
+- **Configuration choices:**
+  - Input text uses AI-supplied variable:
+    - `={{ $fromAI('seller_data', 'Seller behavior and compliance data to validate') }}`
+  - Tool description explains that it validates behavior, detects violations, and assesses severity
+  - System message emphasizes objective, evidence-based policy assessment
+- **Key expressions or variables used:**
+  - `$fromAI('seller_data', ...)`
+- **Input and output connections:**
+  - AI language model input from: `Policy Monitoring Model`
+  - AI tool inputs from:
+    - `Compliance Calculator`
+    - `Violation Severity Scorer`
+  - Outputs via AI tool connection to: `Governance Agent`
+- **Version-specific requirements:**
+  - Type version `3`
+- **Edge cases / failures:**
+  - If the Governance Agent does not pass sufficient seller data, analysis quality drops
+  - Tool arguments extracted by AI may be incomplete or type-inconsistent
+- **Sub-workflow reference:** None
+
+#### Policy Monitoring Model
+- **Type and role:** `@n8n/n8n-nodes-langchain.lmChatOpenAi`; model used by the Policy Monitoring Agent.
+- **Configuration choices:**
+  - Model: `gpt-4o`
+  - Temperature: `0.2`
+- **Key expressions or variables used:** None
+- **Input and output connections:**
+  - Outputs via AI language model connection to: `Policy Monitoring Agent`
+- **Version-specific requirements:**
+  - Type version `1.3`
+  - Requires OpenAI credentials
+- **Edge cases / failures:**
+  - Standard OpenAI auth, latency, quota, or model availability issues
+- **Sub-workflow reference:** None
+
+#### Compliance Calculator
+- **Type and role:** `@n8n/n8n-nodes-langchain.toolCalculator`; arithmetic helper available to the Policy Monitoring Agent.
+- **Configuration choices:**
+  - No custom configuration
+- **Key expressions or variables used:** None directly
+- **Input and output connections:**
+  - Outputs via AI tool connection to: `Policy Monitoring Agent`
+- **Version-specific requirements:**
+  - Type version `1`
+- **Edge cases / failures:**
+  - Limited to calculation-oriented use cases
+  - Wrong numeric extraction by AI can still produce misleading results
+- **Sub-workflow reference:** None
+
+#### Violation Severity Scorer
+- **Type and role:** `@n8n/n8n-nodes-langchain.toolCode`; custom JavaScript scoring tool for compliance severity.
+- **Configuration choices:**
+  - Reads AI-provided numeric inputs:
+    - `return_rate`
+    - `complaint_count`
+    - `violation_history`
+    - `avg_response_time`
+  - Defaults:
+    - `return_rate = 0`
+    - `complaint_count = 0`
+    - `violation_history = 0`
+    - `avg_response_time = 24`
+  - Computes a score from 0–100 using weighted factors:
+    - returns up to 40 points
+    - complaints up to 30 points
+    - violation history up to 20 points
+    - response delay up to 10 points
+  - Classifies severity:
+    - `<25`: low
+    - `<50`: medium
+    - `<75`: high
+    - otherwise critical
+- **Key expressions or variables used:**
+  - `$fromAI('return_rate', ..., 'number', 0)`
+  - `$fromAI('complaint_count', ..., 'number', 0)`
+  - `$fromAI('violation_history', ..., 'number', 0)`
+  - `$fromAI('avg_response_time', ..., 'number', 24)`
+- **Input and output connections:**
+  - Outputs via AI tool connection to: `Policy Monitoring Agent`
+- **Version-specific requirements:**
+  - Type version `1.3`
+  - Requires support for LangChain code tools
+- **Edge cases / failures:**
+  - AI may pass bad numeric values or unexpected units
+  - Negative values are not explicitly guarded against
+  - The scoring model is heuristic and may not match policy/legal expectations without calibration
+- **Sub-workflow reference:** None
+
+---
+
+## 2.4 Appeals and Enforcement Decision
+
+### Overview
+This block separates appeal evaluation from enforcement recommendation. Both agents are callable by the Governance Agent, helping it preserve fairness and proportionality in final decisions.
+
+### Nodes Involved
+- Appeals Review Agent
+- Appeals Review Model
+- Enforcement Decision Agent
+- Enforcement Decision Model
+
+### Node Details
+
+#### Appeals Review Agent
+- **Type and role:** `@n8n/n8n-nodes-langchain.agentTool`; specialized AI tool for appeal evaluation.
+- **Configuration choices:**
+  - Input text:
+    - `={{ $fromAI('appeal_data', 'Appeal submission with seller arguments and evidence') }}`
+  - System instructions emphasize fairness, fresh review, procedural checks, and benefit of doubt when evidence is ambiguous
+  - Tool description defines appeal approval/denial recommendation behavior
+- **Key expressions or variables used:**
+  - `$fromAI('appeal_data', ...)`
+- **Input and output connections:**
+  - AI language model input from: `Appeals Review Model`
+  - Outputs via AI tool connection to: `Governance Agent`
+- **Version-specific requirements:**
+  - Type version `3`
+- **Edge cases / failures:**
+  - If no appeal data exists, the agent may still be called with insufficient context
+  - Ambiguous evidence may lead to inconsistent recommendations
+- **Sub-workflow reference:** None
+
+#### Appeals Review Model
+- **Type and role:** `@n8n/n8n-nodes-langchain.lmChatOpenAi`; model backing the Appeals Review Agent.
+- **Configuration choices:**
+  - Model: `gpt-4o`
+  - Temperature: `0.3`
+- **Key expressions or variables used:** None
+- **Input and output connections:**
+  - Outputs via AI language model connection to: `Appeals Review Agent`
+- **Version-specific requirements:**
+  - Type version `1.3`
+  - Requires OpenAI credentials
+- **Edge cases / failures:**
+  - Standard OpenAI credential, quota, timeout, or latency issues
+- **Sub-workflow reference:** None
+
+#### Enforcement Decision Agent
+- **Type and role:** `@n8n/n8n-nodes-langchain.agentTool`; specialized AI tool to choose the enforcement action.
