[{"_1":2,"_3":-5,"_4":-5},"loaderData",{"_5":6,"_7":8,"_9":10},"actionData","errors","root",{"_152":153,"_154":155},"routes/_app.x.$handle.$name",{"_11":12,"_13":14,"_15":16,"_18":19,"_17":-7,"_115":-5,"_20":116,"_22":117,"_118":119,"_120":121,"_25":122,"_24":-5,"_123":-5,"_124":-5,"_125":53,"_126":127,"_128":44,"_129":-5,"_130":-5,"_131":132},"routes/_app.x.$handle.$name.branch.$branch.code.$",{"_11":12,"_13":14,"_15":16,"_17":-7,"_18":19,"_20":21,"_22":23,"_24":-7,"_25":26,"_27":-5,"_28":-5,"_29":-5,"_30":-5,"_31":-5},"handle","legal","name","untitled-4158","branchName","main","version","path","","project",{"_33":61,"_13":14,"_45":62,"_63":-5,"_64":53,"_65":66,"_67":66,"_68":-5,"_69":-5,"_70":71,"_72":37,"_73":44,"_74":44,"_75":-5,"_76":77,"_78":79,"_80":-5,"_81":-5,"_82":-5,"_83":-5,"_84":85,"_86":85,"_87":88,"_89":88,"_90":88,"_91":85,"_92":85,"_93":94,"_95":96,"_97":98,"_99":53},"tree",[56],"file","contents",[32],"upstream","pull","ahead","behind","envVars",{"_33":34,"_13":35,"_36":37,"_38":-5,"_39":40,"_41":42,"_43":44,"_45":46,"_17":47,"_48":49,"_50":51,"_52":53,"_54":-5,"_18":55},"id","82fa63ec-4c6c-11f0-8cb6-76b3cceeab13","main.tsx","fileId","73926874-4c6b-11f0-a273-76b3cceeab13","parentId","branchId","7390918e-4c6b-11f0-a273-76b3cceeab13","branchVersion",5,"latest",true,"createdAt",["D",1750268938602],4,"fileType","http","content","import { OpenAI } from \"https://esm.town/v/std/openai\";\n\n// --- Agent Communication Protocol (ACP) & Types --- //\n\ntype ACPStatus = \"SUCCESS\" | \"FAILURE\" | \"NEEDS_HUMAN_INPUT\";\n\ninterface ACP {\n metadata: {\n source_step_id: string;\n timestamp: string;\n status: ACPStatus;\n trace_id: string;\n };\n payload: {\n content_type: string;\n content: any;\n };\n error?: {\n code: string;\n message: string;\n };\n feedback?: {\n request_prompt: string;\n notes: string;\n };\n}\n\n// --- Agent & Tool Definitions (Simulated & Real) --- //\n\nconst TRACE_ID = crypto.randomUUID();\n\n/**\n * Utility to create a standardized ACP object.\n */\nfunction createAcp(\n source_step_id: string,\n status: ACPStatus,\n content: any,\n contentType: string = \"application/json\",\n): ACP {\n return {\n metadata: {\n source_step_id,\n timestamp: new Date().toISOString(),\n status,\n trace_id: TRACE_ID,\n },\n payload: {\n content_type: contentType,\n content,\n },\n };\n}\n\n/**\n * Simulated Tool: Searches application logs for errors.\n */\nasync function logSearchTool(serviceName: string, timeWindow: string): Promise {\n console.log(`TOOL: Searching logs for service \"${serviceName}\" in the last ${timeWindow}...`);\n await new Promise(res => setTimeout(res, 250)); // Simulate network delay\n return {\n log_excerpts: [\n `[${new Date().toISOString()}] ERROR: Connection to database 'pg-primary-01' timed out.`,\n `[${new Date().toISOString()}] WARN: High memory pressure detected on service 'api-gateway'.`,\n // FIXED: Was 'new E()', now 'new Date()'\n `[${new Date().toISOString()}] ERROR: Failed to acquire lock 'payment_processing_lock'.`,\n ],\n };\n}\n\n/**\n * Simulated Tool: Checks the current status of a system component.\n */\nasync function systemStatusTool(serviceName: string): Promise {\n console.log(`TOOL: Checking current status of \"${serviceName}\"...`);\n await new Promise(res => setTimeout(res, 250));\n return {\n service: serviceName,\n cpu_usage: \"95%\",\n memory_usage: \"98%\",\n status: \"UNHEALTHY\",\n active_connections: 5000,\n };\n}\n\n/**\n * Agent 1: Parses and normalizes the initial alert.\n */\nasync function triageAgent(inputAcp: ACP): Promise {\n console.log(\"AGENT: Triage Agent running...\");\n const alert = inputAcp.payload.content;\n const triagedInfo = {\n service_name: alert.service,\n severity: alert.severity.toUpperCase(),\n summary: alert.summary,\n affected_host: alert.host,\n };\n return createAcp(\"triage_agent\", \"SUCCESS\", triagedInfo);\n}\n\n/**\n * Agent 2: Gathers additional context using tools.\n */\nasync function dataEnrichmentAgent(inputAcp: ACP): Promise {\n console.log(\"AGENT: Data Enrichment Agent running...\");\n const { service_name } = inputAcp.payload.content;\n // Run tools in parallel to gather data\n const [logs, status] = await Promise.all([\n logSearchTool(service_name, \"15m\"),\n systemStatusTool(service_name),\n ]);\n const enrichedData = {\n ...inputAcp.payload.content,\n contextual_data: {\n logs,\n status,\n },\n };\n return createAcp(\"data_enrichment_agent\", \"SUCCESS\", enrichedData);\n}\n\n/**\n * Agent 3: Analyzes data and proposes a remediation plan. (REAL OpenAI Call)\n */\nasync function rootCauseAnalysisAgent(\n enrichedData: any,\n safetyFeedback?: string,\n): Promise {\n console.log(\"AGENT: Root Cause Analysis Agent running (calling OpenAI)...\");\n const openai = new OpenAI();\n\n let userMessage = `\n Analyze the following incident data to determine the likely root cause and create a step-by-step remediation plan.\n The output MUST be a JSON object with two keys: \"hypothesis\" (a string) and \"remediation_plan\" (an array of strings).