[{"_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","real","name","Testbrd","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","7571aa78-7c14-11f0-8548-0224a6c84d84","main.tsx","fileId","ca2cc4e0-7c13-11f0-a961-0224a6c84d84","parentId","branchId","ca2a2a50-7c13-11f0-a961-0224a6c84d84","branchVersion",4,"latest",true,"createdAt",["D",1755508775992],3,"fileType","http","content","// @ts-ignore\nimport { Hono } from \"npm:hono@4.4.12\";\n// @ts-ignore\nimport { OpenAI } from \"https://esm.town/v/std/openai\";\nimport type { Context } from \"npm:hono@4.4.12\";\nimport { streamText } from \"npm:hono/streaming\";\n\n// --- TYPE DEFINITIONS ---\ninterface CompanyContext {\n company_name: string;\n company_description: string;\n use_case: string;\n language: string;\n}\ninterface ExecuteBody {\n refined_prompt: string;\n user_inputs: Record;\n clarifications: Record;\n company_context: CompanyContext;\n}\ninterface GenerateCriteriaBody {\n refined_prompt: string;\n company_context: CompanyContext;\n}\ninterface EvaluateBody {\n refined_prompt: string;\n criteria: { criterion: string; description: string }[];\n raw_output: string;\n language: string;\n}\ninterface RefineBody {\n refined_prompt: string;\n raw_output: string;\n evaluation: { scores: any[]; critique: string };\n language: string;\n}\n\n// --- AGENT SYSTEM PROMPTS ---\nconst PROMPT_REFINER_SYSTEM_PROMPT = `You are a specialized prompt engineer. Your function is to transmute raw occupational data into an elaborate, efficacious, and long-form directive for a target AI. You will be given an \"Occupation,\" \"Task,\" and \"Company Context.\" Your generated prompts must be comprehensive, detailed, and structured as a professional brief. CORE RULE: You MUST return a prompt that has {{variables}} in it. Your primary goal is to identify the most important, undefined entities within the user's Task description and convert them into descriptive, double-curly-braced {{placeholders}}. You MUST include at least 2 placeholder variables. INSTRUCTIONS: 1. Establish Persona: Begin with \"You are a top 0.1% [Occupation]...\". 2. Define Objective: Translate the given Task into a formal, actionable objective. 3. Integrate Company Context: Use the provided 'Company Context' to make the objective specific and relevant (e.g., \"Your primary mandate for [Company Name] is to...\"). 4. Insert Placeholders: Convert all abstract or undefined nouns into \\`{{snake_case_variables}}\\`. 5. Mandate a Starting Point: Conclude with a clear \"call to action\". Your entire response MUST be a single, minified JSON object containing exactly one key, \\`refined_prompt\\`.`;\nconst INPUT_EXTRACTOR_SYSTEM_PROMPT = `You are a strict parser. Your sole function is to analyze a prompt template and generate a JSON form schema. RULES: 1. REQUIRED FIELDS FROM PLACEHOLDERS ONLY: Find all \\`{{variable_name}}\\` placeholders. Each MUST be turned into a form field object with \\`\"required\": true\\`. Do NOT invent other required fields. 2. OPTIONAL FIELDS: You may add one or two optional fields for expert-level clarifying questions. These MUST have \\`\"required\": false\\`. 3. CONTEXTUAL HINTS: For every field, you MUST add a \\`placeholder\\` key with a concise, contextually-aware example. 4. FINAL OUTPUT: Your response MUST be a single, minified JSON object with one key, \\`\"inputs\"\\`, containing an array of field objects. Each field object must have 'name', 'label', 'type', 'required', and 'placeholder' keys.`;\nconst CLARIFICATION_AGENT_SYSTEM_PROMPT = `You are a world-class strategic consultant. Your function is to read a task briefing and generate 2 to 4 insightful, open-ended questions to elicit more nuanced context from the user, leading to a vastly superior final output. Focus on the \"why\" behind the task, not simple variables. GOOD QUESTIONS: \"What is the primary emotional response we want to evoke?\", \"What does a 'successful' outcome look like in 3 months?\". BAD QUESTIONS: \"What is the product name?\". Your entire response MUST be a single, minified JSON object with one key: \\`\"clarification_questions\"\\`. The value must be an array of form field objects, each with \"name\", \"label\", \"type\" (always \"textarea\"), \"required\" (always false), and \"placeholder\".`;\nconst DYNAMIC_LIST_GENERATOR_PROMPT = `You are a top 0.1% industry analyst. Your function is to generate a list of specific items based on a user-provided type ('occupations' or 'tasks') and company context. The list MUST reflect deep domain expertise. AVOID generic titles. Provide nuanced, specialized results relevant to the company's situation. For 'tasks', the name MUST be an action-oriented phrase starting with a verb. Your entire response MUST be a single, minified JSON object with one key, \\`generated_list\\`, containing an array of objects, each with an \\`id\\`, a \\`name\\`, and a \\`description\\`.`;\nconst HTML_FORMATTER_SYSTEM_PROMPT = `You are an expert in technical communication and semantic HTML. Your sole responsibility is to convert a given raw text document into a clean, well-structured, and semantically correct HTML snippet. MANDATES: 1. Use valid, semantic HTML tags (

,

,
,
,
, etc.). 2. Do NOT include \\`\\`, \\`\\`, or \\`\\` tags. 3. Do NOT include any CSS, \\`\n\n\n\n
\n
\n
Welcome to Agentive
\n
First, tell us about your context.
\n
\n
Company Name
\n
What does your company do?
\n
How will you use these AI agents?