+- **Configuration choices:**
+  - Input text:
+    - `={{ $fromAI('violation_assessment', 'Violation assessment and seller context for enforcement decision') }}`
+  - System message defines progressive discipline, mitigating factors, consistency, and human escalation requirements
+  - Tool description covers warning/review/suspension/termination decisions
+- **Key expressions or variables used:**
+  - `$fromAI('violation_assessment', ...)`
+- **Input and output connections:**
+  - AI language model input from: `Enforcement Decision Model`
+  - Outputs via AI tool connection to: `Governance Agent`
+- **Version-specific requirements:**
+  - Type version `3`
+- **Edge cases / failures:**
+  - If seller history is absent, progressive discipline may be weakly grounded
+  - Human-review escalation is advisory only; there is no enforced pause in this workflow
+- **Sub-workflow reference:** None
+
+#### Enforcement Decision Model
+- **Type and role:** `@n8n/n8n-nodes-langchain.lmChatOpenAi`; model used by the Enforcement Decision Agent.
+- **Configuration choices:**
+  - Model: `gpt-4o`
+  - Temperature: `0.2`
+- **Key expressions or variables used:** None
+- **Input and output connections:**
+  - Outputs via AI language model connection to: `Enforcement Decision Agent`
+- **Version-specific requirements:**
+  - Type version `1.3`
+  - Requires OpenAI credentials
+- **Edge cases / failures:**
+  - Standard OpenAI issues: auth, quota, token size, timeout
+- **Sub-workflow reference:** None
+
+---
+
+## 2.5 Enforcement Routing and Audit Logging
+
+### Overview
+This block normalizes all supported enforcement outcomes into one common post-decision path. It prepares a compact audit payload and writes it to a data table before notifications occur.
+
+### Nodes Involved
+- Route Enforcement Action
+- Prepare Audit Log
+- Enforcement Audit Trail
+
+### Node Details
+
+#### Route Enforcement Action
+- **Type and role:** `n8n-nodes-base.switch`; routes by `enforcement_action`.
+- **Configuration choices:**
+  - Six explicit string-equality branches:
+    - `warning`
+    - `review`
+    - `suspension`
+    - `termination`
+    - `appeal_approved`
+    - `appeal_denied`
+  - All branches currently converge to the same next node: `Prepare Audit Log`
+- **Key expressions or variables used:**
+  - `={{ $json.enforcement_action }}`
+- **Input and output connections:**
+  - Input from: `Governance Agent`
+  - Outputs to: `Prepare Audit Log` on all six branches
+- **Version-specific requirements:**
+  - Type version `3.4`
+- **Edge cases / failures:**
+  - Unsupported action values will not match any branch
+  - Because all outputs converge, the switch currently acts more like validation than differentiated routing
+  - `review` and `termination` have no dedicated seller notification branch later
+- **Sub-workflow reference:** None
+
+#### Prepare Audit Log
+- **Type and role:** `n8n-nodes-base.set`; formats a structured audit record.
+- **Configuration choices:**
+  - Assigns:
+    - `seller_id`
+    - `violation_type`
+    - `severity`
+    - `enforcement_action`
+    - `reasoning`
+    - `evidence` as JSON string
+    - `requires_human_review`
+    - `timestamp` using current execution time
+    - `workflow_execution_id` from execution metadata
+- **Key expressions or variables used:**
+  - `={{ $json.seller_id }}`
+  - `={{ $json.violation_type }}`
+  - `={{ $json.severity }}`
+  - `={{ $json.enforcement_action }}`
+  - `={{ $json.reasoning }}`
+  - `={{ JSON.stringify($json.evidence) }}`
+  - `={{ $json.requires_human_review }}`
+  - `={{ $now.toISO() }}`
+  - `={{ $execution.id }}`
+- **Input and output connections:**
+  - Input from: `Route Enforcement Action`
+  - Output to: `Enforcement Audit Trail`
+- **Version-specific requirements:**
+  - Type version `3.4`
+- **Edge cases / failures:**
+  - If `evidence` is undefined, `JSON.stringify(undefined)` may not behave as intended for storage
+  - Any missing required fields from prior parsing reduce audit quality
+- **Sub-workflow reference:** None
+
+#### Enforcement Audit Trail
+- **Type and role:** `n8n-nodes-base.dataTable`; persists audit records.
+- **Configuration choices:**
+  - Data table operation appears to be insert-like default behavior
+  - Column mapping mode: auto-map input data
+  - Data table ID is placeholder-based and must be configured
+- **Key expressions or variables used:** None directly
+- **Input and output connections:**
+  - Input from: `Prepare Audit Log`
+  - Outputs to:
+    - `Notify Compliance Team`
+    - `Route Notifications`
+- **Version-specific requirements:**
+  - Type version `1.1`
+  - Requires n8n Data Table feature availability
+- **Edge cases / failures:**
+  - Placeholder table ID must be replaced
+  - Schema mismatch between input fields and table columns can fail writes
+  - If this node fails, notifications and seller record updates will not proceed
+- **Sub-workflow reference:** None
+
+---
+
+## 2.6 Notifications and Seller Record Management
+
+### Overview
+This block handles internal Slack alerts, seller-facing Gmail notifications, and compliance-record persistence. Different enforcement actions produce different external communications, then all notification paths update a seller record store.
+
+### Nodes Involved
+- Notify Compliance Team
+- Route Notifications
+- Send Warning Email
+- Send Suspension Notice
+- Send Appeal Decision
+- Prepare Seller Record
+- Seller Compliance Records
+
+### Node Details
+
+#### Notify Compliance Team
+- **Type and role:** `n8n-nodes-base.slack`; internal alert to compliance staff.
