AETHERIA

Intelligent Crisis Response & Communication Orchestrator (ICRCO)

Initializing...

\n \n

\n\n`;\n}",[148],"0eb5ddea-36c3-11f0-bbca-9e149126039e","types.ts","07805a00-36c3-11f0-9b06-9e149126039e",27,["D",1747887184174],"// src/types.ts\nimport { z } from \"npm:zod\"; // Assuming Zod is available globally or via npm\nimport {\n DataSensitivityRating,\n IncidentSeverity,\n LogLevel,\n OutputTone,\n ProcessDefinitionId,\n} from \"./constants_and_ids\";\n\n// --- Core Orchestration Types ---\nexport type TaskInput = {\n masterIncidentId: string;\n taskId: string;\n payload: T_Payload;\n};\n\nexport type TaskOutput = {\n masterIncidentId: string;\n correlationId: string; // taskId from input\n result: T_Result;\n error?: string;\n diagnosticInfo?: any;\n};\n\nexport type LogFunction = (\n level: LogLevel,\n component: string,\n message: string,\n details?: any,\n masterIncidentId?: string,\n taskId?: string,\n) => void;\n\nexport type LogEntry = {\n timestamp: string;\n masterIncidentId: string;\n taskId?: string;\n level: LogLevel;\n component: string;\n message: string;\n details?: any;\n};\n\n// --- Base Input for Process Data (Context for AI) ---\nexport type BaseIncidentContext = {\n incidentSeverity?: IncidentSeverity;\n outputTone?: OutputTone;\n confidentialityLevel?: \"INTERNAL\" | \"PUBLIC_HOLD\" | \"PUBLIC_RELEASE\";\n [key: string]: any; // Allow other contextual data\n};\n\n// --- Process-Specific Output Structures ---\nexport type IncidentImpactAssessmentOut = {\n incidentSummary: string;\n affectedSystems: string[];\n potentialUserImpact: string;\n dataSensitivity: DataSensitivityRating;\n estimatedContainmentTime: string;\n keyKnownFacts: string[];\n keyUnknowns: string[];\n immediateActionsTaken: string[];\n internalEscalationPath: string;\n suggestedNextReviewTime: string;\n};\n\nexport type StakeholderCommunicationDraftOut = {\n incidentTitle: string;\n targetStakeholder: string;\n communicationChannel: string;\n subjectLineOrHeadline: string;\n coreMessage: string;\n keyPoints: string[];\n callToAction?: string;\n dosAndDonts?: string[];\n nextUpdateExpectation?: string;\n};\n\nexport type ExecutiveBriefingPointsOut = {\n incidentOverview: string;\n currentStatus: string;\n businessImpact: string;\n mitigationActions: string[];\n resourceNeeds?: string[];\n decisionPoints?: string[];\n commsStrategySummary: string;\n};\n\nexport type SystemResponse = {\n result?: any;\n error?: string;\n details?: string; // Additional details for user/UI (e.g. rendered HTML)\n diagnosticLog: LogEntry[];\n masterIncidentId: string;\n operationStatus: \"SUCCESS\" | \"PARTIAL_SUCCESS\" | \"ERROR\" | \"VALIDATION_FAILURE\";\n};\n\n// --- Questionnaire Engine Types ---\nexport type ProcessQuestion = {\n key: string;\n promptText: string;\n displayName?: string;\n type: \"text\" | \"textarea\" | \"select\" | \"multiselect\";\n options?: { label: string; value: string }[];\n placeholder?: string;\n optional?: boolean;\n validationRegex?: string;\n validationErrorMessage?: string;\n mapsTo: string;\n guidance?: string;\n};\n\nexport type QuestionnaireState = {\n sessionId: string;\n processId: ProcessDefinitionId;\n currentQuestions: ProcessQuestion[];\n answers: Partial>; // string | string[] for multiselect\n questionHistory: { questionId: string; fieldKey: string; text: string }[];\n isCompleted: boolean;\n totalSteps: number;\n currentStep: number;\n};\n\nexport type QuestionForUI = {\n id: string;\n fieldKey: string;\n text: string;\n type: ProcessQuestion[\"type\"];\n options?: ProcessQuestion[\"options\"];\n placeholder?: ProcessQuestion[\"placeholder\"];\n guidance?: ProcessQuestion[\"guidance\"];\n currentValue?: any; // string | string[]\n};\n\nexport type InputValidationError = {\n fieldKey: string; // Maps to ProcessQuestion.key\n message: string;\n};\n\nexport type QuestionnaireResponse = {\n sessionId: string;\n nextQuestion?: QuestionForUI;\n isCompleted: boolean;\n finalizedData?: OrchestratorInput;\n error?: string;\n fieldError?: { fieldKey: string; message: string }; // Specific error for current question\n state: QuestionnaireState | null;\n};\n\n// --- Zod Schema for Orchestrator Input & Type ---\n// This schema ensures that the data passed to the orchestrator is valid.\n// It's defined here because OrchestratorInput depends on ProcessDefinitionId.\nexport const OrchestratorInputSchema = z.object({\n processId: z.custom(\n // Actual validation depends on available process definitions,\n // so it's often done dynamically or by listing IDs here if static.\n (val) => typeof val === \"string\" && val.length > 0, // Basic check\n \"Invalid process ID for orchestration.\",\n ),\n data: z.record(z.any(), { description: \"Mapped data from questionnaire\" }),\n});\nexport type OrchestratorInput = z.infer;\n\n// --- Process Definition Core Structure ---\n// This is forward-declared here as ProcessDefinition uses many above types.\nexport type ProcessDefinition<\n T_Context extends BaseIncidentContext = BaseIncidentContext,\n T_Output extends Record = Record,\n> = {\n id: ProcessDefinitionId;\n title: string;\n objective: string;\n uiColorClass: string;\n questions: ProcessQuestion[];\n inputValidator: (\n data: T_Context,\n allQuestions: ProcessQuestion[], // For context like displayName\n ) => true | InputValidationError[];\n aiAgentConfiguration: {\n agentName: string;\n promptEngineer: (params: T_Context) => { systemPrompt: string; userPrompt: string };\n outputSchemaParser: (rawJson: string) => T_Output;\n outputPostProcessor?: (output: T_Output, inputContext: T_Context) => T_Output;\n resultRenderer?: (results: T_Output, escapeFn: (s: any) => string) => string;\n };\n};",[155],"be1912d0-36c2-11f0-9b06-9e149126039e","questionairre_agent.ts","a784cbc2-36c2-11f0-aed8-9e149126039e",15,["D",1747887048929],"// src/questionnaire_agent.ts\nimport { ProcessDefinitionId } from \"./constants_and_ids\";\nimport { getProcessDefinition } from \"./process_definitions\"; // Critical import\nimport {\n LogFunction,\n OrchestratorInput,\n ProcessDefinition,\n ProcessQuestion,\n QuestionForUI,\n QuestionnaireResponse,\n QuestionnaireState,\n} from \"./types\";\nimport { Utils } from \"./utils\";\n\nexport class QuestionnaireAgent {\n private logger: LogFunction;\n\n constructor(loggerFn: LogFunction) {\n this.logger = loggerFn;\n }\n\n private getQuestionKeys(questions: ProcessQuestion[], type: \"required\" | \"optional\" | \"all\"): string[] {\n if (type === \"all\") return questions.map(q => q.key);\n return questions.filter(q => type === \"required\" ? !q.optional : q.optional).map(q => q.key);\n }\n\n private isQuestionOptional(key: string, questions: ProcessQuestion[]): boolean {\n return !!questions.find(q => q.key === key && q.optional);\n }\n\n private selectNextQuestionKey(state: QuestionnaireState): string | null {\n const { currentQuestions, answers } = state;\n const requiredKeys = this.getQuestionKeys(currentQuestions, \"required\");\n const optionalKeys = this.getQuestionKeys(currentQuestions, \"optional\");\n\n for (const qKey of requiredKeys) {\n if (!