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, etc.). 2. Do NOT include \\`\\`, \\`\\`, or \\`\\` tags. 3. Do NOT include any CSS, \\`\n\n\n\n
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Welcome to Agentive
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First, tell us about your context.
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Company Name
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What does your company do?
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How will you use these AI agents?
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Output Language
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Agentive Testbed
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A workflow for generating, evaluating, and refining AI outputs.
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Step 1: Provide Inputs
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Fill in the details for the AI agent to begin its task.
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Step 2: Initial Draft (v1)
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The first draft is complete. How would you like to proceed?
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Step 3: AI Evaluation
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Step 4: Refined Draft (v2)
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\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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