+- **Configuration choices:**
+  - OAuth2 authentication
+  - Sends to a configured channel ID placeholder
+  - Message includes seller ID, action, violation type, severity, reasoning, human review flag, and execution ID
+- **Key expressions or variables used:**
+  - `{{ $json.seller_id }}`
+  - `{{ $json.enforcement_action }}`
+  - `{{ $json.violation_type }}`
+  - `{{ $json.severity }}`
+  - `{{ $json.reasoning }}`
+  - `{{ $json.requires_human_review ? 'Yes ⚠️' : 'No' }}`
+  - `{{ $json.workflow_execution_id }}`
+- **Input and output connections:**
+  - Input from: `Enforcement Audit Trail`
+  - No downstream output connected
+- **Version-specific requirements:**
+  - Type version `2.4`
+  - Requires Slack OAuth2 credentials and a valid channel selection
+- **Edge cases / failures:**
+  - Placeholder channel must be replaced
+  - Missing Slack scope or channel access causes posting failures
+  - Long reasoning text may reduce readability in Slack
+- **Sub-workflow reference:** None
+
+#### Route Notifications
+- **Type and role:** `n8n-nodes-base.switch`; routes seller-facing notifications by action type.
+- **Configuration choices:**
+  - Four branches:
+    - `warning`
+    - `suspension`
+    - `appeal_approved`
+    - `appeal_denied`
+  - Appeals approved and denied both route to the same Gmail node
+- **Key expressions or variables used:**
+  - `={{ $json.enforcement_action }}`
+- **Input and output connections:**
+  - Input from: `Enforcement Audit Trail`
+  - Outputs to:
+    - `Send Warning Email`
+    - `Send Suspension Notice`
+    - `Send Appeal Decision`
+    - `Send Appeal Decision`
+- **Version-specific requirements:**
+  - Type version `3.4`
+- **Edge cases / failures:**
+  - `review` and `termination` actions do not trigger seller-facing email here
+  - Unsupported action values result in no seller notification
+- **Sub-workflow reference:** None
+
+#### Send Warning Email
+- **Type and role:** `n8n-nodes-base.gmail`; sends policy warning to seller.
+- **Configuration choices:**
+  - Recipient is a placeholder email value
+  - Subject: `Policy Violation Warning - Action Required`
+  - Body contains violation type, severity, reasoning, evidence list, correction request, and appeal guidance
+- **Key expressions or variables used:**
+  - `{{ $json.violation_type }}`
+  - `{{ $json.severity }}`
+  - `{{ $json.reasoning }}`
+  - `{{ $json.evidence ? $json.evidence.join('\n') : 'See attached details' }}`
+- **Input and output connections:**
+  - Input from: `Route Notifications`
+  - Output to: `Prepare Seller Record`
+- **Version-specific requirements:**
+  - Type version `2.2`
+  - Requires Gmail OAuth2 credentials
+- **Edge cases / failures:**
+  - `sendTo` is currently a placeholder and must be dynamically sourced or replaced
+  - If `evidence` is a string from previous storage transformation rather than array, `.join()` will fail
+  - Gmail sending limits and OAuth expiry can interrupt delivery
+- **Sub-workflow reference:** None
+
+#### Send Suspension Notice
+- **Type and role:** `n8n-nodes-base.gmail`; sends seller suspension notice.
+- **Configuration choices:**
+  - Recipient is a placeholder email value
+  - Subject: `Account Suspension Notice - Immediate Action Required`
+  - Body explains immediate suspension, appeal process, and required evidence
+- **Key expressions or variables used:**
+  - `{{ $json.violation_type }}`
+  - `{{ $json.severity }}`
+  - `{{ $json.reasoning }}`
+  - `{{ $json.evidence ? $json.evidence.join('\n') : 'See attached details' }}`
+- **Input and output connections:**
+  - Input from: `Route Notifications`
+  - Output to: `Prepare Seller Record`
+- **Version-specific requirements:**
+  - Type version `2.2`
+  - Requires Gmail OAuth2 credentials
+- **Edge cases / failures:**
+  - Same email placeholder and evidence-type issues as warning email
+  - Delivery failures could leave internal records updated only partially depending on execution mode
+- **Sub-workflow reference:** None
+
+#### Send Appeal Decision
+- **Type and role:** `n8n-nodes-base.gmail`; sends either appeal approval or denial.
+- **Configuration choices:**
+  - Recipient: placeholder expression form
+  - Subject dynamically becomes `Approved` or `Denied`
+  - Body uses a conditional expression:
+    - approved: account restrictions lifted
+    - denied: original action remains
+- **Key expressions or variables used:**
+  - `{{ $json.enforcement_action === 'appeal_approved' ? ... : ... }}`
+- **Input and output connections:**
+  - Input from: `Route Notifications`
+  - Output to: `Prepare Seller Record`
+- **Version-specific requirements:**
+  - Type version `2.2`
+  - Requires Gmail OAuth2 credentials
+- **Edge cases / failures:**
+  - Placeholder recipient must be replaced
+  - If `enforcement_action` is unexpected, the "denied" branch becomes the default message
+- **Sub-workflow reference:** None
+
+#### Prepare Seller Record
+- **Type and role:** `n8n-nodes-base.set`; maps final case outcome into a seller-status record.
+- **Configuration choices:**
+  - Assigns:
+    - `seller_id`
+    - `current_status`
+    - `last_violation_type`
+    - `last_violation_severity`
+    - `last_action_date`
+    - `total_violations`
+    - `requires_monitoring`
+  - `total_violations` is computed as `($json.total_violations || 0) + 1`
+  - `requires_monitoring` is true for `high` or `critical`
+- **Key expressions or variables used:**
+  - `={{ $json.seller_id }}`
+  - `={{ $json.enforcement_action }}`
+  - `={{ $json.violation_type }}`
+  - `={{ $json.severity }}`
+  - `={{ $now.toISO() }}`
+  - `={{ ($json.total_violations || 0) + 1 }}`
+  - `={{ $json.severity === 'high' || $json.severity === 'critical' }}`
+- **Input and output connections:**
+  - Inputs from:
+    - `Send Warning Email`
+    - `Send Suspension Notice`
+    - `Send Appeal Decision`
+  - Output to: `Seller Compliance Records`
+- **Version-specific requirements:**
+  - Type version `3.4`
+- **Edge cases / failures:**
+  - `total_violations` does not actually read existing table state in this workflow; it increments only if upstream payload already contains the field
+  - `review` and `termination` actions never reach this node because they are not routed through seller notifications
+- **Sub-workflow reference:** None
+
+#### Seller Compliance Records
+- **Type and role:** `n8n-nodes-base.dataTable`; upserts seller compliance profile/status.