(qKey in answers) || (answers[qKey] === \"\" && !this.isQuestionOptional(qKey, currentQuestions))) {\n this.logger(\"INFO\", \"QuestionnaireAgent\", `Selected next required question: ${qKey}`, {\n knownAnswers: Object.keys(answers),\n }, state.sessionId);\n return qKey;\n }\n }\n for (const qKey of optionalKeys) {\n if (!(qKey in answers)) {\n this.logger(\"INFO\", \"QuestionnaireAgent\", `Selected next optional question: ${qKey}`, {\n knownAnswers: Object.keys(answers),\n }, state.sessionId);\n return qKey;\n }\n }\n this.logger(\"AUDIT\", \"QuestionnaireAgent\", `All questions answered for process ${state.processId}.`, {\n answerKeys: Object.keys(answers),\n }, state.sessionId);\n return null;\n }\n\n private formatQuestionForUI(questionKey: string, questions: ProcessQuestion[], currentValue?: any): QuestionForUI {\n const questionDef = questions.find(q => q.key === questionKey);\n if (!questionDef) {\n this.logger(\n \"ERROR\",\n \"QuestionnaireAgent\",\n `Question definition not found for key: ${questionKey}`,\n {},\n undefined,\n );\n throw new Error(`Critical Error: Question definition missing for ${questionKey}.`);\n }\n let displayText = questionDef.promptText;\n if (questionDef.optional && !displayText.toLowerCase().includes(\"(optional)\")) {\n displayText += \" (Optional)\";\n }\n return {\n id: `${questionDef.key}-${Utils.generateShortId(4)}`,\n fieldKey: questionDef.key,\n text: displayText,\n type: questionDef.type,\n options: questionDef.options,\n placeholder: questionDef.placeholder,\n guidance: questionDef.guidance,\n currentValue: currentValue,\n };\n }\n\n private async validateAndTransition(\n state: QuestionnaireState,\n processDef: ProcessDefinition,\n ): Promise {\n this.logger(\n \"AUDIT\",\n \"QuestionnaireAgent\",\n `All fields gathered for ${state.processId}. Proceeding to final validation.`,\n { answersCount: Object.keys(state.answers).length },\n state.sessionId,\n );\n const finalizedInput = this.finalizeDataForOrchestrator(\n state.answers,\n state.currentQuestions,\n state.processId,\n state.sessionId,\n );\n\n const validationResult = processDef.inputValidator(finalizedInput.data, state.currentQuestions);\n\n if (validationResult === true) {\n this.logger(\n \"AUDIT\",\n \"QuestionnaireAgent\",\n `Input validation successful for ${state.processId}. Questionnaire complete.`,\n { dataKeys: Object.keys(finalizedInput.data) },\n state.sessionId,\n );\n state.isCompleted = true;\n state.currentStep = state.totalSteps;\n return { sessionId: state.sessionId, isCompleted: true, finalizedData: finalizedInput, state };\n } else {\n const firstError = validationResult[0];\n this.logger(\n \"WARN\",\n \"QuestionnaireAgent\",\n `Input validation failed for ${state.processId}. Error on field: ${firstError.fieldKey}`,\n { error: firstError, allErrors: validationResult },\n state.sessionId,\n );\n state.isCompleted = false;\n\n const questionToReaskKey = firstError.fieldKey; // This should be ProcessQuestion.key\n const questionDefToReask = state.currentQuestions.find(q => q.key === questionToReaskKey);\n\n if (!questionDefToReask) {\n this.logger(\n \"CRITICAL\",\n \"QuestionnaireAgent\",\n `Validation failed on field key ${questionToReaskKey}, but no corresponding question definition found to re-ask.`,\n {},\n state.sessionId,\n );\n return {\n sessionId: state.sessionId,\n isCompleted: false,\n error: `Internal Validation Error: Misconfigured validation for field key \"${questionToReaskKey}\".`,\n state,\n };\n }\n\n const previousValue = state.answers[questionToReaskKey]; // Use question's key\n delete state.answers[questionToReaskKey];\n const historyIndex = state.questionHistory.findIndex(h => h.fieldKey === questionToReaskKey);\n if (historyIndex !== -1) {\n state.questionHistory.splice(historyIndex, state.questionHistory.length - historyIndex);\n }\n state.currentStep = state.questionHistory.length;\n\n const reaskResponse = await this.nextStep(state.sessionId, state.processId, null, state);\n\n if (reaskResponse.nextQuestion && reaskResponse.nextQuestion.fieldKey === questionToReaskKey) {\n reaskResponse.fieldError = {\n fieldKey: questionToReaskKey,\n message: `${\n questionDefToReask.displayName || questionDefToReask.promptText.split(\"?\")[0]\n }: ${firstError.message}`,\n };\n if (reaskResponse.state && reaskResponse.nextQuestion) {\n reaskResponse.nextQuestion.currentValue = previousValue;\n }\n } else if (!reaskResponse.nextQuestion && !reaskResponse.isCompleted) {\n reaskResponse.error = `Validation Error: ${firstError.message}. Could not determine next question.`;\n }\n return reaskResponse;\n }\n }\n\n public async nextStep(\n sessionId: string | null,\n processId: ProcessDefinitionId,\n lastAnswer: { fieldKey: string; value: any } | null,\n currentState?: QuestionnaireState | null,\n ): Promise {\n const processDef = getProcessDefinition(processId);\n if (!processDef) {\n const newSessionId = sessionId || `ERR-${Utils.generateShortId()}`;\n this.logger(\n \"CRITICAL\",\n \"QuestionnaireAgent\",\n `Process definition not found for ID: ${processId}`,\n {},\n newSessionId,\n );\n return {\n sessionId: newSessionId,\n isCompleted: false,\n error: `Critical Error: Process ID \"${processId}\" is not defined.`,\n state: null,\n };\n }\n\n const questions = processDef.questions;\n const totalSteps = questions.length;\n const currentSessionId = sessionId || `S-${Utils.generateShortId()}`;\n\n let state: QuestionnaireState;\n\n if (\n !currentState || !sessionId || currentState.sessionId !== currentSessionId || currentState.processId !