\n
Output Language
\n
\n
\n
\n
\n\n
\n
\n
Agentive Testbed
\n
A workflow for generating, evaluating, and refining AI outputs.
\n
\n \n
\n
\n
Step 1: Provide Inputs
\n
Fill in the details for the AI agent to begin its task.
\n
\n
\n\n
\n
Step 2: Initial Draft (v1)
\n
\n
\n
The first draft is complete. How would you like to proceed?
\n
\n \n \n
\n
\n
\n\n
\n
Step 3: AI Evaluation
\n
\n
\n\n
\n
\n
Step 4: Refined Draft (v2)
\n
\n \n \n
\n
\n
\n \n
\n
\n
\n\n\n\n`;\n}\n\n// --- HONO APPLICATION SETUP ---\nconst app = new Hono();\n\napp.get(\"/\", (c: Context) => c.html(generateHtml(c.req.url)));\n\n// Main execution endpoint\napp.post(\"/api/execute-stream\", async (c: Context) => {\n const { refined_prompt, user_inputs, clarifications, company_context } = await c.req.json();\n if (!refined_prompt || !user_inputs || !company_context) return c.json({ error: \"Required fields missing\" }, 400);\n\n let finalUserPrompt = refined_prompt;\n for (const [key, value] of Object.entries(user_inputs)) {\n finalUserPrompt = finalUserPrompt.replace(new RegExp(`\\\\{\\\\{\\\\s${key}\\\\s\\\\}\\\\}`, \"g\"), String(value));\n }\n let strategicGuidance = clarifications && Object.keys(clarifications).length > 0\n ? `# USER'S STRATEGIC GUIDANCE\\n${Object.entries(clarifications).map(([q, a]) => `- Q: ${q}\\n - A: ${a}`).join(\"\\n\")}\\n---\\n`\n : \"\";\n\n const systemPromptWithContext =\n `# YOUR CONTEXT\n- Company Name: ${company_context.company_name}\n- Company Description: ${company_context.company_description}\n- Primary Goal: ${company_context.use_case}\n- Language for Output: ${company_context.language}\n---\n${strategicGuidance}# YOUR TASK BRIEFING\n${finalUserPrompt}\nYou MUST write your entire response in the requested language: ${company_context.language}.`;\n\n try {\n const openai = new OpenAI();\n const agentStream = await openai.chat.completions.create({\n model: \"gpt-4o\",\n messages: [{ role: \"system\", content: systemPromptWithContext }, { role: \"user\", content: \"Proceed with the task.\" }],\n stream: true,\n });\n return streamText(c, async (stream) => {\n for await (const part of agentStream) {\n await stream.write(part.choices[0]?.delta?.content || \"\");\n }\n });\n } catch (e) {\n return c.body(\"AI execution failed.\", 500);\n }\n});\n\n// --- API ROUTES FOR AGENTIVE LOOP ---\n\napp.post(\"/api/generate-criteria\", async (c: Context) => {\n const { refined_prompt, company_context } = await c.req.json();\n try {\n const openai = new OpenAI();\n const completion = await openai.chat.completions.create({\n model: \"gpt-4o-mini\",\n messages: [\n { role: \"system\", content: EVALUATION_CRITERIA_GENERATOR_PROMPT },\n {\n role: \"user\",\n content: `Company Context:\\n${JSON.stringify(company_context)}\\n\\nTask Briefing:\\n${refined_prompt}`,\n },\n ],\n response_format: { type: \"json_object\" },\n });\n return c.json(JSON.parse(completion.choices[0].message.content));\n } catch (e) {\n return c.json({ error: `Failed to generate criteria: ${e.message}` }, 500);\n }\n});\n\napp.post(\"/api/evaluate-output\", async (c: Context) => {\n const { refined_prompt, criteria, raw_output, language } = await c.req.json();\n try {\n const openai = new OpenAI();\n const stream = await openai.chat.completions.create({\n model: \"gpt-4o\",\n messages: [\n { role: \"system\", content: `${EVALUATOR_AGENT_PROMPT}\\n\\nOutput Language: ${language}` },\n {\n role: \"user\",\n content: `Task Briefing:\\n${refined_prompt}\\n\\nSuccess Criteria:\\n${JSON.stringify(criteria)}\\n\\nOutput to Evaluate:\\n${raw_output}`,\n },\n ],\n stream: true,\n });\n return streamText(c, async (s) => {\n for await (const part of stream) {\n await s.write(part.choices[0]?.delta?.content || \"\");\n }\n });\n } catch (e) {\n return c.body(`Evaluation failed: ${e.message}`, 500);\n }\n});\n\napp.post(\"/api/refine-output\", async (c: Context) => {\n const { refined_prompt, raw_output, evaluation, language } = await c.req.json();\n try {\n const openai = new OpenAI();\n const stream = await openai.chat.completions.create({\n model: \"gpt-4o\",\n messages: [\n { role: \"system\", content: `${REFINER_AGENT_PROMPT}\\n\\nOutput Language: ${language}` },\n {\n role: \"user\",\n content:\n `Original Task Briefing:\\n${refined_prompt}\\n\\nMy First Draft:\\n${raw_output}\\n\\nQA Critique:\\n${JSON.stringify(evaluation)}`,\n },\n ],\n stream: true,\n });\n return streamText(c, async (s) => {\n for await (const part of stream) {\n await s.write(part.choices[0]?.delta?.content || \"\");\n }\n });\n } catch (e) {\n return c.body(`Refinement failed: ${e.message}`, 500);\n }\n});\n\n// NOTE: The original, more complex dynamic list generation routes are omitted for this simplified demo,\n// but their logic would be added here in a full production version.\nexport default 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Code