+- **Configuration choices:**
+  - Operation: `upsert`
+  - Filter condition:
+    - `seller_id = {{$json.seller_id}}`
+  - Column mapping: auto-map input data
+  - Data table ID is placeholder-based and must be set
+- **Key expressions or variables used:**
+  - Filter on `seller_id`
+- **Input and output connections:**
+  - Input from: `Prepare Seller Record`
+  - No downstream nodes
+- **Version-specific requirements:**
+  - Type version `1.1`
+  - Requires n8n Data Table support
+- **Edge cases / failures:**
+  - Placeholder table ID must be replaced
+  - Upsert behavior depends on the target table schema and unique key expectations
+  - Because only some action types reach this node, records may not be updated for `review` or `termination`
+- **Sub-workflow reference:** None
+
+---
+
+## 2.7 Documentation Sticky Notes
+
+### Overview
+These nodes are non-executable annotations embedded in the canvas. They document setup, architecture, and operating intent, and should be preserved for maintainability.
+
+### Nodes Involved
+- Sticky Note
+- Sticky Note1
+- Sticky Note2
+- Sticky Note3
+- Sticky Note4
+- Sticky Note5
+- Sticky Note6
+
+### Node Details
+
+#### Sticky Note
+- **Type and role:** `n8n-nodes-base.stickyNote`; documents prerequisites, use cases, customization, and benefits.
+- **Configuration choices:** Contains text only.
+- **Input and output connections:** None
+- **Version-specific requirements:** Type version `1`
+- **Edge cases / failures:** None
+- **Sub-workflow reference:** None
+
+#### Sticky Note1
+- **Type and role:** `n8n-nodes-base.stickyNote`; documents setup steps.
+- **Configuration choices:** Contains text only.
+- **Input and output connections:** None
+- **Version-specific requirements:** Type version `1`
+- **Edge cases / failures:** None
+- **Sub-workflow reference:** None
+
+#### Sticky Note2
+- **Type and role:** `n8n-nodes-base.stickyNote`; documents the end-to-end workflow behavior.
+- **Configuration choices:** Contains text only.
+- **Input and output connections:** None
+- **Version-specific requirements:** Type version `1`
+- **Edge cases / failures:** None
+- **Sub-workflow reference:** None
+
+#### Sticky Note3
+- **Type and role:** `n8n-nodes-base.stickyNote`; describes audit and routing section.
+- **Configuration choices:** Contains text only.
+- **Input and output connections:** None
+- **Version-specific requirements:** Type version `1`
+- **Edge cases / failures:** None
+- **Sub-workflow reference:** None
+
+#### Sticky Note4
+- **Type and role:** `n8n-nodes-base.stickyNote`; describes appeals and enforcement section.
+- **Configuration choices:** Contains text only.
+- **Input and output connections:** None
+- **Version-specific requirements:** Type version `1`
+- **Edge cases / failures:** None
+- **Sub-workflow reference:** None
+
+#### Sticky Note5
+- **Type and role:** `n8n-nodes-base.stickyNote`; describes policy and violation analysis.
+- **Configuration choices:** Contains text only.
+- **Input and output connections:** None
+- **Version-specific requirements:** Type version `1`
+- **Edge cases / failures:** None
+- **Sub-workflow reference:** None
+
+#### Sticky Note6
+- **Type and role:** `n8n-nodes-base.stickyNote`; describes notifications and record updates.
+- **Configuration choices:** Contains text only.
+- **Input and output connections:** None
+- **Version-specific requirements:** Type version `1`
+- **Edge cases / failures:** None
+- **Sub-workflow reference:** None
+
+---
+
+# 3. Summary Table
+
+| Node Name | Node Type | Functional Role | Input Node(s) | Output Node(s) | Sticky Note |
+|---|---|---|---|---|---|
+| Receive Seller Data | webhook | Receives incoming seller compliance payload via HTTP POST |  | Governance Agent | ## How It Works<br>This workflow automates marketplace seller compliance monitoring and enforcement for platform trust, legal, and operations teams. It receives seller data via webhook, routes it through a central Governance Agent backed by persistent memory, and fans out to four specialised AI sub-agents: Policy Monitoring (with compliance scoring and violation severity calculation), Appeals Review, and Enforcement Decision. A Structured Enforcement Output parser standardises results before routing to enforcement actions. The workflow then prepares an audit log, writes to an Enforcement Audit Trail, and triggers multi-channel notifications — Gmail appeal decisions, warning emails, Slack alerts to the compliance team, and suspension notices. Finally, seller records are updated in a Seller Compliance Records store. This eliminates manual case reviews, ensures consistent policy application, and creates a full auditable enforcement trail at scale.<br>## Policy & Violation Analysis<br>**What** — Policy Monitoring Agent scores compliance; Violation Severity Scorer calculates risk level.<br>**Why** — Quantifies risk objectively, removing human bias from initial triage. |
+| Governance Agent | langchain agent | Central orchestrator for compliance assessment and final decision | Receive Seller Data; Governance Model; Governance Memory; Policy Monitoring Agent; Appeals Review Agent; Enforcement Decision Agent; Structured Enforcement Output | Route Enforcement Action | ## Policy & Violation Analysis<br>**What** — Policy Monitoring Agent scores compliance; Violation Severity Scorer calculates risk level.<br>**Why** — Quantifies risk objectively, removing human bias from initial triage. |
+| Governance Model | OpenAI Chat Model | LLM powering the Governance Agent |  | Governance Agent | ## Policy & Violation Analysis<br>**What** — Policy Monitoring Agent scores compliance; Violation Severity Scorer calculates risk level.<br>**Why** — Quantifies risk objectively, removing human bias from initial triage. |
+| Governance Memory | memoryBufferWindow | Provides short-term memory to the Governance Agent |  | Governance Agent | ## Policy & Violation Analysis<br>**What** — Policy Monitoring Agent scores compliance; Violation Severity Scorer calculates risk level.<br>**Why** — Quantifies risk objectively, removing human bias from initial triage. |