== processId\n ) {\n this.logger(\"AUDIT\", \"QuestionnaireAgent\", `Initializing new questionnaire state for process ${processId}.`, {\n newSessionId: currentSessionId,\n oldSessionId: sessionId,\n }, currentSessionId);\n state = {\n sessionId: currentSessionId,\n processId,\n currentQuestions: questions,\n answers: {},\n questionHistory: [],\n isCompleted: false,\n totalSteps,\n currentStep: 0,\n };\n if (lastAnswer?.fieldKey && questions.some(q => q.key === lastAnswer.fieldKey)) {\n const value = typeof lastAnswer.value === \"string\" ? lastAnswer.value.trim() : lastAnswer.value;\n if (value !== \"\" || this.isQuestionOptional(lastAnswer.fieldKey, questions)) {\n state.answers[lastAnswer.fieldKey] = value;\n }\n }\n } else {\n state = {\n ...currentState,\n answers: { ...currentState.answers },\n questionHistory: [...currentState.questionHistory],\n totalSteps,\n currentQuestions: questions,\n };\n if (lastAnswer?.fieldKey && questions.some(q => q.key === lastAnswer.fieldKey)) {\n const value = typeof lastAnswer.value === \"string\" ? lastAnswer.value.trim() : lastAnswer.value;\n if (\n (value !== \"\" && value !== null && value !== undefined)\n || this.isQuestionOptional(lastAnswer.fieldKey, questions)\n ) {\n state.answers[lastAnswer.fieldKey] = value;\n } else {\n delete state.answers[lastAnswer.fieldKey];\n }\n }\n }\n state.currentStep = state.questionHistory.length;\n\n const nextQuestionKey = this.selectNextQuestionKey(state);\n\n if (!nextQuestionKey) {\n return this.validateAndTransition(state, processDef);\n } else {\n state.currentStep = state.questionHistory.length + 1;\n const nextQuestionForUI = this.formatQuestionForUI(\n nextQuestionKey,\n state.currentQuestions,\n state.answers[nextQuestionKey],\n );\n if (\n !state.questionHistory.length\n || state.questionHistory[state.questionHistory.length - 1].fieldKey !== nextQuestionForUI.fieldKey\n ) {\n state.questionHistory.push({\n qid: nextQuestionForUI.id,\n fieldKey: nextQuestionForUI.fieldKey,\n text: nextQuestionForUI.text,\n });\n }\n\n this.logger(\n \"INFO\",\n \"QuestionnaireAgent\",\n `Asking question ${nextQuestionKey} (Step ${state.currentStep}/${state.totalSteps})`,\n { questionId: nextQuestionForUI.id, historyLength: state.questionHistory.length },\n state.sessionId,\n );\n return { sessionId: state.sessionId, isCompleted: false, nextQuestion: nextQuestionForUI, state };\n }\n }\n\n public async goBack(\n sessionId: string,\n processId: ProcessDefinitionId,\n currentState: QuestionnaireState,\n ): Promise {\n this.logger(\n \"AUDIT\",\n \"QuestionnaireAgent\",\n `Processing 'goBack' request. Current Step: ${currentState.currentStep}, History Length: ${currentState.questionHistory.length}`,\n {},\n sessionId,\n );\n\n let stateCopy = Utils.deepCloneWithCircularityHandling(currentState);\n\n const processDef = getProcessDefinition(processId);\n if (!processDef) {\n this.logger(\n \"CRITICAL\",\n \"QuestionnaireAgent\",\n `Process definition not found for ID: ${processId} during 'goBack'`,\n {},\n sessionId,\n );\n return {\n sessionId,\n isCompleted: false,\n error: `Critical Error: Process ID \"${processId}\" not defined.`,\n state: stateCopy,\n };\n }\n stateCopy.currentQuestions = processDef.questions;\n\n if (stateCopy.questionHistory.length === 0) {\n this.logger(\n \"WARN\",\n \"QuestionnaireAgent\",\n \"Question history is empty. Cannot go back further. Restarting.\",\n {},\n sessionId,\n );\n return this.restart(sessionId, processId);\n }\n\n stateCopy.questionHistory.pop();\n\n if (stateCopy.questionHistory.length === 0) {\n this.logger(\n \"INFO\",\n \"QuestionnaireAgent\",\n \"Went back to before the first question. Presenting first question.\",\n {},\n sessionId,\n );\n stateCopy.answers = {};\n stateCopy.currentStep = 0;\n return this.nextStep(stateCopy.sessionId, processId, null, stateCopy);\n }\n\n const questionToReaskFromHistory = stateCopy.questionHistory.pop(); // Pop to re-ask this one\n if (!questionToReaskFromHistory) {\n this.logger(\"ERROR\", \"QuestionnaireAgent\", \"History inconsistency during 'goBack'.\", {}, sessionId);\n return this.restart(sessionId, processId);\n }\n stateCopy.isCompleted = false;\n return this.nextStep(stateCopy.sessionId, processId, null, stateCopy); // null for lastAnswer\n }\n\n public async restart(sessionId: string, processId: ProcessDefinitionId): Promise {\n const effectiveSessionId = sessionId || `S-${Utils.generateShortId()}`;\n this.logger(\"AUDIT\", \"QuestionnaireAgent\", `Restarting questionnaire for process ${processId}.`, {\n oldSessionId: sessionId,\n effectiveSessionId,\n }, effectiveSessionId);\n const processDef = getProcessDefinition(processId);\n if (!processDef) {\n this.logger(\n \"CRITICAL\",\n \"QuestionnaireAgent\",\n `Process definition ${processId} not found for restart.`,\n {},\n effectiveSessionId,\n );\n return {\n sessionId: effectiveSessionId,\n isCompleted: false,\n error: `Process ID ${processId} not found.`,\n state: null,\n };\n }\n\n const initialState: QuestionnaireState = {\n sessionId: effectiveSessionId,\n processId: processId,\n currentQuestions: processDef.questions,\n answers: {},\n questionHistory: [],\n isCompleted: false,\n totalSteps: processDef.questions.length,\n currentStep: 0,\n };\n return this.nextStep(initialState.sessionId, processId, null, initialState);\n }\n\n private finalizeDataForOrchestrator(\n answers: Record,\n questions: ProcessQuestion[],\n processId: ProcessDefinitionId,\n sessionId?: string,\n ): OrchestratorInput {\n const processedAnswers = { ...answers };\n questions.forEach(qDef => {\n if (qDef.optional && (processedAnswers[qDef.key] == null || String(processedAnswers[qDef.key]).trim() === \"\")) {\n delete processedAnswers[qDef.key];\n }\n });\n\n const mappedData: Record = {};\n for (const questionKey in processedAnswers) {\n if (Object.prototype.hasOwnProperty.call(processedAnswers, questionKey)) {\n const questionDef = questions.find(q => q.key === questionKey);\n if (questionDef) {\n mappedData[questionDef.mapsTo] = processedAnswers[questionKey];\n } else {\n this.logger(\n \"WARN\",\n \"QuestionnaireAgent.finalizeData\",\n `Answer provided for Q.key \"${questionKey}\" but no Q.def found. Data omitted.`,\n { processId },\n sessionId,\n );\n }\n }\n }\n this.logger(\"AUDIT\", \"QuestionnaireAgent.finalizeData\", `Data finalized and mapped for ${processId}.