{ "name": "Planning-Reflection Loop - Quality Assurance", "nodes": [ { "parameters": {}, "id": "reflection-trigger", "name": "Execute Workflow Trigger", "type": "n8n-nodes-base.executeWorkflowTrigger", "typeVersion": 1.1, "position": [ 100, 300 ] }, { "parameters": { "jsCode": "const input = $input.first().json;\nconst agentOutput = input.agent_output || {};\nconst agentName = input.agent_name || 'Unknown Agent';\nconst requirements = input.original_requirements || {};\nconst phaseNumber = input.phase_number || 1;\nconst maxIterations = input.max_iterations || 3;\nconst currentIteration = input.current_iteration || 1;\n\nreturn {\n json: {\n agent_output: agentOutput,\n agent_name: agentName,\n requirements: requirements,\n phase_number: phaseNumber,\n max_iterations: maxIterations,\n current_iteration: currentIteration,\n evaluation_timestamp: new Date().toISOString()\n }\n};" }, "id": "reflection-init", "name": "Initialize Reflection", "type": "n8n-nodes-base.code", "typeVersion": 2, "position": [ 320, 300 ] }, { "parameters": { "jsCode": "const context = $input.first().json;\nconst output = context.agent_output;\nconst agentName = context.agent_name;\n\n// Quality Evaluation Criteria\nconst evaluationCriteria = {\n completeness: { weight: 0.35, description: 'All required sections present' },\n accuracy: { weight: 0.25, description: 'Information is correct and relevant' },\n actionability: { weight: 0.25, description: 'Clear next steps and action items' },\n consistency: { weight: 0.15, description: 'Aligned with requirements and other outputs' }\n};\n\n// Evaluate Completeness\nlet completenessScore = 0;\nif (output.summary) completenessScore += 25;\nif (output.action_items && output.action_items.length > 0) completenessScore += 25;\nif (output.next_steps && output.next_steps.length > 0) completenessScore += 25;\nif (output.metadata) completenessScore += 25;\n\n// Evaluate Actionability\nlet actionabilityScore = 0;\nif (output.action_items) {\n const hasDeadlines = output.action_items.every(a => a.deadline);\n const hasResponsible = output.action_items.every(a => a.responsible);\n const hasPriority = output.action_items.every(a => a.priority);\n actionabilityScore = (hasDeadlines ? 33 : 0) + (hasResponsible ? 33 : 0) + (hasPriority ? 34 : 0);\n}\n\n// Evaluate Accuracy (basic check - presence of expected fields)\nlet accuracyScore = 0;\nconst agentExpectedFields = {\n 'Legal Agent': ['documents', 'compliance'],\n 'Finance Agent': ['revenue_projections', 'pricing', 'taxes'],\n 'Tech Agent': ['tech_stack', 'architecture', 'api'],\n 'Design Agent': ['colors', 'typography', 'components'],\n 'Marketing Agent': ['pre_launch', 'content', 'seo']\n};\n\nconst expectedFields = agentExpectedFields[agentName] || [];\nif (expectedFields.length > 0) {\n const presentFields = expectedFields.filter(f => output[f]);\n accuracyScore = (presentFields.length / expectedFields.length) * 100;\n} else {\n accuracyScore = 75; // Default if unknown agent\n}\n\n// Evaluate Consistency\nlet consistencyScore = 80; // Base score, would be calculated against other agents in full system\n\n// Calculate Weighted Total Score\nconst totalScore = Math.round(\n (completenessScore * evaluationCriteria.completeness.weight) +\n (accuracyScore * evaluationCriteria.accuracy.weight) +\n (actionabilityScore * evaluationCriteria.actionability.weight) +\n (consistencyScore * evaluationCriteria.consistency.weight)\n);\n\n// Identify Issues\nconst issues = [];\nif (completenessScore < 75) issues.push('Missing required sections in output');\nif (actionabilityScore < 75) issues.push('Action items missing deadlines, priorities, or responsible parties');\nif (accuracyScore < 75) issues.push('Missing expected deliverables for this agent type');\nif (consistencyScore < 75) issues.push('Output may not be consistent with requirements');\n\n// Determine Decision\nlet decision;\nlet reasoning;\nif (totalScore >= 80) {\n decision = 'PROCEED';\n reasoning = 'Output meets quality standards';\n} else if (totalScore >= 50 && context.current_iteration < context.max_iterations) {\n decision = 'IMPROVE';\n reasoning = 'Output needs improvement, requesting revision';\n} else if (totalScore < 50) {\n decision = 'ESCALATE';\n reasoning = 'Output quality too low, human intervention required';\n} else {\n decision = 'PROCEED_WITH_WARNINGS';\n reasoning = 'Max iterations reached, proceeding with warnings';\n}\n\nreturn {\n json: {\n evaluation: {\n agent_name: agentName,\n scores: {\n completeness: completenessScore,\n accuracy: accuracyScore,\n actionability: actionabilityScore,\n consistency: consistencyScore,\n total: totalScore\n },\n issues: issues,\n decision: decision,\n reasoning: reasoning,\n iteration: context.current_iteration,\n max_iterations: context.max_iterations\n },\n original_output: output,\n requirements: context.requirements,\n timestamp: new Date().toISOString()\n }\n};" }, "id": "reflection-evaluate", "name": "Evaluate Output Quality", "type": "n8n-nodes-base.code", "typeVersion": 2, "position": [ 540, 300 ] }, { "parameters": { "rules": { "rules": [ { "value": "PROCEED", "outputKey": "proceed" }, { "value": "IMPROVE", "outputKey": "improve" }, { "value": "ESCALATE", "outputKey": "escalate" }, { "value": "PROCEED_WITH_WARNINGS", "outputKey": "proceed_warnings" } ] }, "dataType": "string", "value": "={{ $json.evaluation.decision }}" }, "id": "reflection-router", "name": "Decision Router", "type": "n8n-nodes-base.switch", "typeVersion": 3, "position": [ 760, 300 ] }, { "parameters": { "jsCode": "const input = $input.first().json;\nreturn {\n json: {\n status: 'QUALITY_APPROVED',\n agent_name: input.evaluation.agent_name,\n quality_score: input.evaluation.scores.total,\n output: input.original_output,\n message: 'Output passed quality check and is ready for next phase',\n timestamp: new Date().toISOString()\n }\n};" }, "id": "reflection-proceed", "name": "Proceed to Next Phase", "type": "n8n-nodes-base.code", "typeVersion": 2, "position": [ 980, 160 ] }, { "parameters": { "jsCode": "const input = $input.first().json;\nreturn {\n json: {\n status: 'IMPROVEMENT_NEEDED',\n agent_name: input.evaluation.agent_name,\n quality_score: input.evaluation.scores.total,\n issues: input.evaluation.issues,\n improvement_instructions: input.evaluation.issues.map(issue => ({\n issue: issue,\n action: 'Revise output to address: ' + issue\n })),\n iteration: input.evaluation.iteration + 1,\n max_iterations: input.evaluation.max_iterations,\n original_requirements: input.requirements,\n timestamp: new Date().toISOString()\n }\n};" }, "id": "reflection-improve", "name": "Request Improvement", "type": "n8n-nodes-base.code", "typeVersion": 2, "position": [ 980, 300 ] }, { "parameters": { "jsCode": "const input = $input.first().json;\nreturn {\n json: {\n status: 'ESCALATED_TO_HUMAN',\n agent_name: input.evaluation.agent_name,\n quality_score: input.evaluation.scores.total,\n issues: input.evaluation.issues,\n reason: 'Quality score below acceptable threshold after ' + input.evaluation.iteration + ' iterations',\n requires_action: 'Human review and intervention required',\n original_output: input.original_output,\n requirements: input.requirements,\n timestamp: new Date().toISOString()\n }\n};" }, "id": "reflection-escalate", "name": "Escalate to Human", "type": "n8n-nodes-base.code", "typeVersion": 2, "position": [ 980, 440 ] } ], "connections": { "Execute Workflow Trigger": { "main": [ [ { "node": "Initialize Reflection", "type": "main", "index": 0 } ] ] }, "Initialize Reflection": { "main": [ [ { "node": "Evaluate Output Quality", "type": "main", "index": 0 } ] ] }, "Evaluate Output Quality": { "main": [ [ { "node": "Decision Router", "type": "main", "index": 0 } ] ] }, "Decision Router": { "main": [ [ { "node": "Proceed to Next Phase", "type": "main", "index": 0 } ], [ { "node": "Request Improvement", "type": "main", "index": 0 } ], [ { "node": "Escalate to Human", "type": "main", "index": 0 } ], [ { "node": "Proceed to Next Phase", "type": "main", "index": 0 } ] ] } }, "settings": { "executionOrder": "v1" } }