+| Structured Enforcement Output | structured output parser | Enforces structured final decision schema | Output Parser Model | Governance Agent | ## Appeals & Enforcement Decision<br>**What** — Appeals Review Agent and Enforcement Decision Agent evaluate case in parallel.<br>**Why** — Separates appeals logic from enforcement to ensure fair, independent assessment. |
+| Policy Monitoring Agent | langchain agent tool | Specialized tool for policy validation and compliance assessment | Policy Monitoring Model; Compliance Calculator; Violation Severity Scorer | Governance Agent | ## Policy & Violation Analysis<br>**What** — Policy Monitoring Agent scores compliance; Violation Severity Scorer calculates risk level.<br>**Why** — Quantifies risk objectively, removing human bias from initial triage. |
+| Appeals Review Agent | langchain agent tool | Specialized tool for appeal evaluation | Appeals Review Model | Governance Agent | ## Appeals & Enforcement Decision<br>**What** — Appeals Review Agent and Enforcement Decision Agent evaluate case in parallel.<br>**Why** — Separates appeals logic from enforcement to ensure fair, independent assessment. |
+| Enforcement Decision Agent | langchain agent tool | Specialized tool for selecting enforcement action | Enforcement Decision Model | Governance Agent | ## Appeals & Enforcement Decision<br>**What** — Appeals Review Agent and Enforcement Decision Agent evaluate case in parallel.<br>**Why** — Separates appeals logic from enforcement to ensure fair, independent assessment. |
+| Policy Monitoring Model | OpenAI Chat Model | LLM for policy monitoring analysis |  | Policy Monitoring Agent | ## Policy & Violation Analysis<br>**What** — Policy Monitoring Agent scores compliance; Violation Severity Scorer calculates risk level.<br>**Why** — Quantifies risk objectively, removing human bias from initial triage. |
+| Appeals Review Model | OpenAI Chat Model | LLM for appeal review |  | Appeals Review Agent | ## Appeals & Enforcement Decision<br>**What** — Appeals Review Agent and Enforcement Decision Agent evaluate case in parallel.<br>**Why** — Separates appeals logic from enforcement to ensure fair, independent assessment. |
+| Enforcement Decision Model | OpenAI Chat Model | LLM for enforcement recommendation |  | Enforcement Decision Agent | ## Appeals & Enforcement Decision<br>**What** — Appeals Review Agent and Enforcement Decision Agent evaluate case in parallel.<br>**Why** — Separates appeals logic from enforcement to ensure fair, independent assessment. |
+| Output Parser Model | OpenAI Chat Model | Lightweight model to help structured parsing |  | Structured Enforcement Output | ## Appeals & Enforcement Decision<br>**What** — Appeals Review Agent and Enforcement Decision Agent evaluate case in parallel.<br>**Why** — Separates appeals logic from enforcement to ensure fair, independent assessment. |
+| Compliance Calculator | calculator tool | Math helper for compliance analysis |  | Policy Monitoring Agent | ## Policy & Violation Analysis<br>**What** — Policy Monitoring Agent scores compliance; Violation Severity Scorer calculates risk level.<br>**Why** — Quantifies risk objectively, removing human bias from initial triage. |
+| Violation Severity Scorer | code tool | Computes numeric severity score and severity label |  | Policy Monitoring Agent | ## Policy & Violation Analysis<br>**What** — Policy Monitoring Agent scores compliance; Violation Severity Scorer calculates risk level.<br>**Why** — Quantifies risk objectively, removing human bias from initial triage. |
+| Route Enforcement Action | switch | Branches by final enforcement action | Governance Agent | Prepare Audit Log | ## Audit & Route Actions<br>**What** — Formats audit log, writes to Enforcement Audit Trail, routes enforcement action.<br>**Why** — Guarantees full traceability and triggers the correct downstream response path. |
+| Prepare Audit Log | set | Maps final decision into audit record fields | Route Enforcement Action | Enforcement Audit Trail | ## Audit & Route Actions<br>**What** — Formats audit log, writes to Enforcement Audit Trail, routes enforcement action.<br>**Why** — Guarantees full traceability and triggers the correct downstream response path. |
+| Enforcement Audit Trail | dataTable | Stores auditable enforcement event records | Prepare Audit Log | Notify Compliance Team; Route Notifications | ## Audit & Route Actions<br>**What** — Formats audit log, writes to Enforcement Audit Trail, routes enforcement action.<br>**Why** — Guarantees full traceability and triggers the correct downstream response path. |
+| Notify Compliance Team | slack | Sends internal Slack alert | Enforcement Audit Trail |  | ## Notify & Record<br>**What** — Sends Gmail decisions, warning emails, Slack alerts, suspension notices; updates seller records.<br>**Why** — Closes the loop with all stakeholders and maintains a live compliance database. |
+| Send Warning Email | gmail | Sends seller warning notice | Route Notifications | Prepare Seller Record | ## Notify & Record<br>**What** — Sends Gmail decisions, warning emails, Slack alerts, suspension notices; updates seller records.<br>**Why** — Closes the loop with all stakeholders and maintains a live compliance database. |
+| Send Suspension Notice | gmail | Sends seller suspension notice | Route Notifications | Prepare Seller Record | ## Notify & Record<br>**What** — Sends Gmail decisions, warning emails, Slack alerts, suspension notices; updates seller records.<br>**Why** — Closes the loop with all stakeholders and maintains a live compliance database. |
+| Send Appeal Decision | gmail | Sends appeal approval or denial email | Route Notifications | Prepare Seller Record | ## Notify & Record<br>**What** — Sends Gmail decisions, warning emails, Slack alerts, suspension notices; updates seller records.<br>**Why** — Closes the loop with all stakeholders and maintains a live compliance database. |