`, {\n originalAnswerKeys: Object.keys(answers).length,\n processedAnswerKeys: Object.keys(processedAnswers).length,\n mappedDataKeys: Object.keys(mappedData).length,\n }, sessionId);\n return { processId: processId, data: mappedData };\n }\n}",[162],"dba9fdbe-36c2-11f0-9ca6-9e149126039e","process_definitions.ts","d1157824-36c2-11f0-b03f-9e149126039e",18,["D",1747887098531],"// src/process_definitions.ts\nimport { ProcessDefinitionId } from \"./constants_and_ids\";\nimport {\n ExecutiveBriefingPointsOut,\n IncidentImpactAssessmentOut,\n InputValidationError,\n ProcessDefinition,\n StakeholderCommunicationDraftOut,\n} from \"./types\";\nimport { Utils } from \"./utils\";\n\n// Note: The full definitions are very long. I'll include one and placeholders for others.\n// The actual content would be copied from the original script.\n\nconst AETHERIA_PROCESS_DEFINITIONS: ProcessDefinition[] = [\n // 1. Assess Incident Impact & Generate Initial Internal Report\n {\n id: \"assess_incident_impact\",\n title: \"Incident Impact Assessment\",\n objective:\n \"Rapidly assess a critical incident's scope, impact, and current status to form a baseline internal understanding.\",\n uiColorClass: \"border-red-600\",\n questions: [\n {\n key: \"q_incidentType\",\n promptText:\n \"Describe the nature of the incident (e.g., System Outage, Security Breach, Data Loss, Service Degradation).\",\n displayName: \"Incident Type\",\n type: \"text\",\n mapsTo: \"incidentType\",\n placeholder: \"e.g., Unplanned database downtime\",\n },\n {\n key: \"q_detectedAt\",\n promptText: \"When was the incident first detected or reported (approximate date/time)?\",\n displayName: \"Detection Time\",\n type: \"text\",\n mapsTo: \"detectionTime\",\n placeholder: \"YYYY-MM-DD HH:MM UTC\",\n },\n {\n key: \"q_affectedServices\",\n promptText: \"Which specific services, systems, or products are directly affected?\",\n displayName: \"Affected Services\",\n type: \"textarea\",\n mapsTo: \"affectedServicesCritical\",\n placeholder: \"List services, e.g., API Gateway, Customer Portal, Payment Processing\",\n },\n {\n key: \"q_userImpact\",\n promptText: \"Describe the observed or anticipated impact on users/customers.\",\n displayName: \"User Impact\",\n type: \"textarea\",\n mapsTo: \"userImpactDescription\",\n placeholder: \"e.g., Users unable to log in, slow performance, incorrect data displayed\",\n },\n {\n key: \"q_dataSensitivity\",\n promptText: \"Is there any known or suspected exposure/compromise of sensitive data (PII, PHI, financial)?\",\n displayName: \"Data Sensitivity\",\n type: \"select\",\n mapsTo: \"dataExposureRisk\",\n options: [\n { label: \"Select Risk\", value: \"\" },\n { label: \"No Sensitive Data Involved\", value: \"NONE\" },\n { label: \"Low Sensitivity Data\", value: \"LOW\" },\n { label: \"Medium Sensitivity (Internal/Proprietary)\", value: \"MEDIUM\" },\n { label: \"High Sensitivity (Regulated/Confidential)\", value: \"HIGH\" },\n { label: \"Confirmed PII/PHI Exposure\", value: \"PII_PHI\" },\n ],\n },\n {\n key: \"q_currentActions\",\n promptText: \"What immediate actions have been taken or are in progress to contain/mitigate?\",\n displayName: \"Immediate Actions\",\n type: \"textarea\",\n mapsTo: \"mitigationActionsTaken\",\n placeholder: \"e.g., Rolled back deployment X, isolated affected servers, initiated investigation protocol Y\",\n },\n {\n key: \"q_incidentSeverity\",\n promptText: \"Initial assessment of incident severity?\",\n displayName: \"Severity\",\n type: \"select\",\n mapsTo: \"incidentSeverity\",\n options: [\n { label: \"Select Severity\", value: \"\" },\n { label: \"CRITICAL (Widespread, major impact)\", value: \"CRITICAL\" },\n { label: \"HIGH (Significant impact, contained)\", value: \"HIGH\" },\n { label: \"MEDIUM (Moderate impact, limited scope)\", value: \"MEDIUM\" },\n { label: \"LOW (Minor impact, easily recoverable)\", value: \"LOW\" },\n ],\n optional: true,\n },\n {\n key: \"q_outputTone_impact\",\n promptText: \"Desired AI output tone for the assessment summary? (Optional)\",\n displayName: \"Output Tone\",\n type: \"select\",\n optional: true,\n mapsTo: \"outputTone\",\n options: [{ label: \"-- Tone --\", value: \"\" }, { label: \"Direct & Factual\", value: \"direct_factual\" }, {\n label: \"Objective & Analytical\",\n value: \"objective_analytical\",\n }],\n },\n ],\n inputValidator: (data, questions) => {\n const errors: InputValidationError[] = [];\n const requiredFields: string[] = [\n \"incidentType\",\n \"detectionTime\",\n \"affectedServicesCritical\",\n \"userImpactDescription\",\n \"dataExposureRisk\",\n \"mitigationActionsTaken\",\n ];\n for (const field of requiredFields) {\n // Using mapsTo for data keys\n if (!data[field] || String(data[field]).trim() === \"\") {\n const q = questions.find(q => q.mapsTo === field);\n errors.push({\n fieldKey: q?.key || String(field),\n message: `${q?.displayName || field} is critical and must be provided.`,\n });\n }\n }\n return errors.length ? errors : true;\n },\n aiAgentConfiguration: {\n agentName: \"ImpactAssessorAI\",\n promptEngineer: (p: any) => ({ // p is BaseIncidentContext & specific fields from mapsTo\n systemPrompt: Utils.fillPromptTemplate(\n `You are an AI assistant specializing in summarizing critical incident information for internal technical and management teams. Your primary goal is to produce a structured, factual, and concise assessment based on the provided inputs. The output must be in JSON format. Adhere to the following structure:\n{\n \"incidentSummary\": \"string (Brief, 1-2 sentence overview of the incident)\",\n \"affectedSystems\": [\"string\"],\n \"potentialUserImpact\": \"string (Detailed description of how users are affected)\",\n \"dataSensitivity\": \"NONE\" | \"LOW\" | \"MEDIUM\" | \"HIGH\" | \"PII_PHI\",\n \"estimatedContainmentTime\": \"string (e.g., 'Within 2 hours', 'Uncertain, under investigation', '4-6 hours once root cause identified')\",\n \"keyKnownFacts\": [\"string (Bullet points of confirmed information)\"],\n \"keyUnknowns\": [\"string (Bullet points of critical information still needed)\"],\n \"immediateActionsTaken\": [\"string (Summary of actions already performed)\"],\n \"internalEscalationPath\": \"string (e.g., 'Incident Commander -> VP Engineering -> CTO')\",\n \"suggestedNextReviewTime\": \"string (e.g., 'In 30 minutes', 'In 1 hour', 'ASAP')\"\n}\nMaintain a {{outputTone}} tone. Confidentiality is paramount. Do not speculate beyond provided facts unless explicitly asked to identify unknowns.