\n Example plan step: \"kubectl scale deployment/api-gateway --replicas=3\"\n\n Incident Data:\n ${JSON.stringify(enrichedData, null, 2)}\n `;\n\n if (safetyFeedback) {\n userMessage +=\n `\\nIMPORTANT: A previous plan was rejected as unsafe. You MUST address this feedback: \"${safetyFeedback}\". Propose a new, safer plan.`;\n }\n\n try {\n const response = await openai.chat.completions.create({\n model: \"gpt-4o\",\n response_format: { \"type\": \"json_object\" },\n messages: [\n {\n role: \"system\",\n content:\n \"You are an expert Site Reliability Engineer (SRE). Your task is to analyze incident data, form a hypothesis, and create a safe, step-by-step remediation plan in JSON format. Do not include dangerous commands like 'reboot' unless absolutely necessary and explicitly mentioned in the context.\",\n },\n { role: \"user\", content: userMessage },\n ],\n temperature: 0.3,\n });\n const planJson = response.choices?.[0]?.message?.content;\n if (!planJson) throw new Error(\"OpenAI returned empty content.\");\n const plan = JSON.parse(planJson);\n return createAcp(\"root_cause_analysis_agent\", \"SUCCESS\", plan);\n } catch (e) {\n console.error(\"OpenAI API Error:\", e);\n return {\n ...createAcp(\"root_cause_analysis_agent\", \"FAILURE\", null),\n error: { code: \"LLM_FAILURE\", message: e.message },\n };\n }\n}\n\n/**\n * Agent 4: The Safety Gate. Checks the plan for dangerous operations.\n */\nasync function safetyCheckerAgent(inputAcp: ACP): Promise {\n console.log(`AGENT: Safety Checker Agent running...`);\n await new Promise(res => setTimeout(res, 200));\n const { remediation_plan } = inputAcp.payload.content;\n const dangerousCommands = [\"reboot\", \"rm -rf\", \"shutdown\", \"DROP DATABASE\"];\n\n const unsafeStep = remediation_plan.find(step => dangerousCommands.some(cmd => step.includes(cmd)));\n if (unsafeStep) {\n const feedback =\n `The proposed plan contains a potentially destructive command: \"${unsafeStep}\". Replace it with a less disruptive action, like a service restart or scaling event.`;\n return {\n ...createAcp(\"safety_checker_agent\", \"FAILURE\", remediation_plan, \"application/json\"),\n feedback: { request_prompt: \"Create safer plan\", notes: feedback },\n };\n }\n return createAcp(\"safety_checker_agent\", \"SUCCESS\", \"Safety check passed.\", \"text/plain\");\n}\n\n/**\n * Agent 5: Formats the final approved plan into an executable script.\n */\nasync function remediationFormatterAgent(inputAcp: ACP): Promise {\n console.log(\"AGENT: Remediation Formatter Agent running...\");\n const { remediation_plan } = inputAcp.payload.content;\n const script = `#!/bin/bash\n# Auto-generated remediation script\n# TRACE ID: ${TRACE_ID}\n\necho \"--- Starting automated remediation ---\"\n\n# Approved Steps:\n${remediation_plan.map(step => `echo \"Executing: ${step}\"\\n${step}\\necho \"Step completed.\"\\nsleep 2`).join(\"\\n\\n\")}\n\necho \"--- Remediation complete ---\"\n`;\n return createAcp(\"remediation_formatter_agent\", \"SUCCESS\", script, \"application/x-sh\");\n}\n\n// --- Workflow Orchestrator --- //\n\nasync function runIncidentResponseWorkflow(\n alertPayload: object,\n safetyFeedback?: string,\n) {\n const log = [];\n\n // Step 1: Triage Alert\n const initialAcp = createAcp(\"start\", \"SUCCESS\", alertPayload);\n const triageResult = await triageAgent(initialAcp);\n log.push(\"Triage Agent: Alert parsed and normalized.\");\n\n // Step 2: Enrich Data\n const enrichmentResult = await dataEnrichmentAgent(triageResult);\n log.push(`Enrichment Agent: Fetched logs and status for ${triageResult.payload.content.service_name}.`);\n const enrichedData = enrichmentResult.payload.content;\n\n // Step 3: Propose Remediation Plan\n const planResult = await rootCauseAnalysisAgent(\n enrichedData,\n safetyFeedback, // Pass safety feedback if it exists\n );\n if (planResult.metadata.status === \"FAILURE\") return { log, finalResult: planResult };\n log.push(\"RCA Agent: Generated root cause hypothesis and remediation plan.\");\n\n // Step 4: Safety Gate\n const safetyResult = await safetyCheckerAgent(planResult);\n if (safetyResult.metadata.status === \"FAILURE\") {\n log.push(\"Safety Gate: FAILED. Remediation plan is unsafe. Rerouting for revision...\");\n // RECURSIVE CALL to re-run the analysis loop with feedback\n return runIncidentResponseWorkflow(alertPayload, safetyResult.feedback.notes);\n }\n log.push(\"Safety Gate: PASSED.\");\n\n // Step 5: Needs Human Input (SRE Approval)\n const planForApproval = planResult.payload.content;\n const humanStepResult = createAcp(\"orchestrator\", \"NEEDS_HUMAN_INPUT\", planForApproval, \"application/json\");\n log.push(\"Workflow paused. Awaiting SRE approval for the proposed plan.\");\n\n return { log, finalResult: humanStepResult };\n}\n\n// --- HTML Front-End --- //\n\nfunction generateHtmlShell() {\n return `\n\n\n\n\n\nAutomated Incident Response Workflow\n\n\n\n