+| Seller Compliance Records | dataTable | Upserts latest seller compliance state | Prepare Seller Record |  | ## Notify & Record<br>**What** — Sends Gmail decisions, warning emails, Slack alerts, suspension notices; updates seller records.<br>**Why** — Closes the loop with all stakeholders and maintains a live compliance database. |
+| Prepare Seller Record | set | Formats seller status record for persistence | Send Warning Email; Send Suspension Notice; Send Appeal Decision | Seller Compliance Records | ## Notify & Record<br>**What** — Sends Gmail decisions, warning emails, Slack alerts, suspension notices; updates seller records.<br>**Why** — Closes the loop with all stakeholders and maintains a live compliance database. |
+| Route Notifications | switch | Routes seller-facing notifications by action | Enforcement Audit Trail | Send Warning Email; Send Suspension Notice; Send Appeal Decision | ## Notify & Record<br>**What** — Sends Gmail decisions, warning emails, Slack alerts, suspension notices; updates seller records.<br>**Why** — Closes the loop with all stakeholders and maintains a live compliance database. |
+| Sticky Note | stickyNote | Canvas documentation for prerequisites and benefits |  |  | ## Prerequisites<br>- Gmail account with OAuth2 credentials<br>- Slack workspace and bot token<br>- Database or Google Sheets for audit and records storage<br>## Use Cases<br>- Automated suspension and warning issuance for policy-violating marketplace sellers<br>## Customisation<br>- Swap enforcement channels (e.g., replace Gmail with SendGrid)<br>## Benefits<br>- Eliminates manual seller case reviews — scales enforcement without added headcount |
+| Sticky Note1 | stickyNote | Canvas documentation for setup steps |  |  | ## Setup Steps<br>1. Configure webhook URL in **Receive Seller Data** node and secure with authentication.<br>2. Set AI model credentials (OpenAI/Anthropic) in all agent and model nodes.<br>3. Add Slack credentials and target channel to **Notify Compliance Team** node.<br>4. Connect database/Google Sheets credentials.<br>5. Activate and test with a sample seller payload. |
+| Sticky Note2 | stickyNote | Canvas documentation for workflow behavior |  |  | ## How It Works<br>This workflow automates marketplace seller compliance monitoring and enforcement for platform trust, legal, and operations teams. It receives seller data via webhook, routes it through a central Governance Agent backed by persistent memory, and fans out to four specialised AI sub-agents: Policy Monitoring (with compliance scoring and violation severity calculation), Appeals Review, and Enforcement Decision. A Structured Enforcement Output parser standardises results before routing to enforcement actions. The workflow then prepares an audit log, writes to an Enforcement Audit Trail, and triggers multi-channel notifications — Gmail appeal decisions, warning emails, Slack alerts to the compliance team, and suspension notices. Finally, seller records are updated in a Seller Compliance Records store. This eliminates manual case reviews, ensures consistent policy application, and creates a full auditable enforcement trail at scale. |
+| Sticky Note3 | stickyNote | Canvas documentation for audit and routing section |  |  | ## Audit & Route Actions<br>**What** — Formats audit log, writes to Enforcement Audit Trail, routes enforcement action.<br>**Why** — Guarantees full traceability and triggers the correct downstream response path. |
+| Sticky Note4 | stickyNote | Canvas documentation for appeals and enforcement section |  |  | ## Appeals & Enforcement Decision<br>**What** — Appeals Review Agent and Enforcement Decision Agent evaluate case in parallel.<br>**Why** — Separates appeals logic from enforcement to ensure fair, independent assessment. |
+| Sticky Note5 | stickyNote | Canvas documentation for policy and violation section |  |  | ## Policy & Violation Analysis<br>**What** — Policy Monitoring Agent scores compliance; Violation Severity Scorer calculates risk level.<br>**Why** — Quantifies risk objectively, removing human bias from initial triage. |
+| Sticky Note6 | stickyNote | Canvas documentation for notifications and record updates |  |  | ## Notify & Record<br>**What** — Sends Gmail decisions, warning emails, Slack alerts, suspension notices; updates seller records.<br>**Why** — Closes the loop with all stakeholders and maintains a live compliance database. |
+
+---
+
+# 4. Reproducing the Workflow from Scratch
+
+1. **Create a new workflow**
+   - Name it something like: `Intelligent seller governance enforcement and compliance automation`.
+
+2. **Add the webhook trigger**
+   - Create a `Webhook` node named `Receive Seller Data`.
+   - Set:
+     - Method: `POST`
+     - Path: `governance/seller-compliance`
+   - Recommended hardening:
+     - enable authentication or front it with a protected API gateway
+     - validate inbound JSON before AI processing
+
+3. **Add the central AI agent**
+   - Create an `AI Agent` node named `Governance Agent`.
+   - Set prompt mode to define the prompt directly.
+   - Set input text to:
+     - `{{$json.body}}`
+   - Paste a system message describing the governance role:
+     - orchestrates policy monitoring, appeals, and enforcement
+     - ensures fairness and auditability
+     - escalates high-stakes cases
+   - Enable structured output parsing.
+
+4. **Add the governance model**
+   - Create an `OpenAI Chat Model` node named `Governance Model`.
+   - Select model `gpt-4o`.
+   - Set temperature to `0.2`.
+   - Connect it to the `Governance Agent` using the AI language model connection.
+   - Configure OpenAI credentials.
+
+5. **Add memory for the governance agent**
+   - Create a `Memory Buffer Window` node named `Governance Memory`.
+   - Keep default settings unless you want a larger or smaller memory window.
+   - Connect it to the `Governance Agent` via the AI memory connection.
+
+6. **Add the structured output parser**
+   - Create a `Structured Output Parser` node named `Structured Enforcement Output`.
+   - Set schema mode to manual.
+   - Enable auto-fix.