`,\n { outputTone: p.outputTone || \"direct_factual\" },\n ),\n userPrompt: `Incident Details:\n- Type: ${p.incidentType}\n- Detected: ${p.detectionTime}\n- Affected Services: ${p.affectedServicesCritical}\n- User Impact: ${p.userImpactDescription}\n- Data Sensitivity Risk: ${p.dataExposureRisk}\n- Actions Taken: ${p.mitigationActionsTaken}\n- Severity (Initial): ${p.incidentSeverity || \"Not specified, infer if possible or state as HIGH\"}\n\nBased on this information, generate the comprehensive incident impact assessment as per the JSON structure provided in the system prompt. Prioritize accuracy and clarity. For 'estimatedContainmentTime', provide a reasonable estimate or state if uncertain. For 'internalEscalationPath', assume a standard path if not specified (e.g., On-call -> Manager -> Director -> VP). For 'suggestedNextReviewTime', suggest an appropriate interval based on common incident response practices (e.g., 30-60 mins for active critical incidents).`,\n }),\n outputSchemaParser: (rawJson: string): IncidentImpactAssessmentOut => {\n const parsed = JSON.parse(rawJson);\n if (!parsed.incidentSummary || !parsed.affectedSystems || !parsed.potentialUserImpact) {\n throw new Error(\"Critical fields missing in AI-generated impact assessment.\");\n }\n return parsed as IncidentImpactAssessmentOut;\n },\n },\n },\n // ... (Definition for \"draft_stakeholder_communication\")\n // ... (Definition for \"generate_executive_briefing\")\n // Ensure these are fully copied from the original long script\n];\n\n/**\n * Retrieves a process definition by its ID.\n * @param id The ProcessDefinitionId of the desired process.\n * @returns The ProcessDefinition object, or undefined if not found.\n */\nexport function getProcessDefinition(id: ProcessDefinitionId): ProcessDefinition | undefined {\n return AETHERIA_PROCESS_DEFINITIONS.find(p => p.id === id);\n}",[169],"7ee632f0-36c2-11f0-8589-9e149126039e","orchestrator.ts","76cbe650-36c2-11f0-950c-9e149126039e",9,["D",1747886942900],"// src/orchestrator.ts\nimport { executeAIAgent } from \"./ai_agent_runner\";\nimport { AuditLogger } from \"./logger\";\nimport { getProcessDefinition } from \"./process_definitions\";\nimport { LogFunction, OrchestratorInput, SystemResponse, TaskInput } from \"./types\";\nimport { Utils } from \"./utils\";\n\nexport class CrisisWorkflowOrchestrator {\n private baseLogger: LogFunction;\n private masterIncidentId: string;\n private logger: LogFunction; // Instance-specific logger\n\n constructor(auditLoggerInstance: AuditLogger, masterIncidentId?: string) {\n // Use provided masterIncidentId or default from logger, or generate new\n this.masterIncidentId = masterIncidentId || auditLoggerInstance.getMasterIncidentIdContext()\n || `M-${Utils.generateShortId()}`;\n\n // Ensure the logger instance uses this orchestrator's masterIncidentId for its context\n auditLoggerInstance.setMasterIncidentIdContext(this.masterIncidentId);\n this.baseLogger = auditLoggerInstance.log.bind(auditLoggerInstance);\n\n this.logger = (level, component, message, details?, _masterIdOverride?, taskId?) => {\n this.baseLogger(level, component, message, details, this.masterIncidentId, taskId);\n };\n this.logger(\n \"AUDIT\",\n \"CrisisOrchestrator\",\n `New instance initialized for Master Incident ID: ${this.masterIncidentId}.`,\n );\n }\n\n public async initiateProcess(input: OrchestratorInput): Promise {\n this.logger(\"AUDIT\", \"CrisisOrchestrator\", `Initiating workflow for Process ID: ${input.processId}`, {\n inputDataKeys: Object.keys(input.data),\n });\n\n const processDefinition = getProcessDefinition(input.processId);\n if (!processDefinition) {\n const errorMessage = `FATAL: Process Definition ID \"${input.processId}\" not found. Workflow cannot proceed.`;\n this.logger(\"CRITICAL\", \"CrisisOrchestrator\", errorMessage, {\n availableProcessIds: [\n \"assess_incident_impact\",\n \"draft_stakeholder_communication\",\n \"generate_executive_briefing\",\n ],\n }); // Manually list or get from definitions\n return {\n masterIncidentId: this.masterIncidentId,\n error: errorMessage,\n diagnosticLog: [], // Logger instance will hold logs if passed externally\n operationStatus: \"ERROR\",\n };\n }\n\n const taskId = `${input.processId.substring(0, 10)}-${Utils.generateShortId(6)}`;\n const agentTaskInput: TaskInput = {\n masterIncidentId: this.masterIncidentId,\n taskId,\n payload: input.data,\n };\n\n try {\n const agentTaskOutput = await executeAIAgent(\n agentTaskInput,\n this.logger, // Pass the instance-bound logger\n processDefinition.aiAgentConfiguration,\n );\n\n if (agentTaskOutput.error) {\n this.logger(\n \"WARN\",\n \"CrisisOrchestrator\",\n `AI Agent for process ${input.processId} (Task ${taskId}) reported an error.`,\n { agentError: agentTaskOutput.error },\n );\n return {\n masterIncidentId: this.masterIncidentId,\n error: `Workflow Partially Failed: ${agentTaskOutput.error}`,\n result: agentTaskOutput.result,\n diagnosticLog: [],\n operationStatus: \"PARTIAL_SUCCESS\",\n };\n }\n\n this.logger(\n \"AUDIT\",\n \"CrisisOrchestrator\",\n `Workflow for Process ID ${input.processId} (Task ${taskId}) completed successfully.`,\n { outputKeys: Object.keys(agentTaskOutput.result) },\n );\n return {\n masterIncidentId: this.masterIncidentId,\n result: agentTaskOutput.result,\n diagnosticLog: [],\n operationStatus: \"SUCCESS\",\n };\n } catch (orchestrationError: any) {\n const criticalErrorMessage =\n `Workflow Critically Failed for Process ID ${input.processId} (Task ${taskId}): ${orchestrationError.message}`;\n this.logger(\"CRITICAL\", \"CrisisOrchestrator\", criticalErrorMessage, {\n errorStack: orchestrationError.stack?.substring(0, 500),\n });\n return {\n masterIncidentId: this.masterIncidentId,\n error: criticalErrorMessage,\n diagnosticLog: [],\n operationStatus: \"ERROR\",\n };\n }\n }\n}",[176],"ec4462f4-36c2-11f0-9f11-9e149126039e","openai_service.ts","e3fcc802-36c2-11f0-8c4a-9e149126039e",21,["D",1747887126389],"// src/openai_service.ts\nimport { OpenAI } from \"https://esm.town/v/std/openai\"; // Val Town specific import\nimport { LogFunction } from \"./types\";\n\n/**\n * Invokes the OpenAI Chat Completion API with specified prompts and parameters.