Automated Incident Response

Submit a monitoring alert (JSON) to begin the response workflow.

\n \n \n

SRE Approval Required

Please review the proposed remediation plan. The workflow is paused.

\n \n \n

\n \n

Workflow Complete: Remediation Script Generated

\n\n\n`;\n}\n\n// --- Main HTTP Request Handler --- //\n\nexport default async function(req: Request) {\n const url = new URL(req.url);\n const action = url.searchParams.get(\"action\");\n\n // Router for different actions\n if (req.method === \"POST\" && action === \"startWorkflow\") {\n try {\n const { alertPayload } = await req.json();\n if (!alertPayload) throw new Error(\"alertPayload is required.\");\n const { log, finalResult } = await runIncidentResponseWorkflow(alertPayload);\n return new Response(JSON.stringify({ log, finalResult }), {\n status: 200,\n headers: { \"Content-Type\": \"application/json\" },\n });\n } catch (e) {\n return new Response(JSON.stringify({ error: e.message }), {\n status: 500,\n headers: { \"Content-Type\": \"application/json\" },\n });\n }\n }\n\n if (req.method === \"POST\" && action === \"continueWorkflow\") {\n try {\n const { decision, plan, feedback, alertPayload } = await req.json();\n let log = [];\n let finalResult;\n\n if (decision === \"approved\") {\n const approvedAcp = createAcp(\"human_approval\", \"SUCCESS\", plan);\n finalResult = await remediationFormatterAgent(approvedAcp);\n } else {\n // Rerun the analysis -> safety loop with the new human feedback\n // FIXED: The original alertPayload is now correctly passed from the client\n if (!alertPayload) throw new Error(\"Original alertPayload is required for revisions.\");\n const revisionResponse = await runIncidentResponseWorkflow(alertPayload, feedback);\n log = revisionResponse.log;\n finalResult = revisionResponse.finalResult;\n }\n\n return new Response(JSON.stringify({ log, finalResult }), {\n status: 200,\n headers: { \"Content-Type\": \"application/json\" },\n });\n } catch (e) {\n return new Response(JSON.stringify({ error: e.message }), {\n status: 500,\n headers: { \"Content-Type\": \"application/json\" },\n });\n }\n }\n\n // Default GET request serves the HTML UI\n return new Response(generateHtmlShell(), {\n status: 200,\n headers: { 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Code