+   - Define a schema with these fields:
+     - `seller_id` string
+     - `violation_type` string
+     - `severity` enum: `low`, `medium`, `high`, `critical`
+     - `enforcement_action` enum: `warning`, `review`, `suspension`, `termination`, `appeal_approved`, `appeal_denied`
+     - `reasoning` string
+     - `evidence` array of strings
+     - `requires_human_review` boolean
+     - `timestamp` string
+   - Make required:
+     - `seller_id`
+     - `violation_type`
+     - `severity`
+     - `enforcement_action`
+     - `reasoning`
+     - `requires_human_review`
+
+7. **Add the parser model**
+   - Create an `OpenAI Chat Model` node named `Output Parser Model`.
+   - Set model to `gpt-4o-mini`.
+   - Set temperature to `0`.
+   - Connect it to `Structured Enforcement Output` as the parser model.
+   - Connect `Structured Enforcement Output` to `Governance Agent` as the AI output parser.
+
+8. **Add the Policy Monitoring tool agent**
+   - Create an `AI Agent Tool` node named `Policy Monitoring Agent`.
+   - Set text input to:
+     - `{{$fromAI('seller_data', 'Seller behavior and compliance data to validate')}}`
+   - Add a system message instructing it to:
+     - validate seller behavior against policies
+     - analyze return rates, complaint ratios, response times, prohibited items, etc.
+     - detect fraud/non-compliance
+     - assess severity
+     - return evidence-based findings
+   - Add a tool description summarizing that capability.
+   - Connect this node to `Governance Agent` as an AI tool.
+
+9. **Add the Policy Monitoring model**
+   - Create an `OpenAI Chat Model` node named `Policy Monitoring Model`.
+   - Use `gpt-4o`, temperature `0.2`.
+   - Connect it to `Policy Monitoring Agent`.
+   - Reuse the same OpenAI credential if desired.
+
+10. **Add the calculator tool**
+    - Create a `Calculator Tool` node named `Compliance Calculator`.
+    - Leave default configuration.
+    - Connect it to `Policy Monitoring Agent` as an AI tool.
+
+11. **Add the severity code tool**
+    - Create a `Code Tool` node named `Violation Severity Scorer`.
+    - Use JavaScript.
+    - Add logic that:
+      - reads `return_rate`, `complaint_count`, `violation_history`, and `avg_response_time` from `$fromAI(...)`
+      - computes a weighted score up to 100
+      - maps score to `low`, `medium`, `high`, or `critical`
+      - returns score, severity, and metric breakdown
+    - Connect it to `Policy Monitoring Agent` as an AI tool.
+
+12. **Add the Appeals Review tool agent**
+    - Create an `AI Agent Tool` node named `Appeals Review Agent`.
+    - Set text input to:
+      - `{{$fromAI('appeal_data', 'Appeal submission with seller arguments and evidence')}}`
+    - Add a system message emphasizing:
+      - fresh review
+      - fairness
+      - mitigation and procedural review
+      - benefit of doubt in ambiguous cases
+    - Add a tool description about appeal evaluation.
+    - Connect it to `Governance Agent` as an AI tool.
+
+13. **Add the Appeals Review model**
+    - Create an `OpenAI Chat Model` node named `Appeals Review Model`.
+    - Use `gpt-4o`, temperature `0.3`.
+    - Connect it to `Appeals Review Agent`.
+
+14. **Add the Enforcement Decision tool agent**
+    - Create an `AI Agent Tool` node named `Enforcement Decision Agent`.
+    - Set text input to:
+      - `{{$fromAI('violation_assessment', 'Violation assessment and seller context for enforcement decision')}}`
+    - Add a system message covering:
+      - progressive discipline
+      - mitigating factors
+      - consistency across similar cases
+      - human review for suspensions/terminations
+    - Add a tool description about deciding warning/review/suspension/termination.
+    - Connect it to `Governance Agent` as an AI tool.
+
+15. **Add the Enforcement Decision model**
+    - Create an `OpenAI Chat Model` node named `Enforcement Decision Model`.
+    - Use `gpt-4o`, temperature `0.2`.
+    - Connect it to `Enforcement Decision Agent`.
+
+16. **Connect the trigger to the orchestrator**
+    - Connect `Receive Seller Data` main output to `Governance Agent` main input.
+
+17. **Add enforcement-action routing**
+    - Create a `Switch` node named `Route Enforcement Action`.
+    - Add six conditions on `{{$json.enforcement_action}}`:
+      - equals `warning`
+      - equals `review`
+      - equals `suspension`
+      - equals `termination`
+      - equals `appeal_approved`
+      - equals `appeal_denied`
+    - Connect `Governance Agent` to this node.
+    - Connect every branch to the same next node, `Prepare Audit Log`.
+
+18. **Add audit-log formatting**
+    - Create a `Set` node named `Prepare Audit Log`.
+    - Add fields:
+      - `seller_id` = `{{$json.seller_id}}`
+      - `violation_type` = `{{$json.violation_type}}`
+      - `severity` = `{{$json.severity}}`
+      - `enforcement_action` = `{{$json.enforcement_action}}`
+      - `reasoning` = `{{$json.reasoning}}`
+      - `evidence` = `{{JSON.stringify($json.evidence)}}`
+      - `requires_human_review` = `{{$json.requires_human_review}}`
+      - `timestamp` = `{{$now.toISO()}}`
+      - `workflow_execution_id` = `{{$execution.id}}`
+    - Connect `Route Enforcement Action` to `Prepare Audit Log`.
+
+19. **Add audit persistence**
+    - Create a `Data Table` node named `Enforcement Audit Trail`.
+    - Set column mapping to auto-map input fields.
+    - Select or create a data table for audit storage.
+    - Replace the placeholder with the actual table ID.
+    - Connect `Prepare Audit Log` to `Enforcement Audit Trail`.
+
+20. **Add internal Slack notification**
+    - Create a `Slack` node named `Notify Compliance Team`.
+    - Use OAuth2 authentication.
+    - Post to a selected compliance channel.
+    - Message should include:
+      - seller ID
+      - action
+      - violation
+      - severity
+      - reasoning
+      - human review flag
+      - execution ID
+    - Connect `Enforcement Audit Trail` to `Notify Compliance Team`.
+    - Configure Slack credentials and ensure the bot can post to the chosen channel.