\n * @param systemPrompt The system message to guide the AI's behavior.\n * @param userPrompt The user's message or query.\n * @param masterIncidentId For logging and traceability.\n * @param taskId Specific task identifier for logging.\n * @param logger A logging function instance.\n * @returns The AI's response content as a string, or null if no content.\n * @throws Error if the API call fails critically.\n */\nexport async function invokeOpenAIChatCompletion(\n systemPrompt: string,\n userPrompt: string,\n masterIncidentId: string,\n taskId: string,\n logger: LogFunction,\n): Promise {\n logger(\n \"AUDIT\",\n \"OpenAIService\",\n `Initiating LLM call for task ${taskId}`,\n { systemPromptLength: systemPrompt.length, userPromptLength: userPrompt.length },\n masterIncidentId,\n taskId,\n );\n\n try {\n const openAIClient = new OpenAI(); // API key should be configured via environment variables\n\n const completion = await openAIClient.chat.completions.create({\n model: \"gpt-4o-mini\", // Consider making this configurable\n messages: [\n { role: \"system\", content: systemPrompt },\n { role: \"user\", content: userPrompt + \"\\n\\nEnsure your entire response is a single JSON object as specified.\" },\n ],\n temperature: 0.5, // Lower for factual crisis comms\n response_format: { type: \"json_object\" },\n });\n\n const responseText = completion.choices[0]?.message?.content;\n const usageData = completion.usage;\n const finishReason = completion.choices[0]?.finish_reason;\n\n if (!responseText) {\n logger(\n \"WARN\",\n \"OpenAIService\",\n `LLM call for task ${taskId} yielded no content.`,\n { usageData, finishReason },\n masterIncidentId,\n taskId,\n );\n return null;\n }\n\n logger(\n \"INFO\",\n \"OpenAIService\",\n `LLM call for task ${taskId} successful.`,\n { responseLength: responseText.length, usageData, finishReason },\n masterIncidentId,\n taskId,\n );\n return responseText.trim();\n } catch (error: any) {\n const errorDetails = {\n errorMessage: error.message,\n errorName: error.name,\n errorStack: error.stack?.substring(0, 500),\n errorCode: error.code,\n errorParam: error.param,\n errorStatus: error.status,\n errorType: error.type,\n };\n logger(\n \"CRITICAL\",\n \"OpenAIService\",\n `LLM call for task ${taskId} failed: ${error.message}`,\n { error: errorDetails },\n masterIncidentId,\n taskId,\n );\n throw new Error(\n `OpenAI Service Failure: ${error.message}`\n + (error.code ? ` (Code: ${error.code}, Status: ${error.status})` : ` (Status: ${error.status})`),\n );\n }\n}",[183],"2bb2a45a-36c3-11f0-9460-9e149126039e","new-file-4409.tsx","2ad288c0-36c3-11f0-a66a-9e149126039e",32,["D",1747887232807],1,[190],"2bc6a914-36c3-11f0-9460-9e149126039e",["D",1747887232941],[27],"fb341b92-36c2-11f0-815d-9e149126039e","logger.ts","f5eab7a4-36c2-11f0-9f11-9e149126039e",24,["D",1747887151449],"// src/logger.ts\nimport { LogEntry, LogFunction, LogLevel } from \"./types\";\nimport { Utils } from \"./utils\";\n\nexport class AuditLogger {\n private logs: LogEntry[];\n private masterIncidentIdContext?: string;\n\n constructor(masterIncidentId?: string) {\n this.logs = [];\n // Ensure a master ID is always set, generate if not provided\n this.masterIncidentIdContext = masterIncidentId || `M-${Utils.generateShortId()}`;\n if (!masterIncidentId) {\n this.log(\"AUDIT\", \"AuditLogger\", `Implicitly generated Master Incident ID: ${this.masterIncidentIdContext}`);\n }\n }\n\n public log: LogFunction = (\n level: LogLevel,\n component: string,\n message: string,\n details?: any,\n masterIncidentIdOverride?: string,\n taskId?: string,\n ): void => {\n const effectiveMasterId = masterIncidentIdOverride || this.masterIncidentIdContext || \"M-UNKNOWN\";\n let serializableDetails = Utils.deepCloneWithCircularityHandling(details);\n\n const entry: LogEntry = {\n timestamp: new Date().toISOString(),\n masterIncidentId: effectiveMasterId,\n taskId: taskId || \"T-NONE\", // Default task ID if not provided\n level,\n component,\n message,\n details: serializableDetails,\n };\n this.logs.push(entry);\n\n const detailsString = serializableDetails !== undefined ? `| Details: ${JSON.stringify(serializableDetails)}` : \"\";\n console.log(\n `[${entry.timestamp}][${entry.masterIncidentId}][${entry.taskId}][${level}][${component}] ${message} ${detailsString}`,\n );\n };\n\n public getLogs(): LogEntry[] {\n return Utils.deepCloneWithCircularityHandling(this.logs);\n }\n\n public clearLogs(): void {\n this.logs = [];\n this.log(\"AUDIT\", \"AuditLogger\", \"Log cache cleared.\");\n }\n\n public setMasterIncidentIdContext(masterIncidentId: string): void {\n if (!masterIncidentId) {\n this.log(\"WARN\", \"AuditLogger\", \"Attempted to set an empty Master Incident ID context. Operation ignored.\");\n return;\n }\n this.masterIncidentIdContext = masterIncidentId;\n this.log(\"AUDIT\", \"AuditLogger\", `Master Incident ID context explicitly set to: ${masterIncidentId}`);\n }\n\n public getMasterIncidentIdContext(): string | undefined {\n return this.masterIncidentIdContext;\n }\n}",[200],"3246ec1e-36c2-11f0-8c4a-9e149126039e","index.ts",3,["D",1747886814348],"// src/main_handler.ts OR index.ts (entry point)\n\n// Val Town specific fetch (if not global)\n// import { fetch } from \"https://esm.town/v/std/fetch\";\nimport { z } from \"npm:zod\";\n\nimport { AuditLogger } from \"./logger\";\nimport { CrisisWorkflowOrchestrator } from \"./orchestrator\";\nimport { AETHERIA_PROCESS_DEFINITIONS, getProcessDefinition } from \"./process_definitions\"; // For UI data\nimport { QuestionnaireAgent } from \"./questionnaire_agent\";\nimport { OrchestratorInput, OrchestratorInputSchema, QuestionnaireResponse, SystemResponse } from \"./types\";\nimport { generateAetheriaUI } from \"./ui_generator\";\nimport { Utils } from \"./utils\";\n\n/**\n * Main HTTP request handler for the Aetheria ICRCO system.\n * This function routes requests to the appropriate handlers for UI,\n * questionnaire interactions, and AI process execution.\n *\n * @param request The incoming HTTP Request object.\n * @returns A Promise resolving to a Response object.