,

,
,
,
, 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 app.fetch;","deleted","label",[98],"permalink","https://www.val.town/x/real/Testbrd/code/main.tsx","extension","actionData","errors"]