+
+21. **Add seller notification routing**
+    - Create a second `Switch` node named `Route Notifications`.
+    - Add four conditions on `{{$json.enforcement_action}}`:
+      - `warning`
+      - `suspension`
+      - `appeal_approved`
+      - `appeal_denied`
+    - Connect `Enforcement Audit Trail` to `Route Notifications`.
+
+22. **Add warning email**
+    - Create a `Gmail` node named `Send Warning Email`.
+    - Configure Gmail OAuth2 credentials.
+    - Set recipient to the seller email source you want to use.
+      - In the JSON it is still a placeholder, so in a real build use something like `{{$json.seller_email}}`.
+    - Subject:
+      - `Policy Violation Warning - Action Required`
+    - Body should include:
+      - violation type
+      - severity
+      - reasoning
+      - evidence
+      - remediation timeline
+      - appeal instructions
+    - Connect the `warning` branch from `Route Notifications` to this node.
+
+23. **Add suspension email**
+    - Create a `Gmail` node named `Send Suspension Notice`.
+    - Use the same Gmail credential.
+    - Set seller recipient.
+    - Subject:
+      - `Account Suspension Notice - Immediate Action Required`
+    - Body should explain:
+      - violation details
+      - immediate suspension
+      - appeal deadline
+      - evidence/documentation required
+    - Connect the `suspension` branch from `Route Notifications` to this node.
+
+24. **Add appeal decision email**
+    - Create a `Gmail` node named `Send Appeal Decision`.
+    - Use a conditional body expression:
+      - if `enforcement_action` is `appeal_approved`, send approval message
+      - else send denial message
+    - Subject should similarly switch between `Approved` and `Denied`.
+    - Connect both `appeal_approved` and `appeal_denied` branches from `Route Notifications` to this node.
+
+25. **Add seller record preparation**
+    - Create a `Set` node named `Prepare Seller Record`.
+    - Map:
+      - `seller_id`
+      - `current_status` = `{{$json.enforcement_action}}`
+      - `last_violation_type`
+      - `last_violation_severity`
+      - `last_action_date` = `{{$now.toISO()}}`
+      - `total_violations` = `{{($json.total_violations || 0) + 1}}`
+      - `requires_monitoring` = `{{$json.severity === 'high' || $json.severity === 'critical'}}`
+    - Connect:
+      - `Send Warning Email` → `Prepare Seller Record`
+      - `Send Suspension Notice` → `Prepare Seller Record`
+      - `Send Appeal Decision` → `Prepare Seller Record`
+
+26. **Add seller compliance record storage**
+    - Create a `Data Table` node named `Seller Compliance Records`.
+    - Set operation to `Upsert`.
+    - Add filter:
+      - key `seller_id`
+      - value `{{$json.seller_id}}`
+    - Enable auto-map input fields.
+    - Replace the placeholder data table ID with a real table.
+    - Connect `Prepare Seller Record` to `Seller Compliance Records`.
+
+27. **Add visual documentation if desired**
+    - Create sticky notes for:
+      - prerequisites
+      - setup steps
+      - architecture overview
+      - policy analysis
+      - appeals/enforcement
+      - audit/routing
+      - notifications/recording
+
+28. **Configure credentials**
+    - **OpenAI**
+      - Required for:
+        - Governance Model
+        - Policy Monitoring Model
+        - Appeals Review Model
+        - Enforcement Decision Model
+        - Output Parser Model
+    - **Gmail OAuth2**
+      - Required for:
+        - Send Warning Email
+        - Send Suspension Notice
+        - Send Appeal Decision
+    - **Slack OAuth2**
+      - Required for:
+        - Notify Compliance Team
+    - **Data Table / storage**
+      - Ensure both data tables exist and the n8n instance supports Data Tables
+
+29. **Replace all placeholders**
+    - Replace:
+      - seller email placeholder
+      - compliance Slack channel placeholder
+      - enforcement audit trail table ID placeholder
+      - seller compliance records table ID placeholder
+
+30. **Test with a sample payload**
+    - Use a JSON payload containing at minimum:
+      - seller ID
+      - violation details
+      - compliance metrics
+      - appeal data if relevant
+      - ideally seller email
+    - Confirm the Governance Agent returns valid schema fields.
+
+31. **Validate edge behavior before activation**
+    - Test all supported action outputs:
+      - `warning`
+      - `review`
+      - `suspension`
+      - `termination`
+      - `appeal_approved`
+      - `appeal_denied`
+    - Important observation:
+      - `review` and `termination` currently go to audit logging and Slack, but not to seller-facing email or seller-record update paths.
+    - If that is not intentional, add extra branches for those actions.
+
+32. **Activate the workflow**
+    - Once credentials, table IDs, and recipient sourcing are correct, activate the workflow and expose the production webhook URL.
+
+---
+
+# 5. General Notes & Resources
+
+| Note Content | Context or Link |
+|---|---|
+| Gmail account with OAuth2 credentials required | Prerequisites |
+| Slack workspace and bot token required | Prerequisites |
+| Database or Google Sheets for audit and records storage | Prerequisites note; actual workflow uses n8n Data Tables |
+| Automated suspension and warning issuance for policy-violating marketplace sellers | Use case |
+| Swap enforcement channels, for example replacing Gmail with SendGrid | Customisation |
+| Eliminates manual seller case reviews and scales enforcement without added headcount | Benefit |
+| Configure webhook URL in `Receive Seller Data` and secure it with authentication | Setup guidance |
+| Set AI model credentials in all agent and model nodes | Setup guidance |
+| Add Slack credentials and target channel to `Notify Compliance Team` | Setup guidance |
+| Connect database or storage credentials and tables | Setup guidance |
+| Activate and test with a sample seller payload | Setup guidance |
+| The workflow uses a central governance agent plus specialized policy, appeals, and enforcement agents | Architecture note |
+| The workflow creates an audit trail before notifications | Operational note |
+| The workflow currently does not send seller-facing notifications for `review` or `termination` actions | Important implementation note |
+| The workflow currently increments `total_violations` only from incoming data, not by reading the existing record first | Data consistency note |
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