\n */\nexport default async function handleRequest(request: Request): Promise {\n const requestId = `REQ-${Utils.generateShortId(10)}`;\n const requestLogger = new AuditLogger(requestId); // Logger for this request\n const log = requestLogger.log.bind(requestLogger);\n\n const url = new URL(request.url);\n const baseHeaders = {\n \"Content-Type\": \"application/json\",\n \"Access-Control-Allow-Origin\": \"*\", // Adjust for production\n \"Access-Control-Allow-Methods\": \"GET, POST, OPTIONS\",\n \"Access-Control-Allow-Headers\": \"Content-Type, Authorization, X-Aetheria-Request-ID\",\n \"X-Aetheria-Request-ID\": requestId,\n };\n\n log(\"AUDIT\", \"HTTPGateway\", `Received request: ${request.method} ${url.pathname}`, {\n headers: JSON.stringify(Object.fromEntries(request.headers)),\n });\n\n if (request.method === \"OPTIONS\") {\n log(\"INFO\", \"HTTPGateway\", \"Handling OPTIONS preflight request.\", {});\n return new Response(null, { status: 204, headers: baseHeaders });\n }\n\n try {\n // Serve UI\n if (request.method === \"GET\" && (url.pathname === \"/\" || url.pathname === \"\")) {\n log(\"INFO\", \"HTTPGateway\", \"Serving Aetheria UI.\", { path: url.pathname });\n const uiProcessDefinitions = AETHERIA_PROCESS_DEFINITIONS.map(p => ({\n id: p.id,\n title: p.title,\n objective: p.objective,\n uiColorClass: p.uiColorClass,\n questionCount: p.questions.length,\n }));\n const html = generateAetheriaUI(JSON.stringify(uiProcessDefinitions));\n return new Response(html, {\n status: 200,\n headers: { ...baseHeaders, \"Content-Type\": \"text/html;charset=utf-8\" },\n });\n }\n\n // Questionnaire Endpoint\n if (request.method === \"POST\" && url.pathname === \"/questionnaire\") {\n log(\"INFO\", \"HTTPGateway\", \"Processing POST to /questionnaire.\", { path: url.pathname });\n let requestBody;\n let rawRequestBody = \"[NoBodyRead]\";\n try {\n rawRequestBody = await request.text(); // Read as text first for logging\n requestBody = JSON.parse(rawRequestBody);\n // Basic validation for action and processId\n if (\n !requestBody?.processId || typeof requestBody.processId !== \"string\" || !requestBody.action\n || ![\"start\", \"answer\", \"back\", \"restart\"].includes(requestBody.action)\n ) {\n throw new Error(\"Invalid questionnaire request structure: missing/invalid action or processId.\");\n }\n if ((requestBody.action === \"answer\" || requestBody.action === \"back\") && !requestBody.state) {\n throw new Error(`Invalid questionnaire request: 'state' is required for action '${requestBody.action}'.`);\n }\n if (requestBody.action === \"answer\" && !requestBody.lastAnswer) {\n throw new Error(\"Invalid questionnaire request: 'lastAnswer' is required for action 'answer'.\");\n }\n } catch (e: any) {\n log(\"WARN\", \"HTTPGateway\", \"Bad request to /questionnaire (invalid JSON or structure).\", {\n error: e.message,\n bodyPreview: rawRequestBody.substring(0, 250),\n });\n return new Response(\n JSON.stringify({\n error: `Invalid Request: ${e.message}`,\n state: null,\n diagnosticLog: requestLogger.getLogs(),\n }),\n { status: 400, headers: baseHeaders },\n );\n }\n\n const questionnaireAgent = new QuestionnaireAgent(log);\n let qResponse: QuestionnaireResponse;\n try {\n log(\"AUDIT\", \"HTTPGateway\", `Questionnaire action: ${requestBody.action}`, {\n processId: requestBody.processId,\n sessionId: requestBody.sessionId,\n lastAnswerField: requestBody.lastAnswer?.fieldKey,\n });\n\n // Type assertion after validation for state and lastAnswer where needed\n switch (requestBody.action) {\n case \"start\":\n qResponse = await questionnaireAgent.nextStep(\n requestBody.sessionId,\n requestBody.processId,\n requestBody.lastAnswer,\n null,\n );\n break;\n case \"answer\":\n qResponse = await questionnaireAgent.nextStep(\n requestBody.sessionId,\n requestBody.processId,\n requestBody.lastAnswer,\n requestBody.state,\n );\n break;\n case \"back\":\n qResponse = await questionnaireAgent.goBack(\n requestBody.sessionId,\n requestBody.processId,\n requestBody.state,\n );\n break;\n case \"restart\":\n qResponse = await questionnaireAgent.restart(requestBody.sessionId, requestBody.processId);\n break;\n default: // Should not be reached due to prior validation\n qResponse = {\n sessionId: requestBody.sessionId || `ERR-${Utils.generateShortId()}`,\n isCompleted: false,\n error: \"Invalid action specified.\",\n state: requestBody.state || null,\n };\n }\n log(\"INFO\", \"HTTPGateway\", \"Questionnaire step processed.\", {\n sessionId: qResponse.sessionId,\n isDone: qResponse.isCompleted,\n hasError: !!qResponse.error,\n hasFieldError: !!qResponse.fieldError,\n nextQKey: qResponse.nextQuestion?.fieldKey,\n });\n return new Response(JSON.stringify(qResponse), { status: 200, headers: baseHeaders });\n } catch (qError: any) {\n log(\"CRITICAL\", \"HTTPGateway\", `System error during questionnaire processing action '${requestBody.action}'.`, {\n error: qError.message,\n stack: qError.stack?.substring(0, 300),\n });\n return new Response(\n JSON.stringify({\n error: `Internal Questionnaire System Error: ${qError.message}`,\n state: requestBody.state || null,\n diagnosticLog: requestLogger.getLogs(),\n masterIncidentId: requestId,\n }),\n { status: 500, headers: baseHeaders },\n );\n }\n }\n\n // Execution Endpoint\n if (request.method === \"POST\" && url.pathname === \"/execute\") {\n log(\"INFO\", \"HTTPGateway\", \"Processing POST to /execute for AI agent execution.\", { path: url.pathname });\n let validatedInput: OrchestratorInput;\n let rawPayload = \"[NoPayloadRead]\";\n try {\n const payload = await request.json();\n rawPayload = JSON.stringify(payload).substring(0, 500);\n log(\"AUDIT\", \"HTTPGateway\", \"Received payload for /execute.\", { payloadPreview: rawPayload });\n\n // Validate processId exists before full Zod parse\n const processDefExists = getProcessDefinition(payload.processId);\n if (!processDefExists) {\n throw new z.ZodError([{\n path: [\"processId\"],\n message: `Process ID '${payload.processId}' not found or is invalid.`,\n code: z.ZodIssueCode.custom,\n }]);\n }\n validatedInput = OrchestratorInputSchema.parse(payload);\n log(\"INFO\", \"HTTPGateway\", \"Payload for /execute validated successfully.\", {\n processId: validatedInput.processId,\n dataKeys: Object.keys(validatedInput.data),\n });\n } catch (e: any) {\n let errorDetail = \"Invalid input structure or data.\";\n if (e instanceof z.ZodError) {\n errorDetail = e.issues.map(issue => `${issue.path.join(\".\") || \"input\"}: ${issue.message}`).join(\"; \");\n } else {\n errorDetail = e.message;\n }\n log(\"WARN\", \"HTTPGateway\", \"Validation failed for /execute payload.\", {\n error: errorDetail,\n payloadPreview: rawPayload,\n });\n return new Response(\n JSON.stringify({\n error: \"Invalid Input Data.\",\n details: errorDetail,\n diagnosticLog: requestLogger.getLogs(),\n masterIncidentId: `M-VALIDATION-${requestId}`,\n }),\n { status: 400, headers: baseHeaders },\n );\n }\n\n const orchestratorLogger = new AuditLogger(requestId); // Orchestrator uses this request's ID as its MasterIncidentID\n const orchestrator = new CrisisWorkflowOrchestrator(orchestratorLogger);\n const finalSystemResponse: SystemResponse = await orchestrator.initiateProcess(validatedInput);\n\n finalSystemResponse.diagnosticLog = orchestratorLogger.getLogs();\n\n // Server-side render example (if a resultRenderer is defined)\n const processDef = getProcessDefinition(validatedInput.processId);\n if (processDef?.aiAgentConfiguration.resultRenderer && finalSystemResponse.result && !finalSystemResponse.error) {\n try {\n finalSystemResponse.details = processDef.aiAgentConfiguration.resultRenderer(\n finalSystemResponse.result,\n Utils.escapeHTML,\n );\n } catch (renderError: any) {\n log(\"WARN\", \"HTTPGateway\", `Server-side result rendering failed for process ${validatedInput.processId}.`, {\n error: renderError.message,\n });\n // Optionally add this detail to the response if helpful for debugging\n // finalSystemResponse.details = (finalSystemResponse.details || \"\") + ` [RenderError: ${renderError.message}]`;\n }\n }\n\n const responseStatus = finalSystemResponse.error\n ? (finalSystemResponse.error.includes(\"AI output was unparseable\") ? 422 : 500)\n : 200;\n log(\"AUDIT\", \"HTTPGateway\", `/execute workflow finished with status ${responseStatus}.`, {\n masterIncidentId: finalSystemResponse.masterIncidentId,\n hasError: !!finalSystemResponse.error,\n operationStatus: finalSystemResponse.operationStatus,\n });\n return new Response(JSON.stringify(finalSystemResponse), { status: responseStatus, headers: baseHeaders });\n }\n\n log(\"WARN\", \"HTTPGateway\", `Unsupported route or method: ${request.method} ${url.pathname}.`, {});\n return new Response(\n JSON.stringify({\n error: \"Not Found. The requested resource or method is not available on this server.\",\n diagnosticLog: requestLogger.getLogs(),\n masterIncidentId: requestId,\n }),\n { status: 404, headers: baseHeaders },\n );\n } catch (criticalError: any) {\n log(\"CRITICAL\", \"HTTPGateway\", \"Unhandled critical error at HTTP Gateway.\", {\n error: criticalError.message,\n stack: criticalError.stack?.substring(0, 500),\n });\n return new Response(\n JSON.stringify({\n error: \"Internal Server Error.\",\n details: \"An unexpected critical error occurred. The technical team has been notified.\",\n diagnosticLog: requestLogger.getLogs(),\n masterIncidentId: `M-CRITICAL-${requestId}`,\n }),\n { status: 500, headers: baseHeaders },\n );\n }\n}\n\n// Example of how this might be exported or run in a Val Town like environment:\nexport default handleRequest;",[207],"9288ce30-36c2-11f0-a058-9e149126039e","ai_agent_runner.ts","8a87167e-36c2-11f0-b034-9e149126039e",12,["D",1747886975843],"// src/ai_agent_runner.ts\nimport { invokeOpenAIChatCompletion } from \"./openai_service\";\nimport { BaseIncidentContext, LogFunction, ProcessDefinition, TaskInput, TaskOutput } from \"./types\";\n\n/**\n * Executes a defined AI agent based on the provided task input and agent configuration.\n * @param taskInput Contains masterIncidentId, taskId, and payload for the AI.\n * @param logger A logging function instance.\n * @param agentConfig The configuration for the AI agent, including prompt engineering and parsing.\n * @returns A promise resolving to the TaskOutput from the AI agent.\n */\nexport async function executeAIAgent<\n T_Context extends BaseIncidentContext,\n T_Output extends Record,\n>(\n taskInput: TaskInput,\n logger: LogFunction,\n agentConfig: ProcessDefinition[\"aiAgentConfiguration\"],\n): Promise> {\n const { masterIncidentId, taskId, payload } = taskInput;\n const { agentName, promptEngineer, outputSchemaParser, outputPostProcessor } = agentConfig;\n\n logger(\n \"AUDIT\",\n agentName,\n `Executing agent for task ${taskId}.`,\n { inputKeys: Object.keys(payload) },\n masterIncidentId,\n taskId,\n );\n\n try {\n const { systemPrompt, userPrompt } = promptEngineer(payload);\n if (!systemPrompt || !userPrompt) {\n const msg = `Prompt generation failed for task ${taskId}. System or user prompt is empty.`;\n logger(\n \"CRITICAL\",\n agentName,\n msg,\n { systemPromptExists: !!systemPrompt, userPromptExists: !!userPrompt },\n masterIncidentId,\n taskId,\n );\n return { masterIncidentId, correlationId: taskId, result: {} as T_Output, error: `${agentName} Error: ${msg}` };\n }\n\n const rawOpenAIResult = await invokeOpenAIChatCompletion(\n systemPrompt,\n userPrompt,\n masterIncidentId,\n taskId,\n logger,\n );\n if (!rawOpenAIResult) {\n const msg = `LLM returned no content for task ${taskId}.`;\n logger(\"WARN\", agentName, msg, {}, masterIncidentId, taskId);\n return { masterIncidentId, correlationId: taskId, result: {} as T_Output, error: `${agentName} Error: ${msg}` };\n }\n\n let parsedOutput: T_Output;\n try {\n parsedOutput = outputSchemaParser(rawOpenAIResult);\n } catch (parseError: any) {\n const msg = `Failed to parse LLM output for task ${taskId}. Error: ${parseError.message}`;\n logger(\n \"ERROR\",\n agentName,\n msg,\n { rawResponsePreview: rawOpenAIResult.substring(0, 500) },\n masterIncidentId,\n taskId,\n );\n return { masterIncidentId, correlationId: taskId, result: {} as T_Output, error: `${agentName} Error: ${msg}` };\n }\n\n if (outputPostProcessor) {\n try {\n parsedOutput = outputPostProcessor(parsedOutput, payload);\n logger(\n \"INFO\",\n agentName,\n `Output post-processor applied successfully for task ${taskId}.`,\n {},\n masterIncidentId,\n taskId,\n );\n } catch (postProcessError: any) {\n // Log warning but proceed with pre-processed data\n logger(\n \"WARN\",\n agentName,\n `Output post-processor failed for task ${taskId}. Error: ${postProcessError.message}. 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Diff · v33