Agent Create

Scaffold a new agent: $ARGUMENTS

Before Starting

1. Search OpenAI docs for the latest Agents SDK patterns:
   • Use mcp__openai-docs__search_openai_docs with query "Agents SDK agent definition" to confirm current Agent constructor and run() patterns
   • Use mcp__openai-docs__search_openai_docs with query "Agents SDK tools" for tool definition patterns
2. Read existing agent definitions to understand project conventions:
   • src/agents/chat-coaches/index.ts — simple agent (tenant-driven prompt, single model)
   • Read 1-2 files in the ai-lab-agent repo at /Users/joshuashepherd/Desktop/Dev/repos/ai-lab-agent/src/agents/ai-lab/index.ts for the function-based config pattern (instructions/tools/modelSettings as functions receiving RunContext)
   • /Users/joshuashepherd/Desktop/Dev/repos/movemental-dashboard/src/agents/writing-assistant/index.ts — multi-mode agent with per-mode temperature/token configs
   • /Users/joshuashepherd/Desktop/Dev/repos/movemental-dashboard/src/agents/seo-expert/index.ts — tool-heavy agent with ordered tool execution
3. Read the shared infrastructure:
   • src/agents/shared/enhanced-agent-bridge.ts — bridge class that wraps agent execution
   • src/agents/shared/types.ts — ChatKitRequest, ChatKitEvent, WritingContext types

Scaffold Steps

Step 1: Agent Definition

Create src/agents/<name>/index.ts:

import { Agent } from '@openai/agents';

export const <name>Agent = new Agent({
  name: '<Display Name>',
  instructions: '<system prompt — or import from instructions.ts>',
  model: '<model-id>',  // gpt-4o, gpt-4o-mini, etc.
  tools: [],  // or import from tools.ts
  modelSettings: {
    temperature: 0.7,
    maxTokens: 2000,
  },
});

For complex agents that need runtime adaptation, use the function-based pattern from ai-lab-agent:

export const <name>Agent = new Agent({
  name: '<Display Name>',
  instructions: (context: RunContext<MyContext>) => generateInstructions(context),
  tools: (context: RunContext<MyContext>) => getToolsForAgent(context),
  modelSettings: (context: RunContext<MyContext>) => getModelSettings(context),
});

Step 2: Instructions File (if complex)

Create src/agents/<name>/instructions.ts with composable instruction sections. Follow the pattern in ai-lab-agent where instructions are split into identity, context, and mode sections.

Step 3: Tools File (if needed)

Create src/agents/<name>/tools.ts using the tool() helper from @openai/agents with Zod parameter schemas. See the agent-tool skill for detailed tool patterns.

Step 4: API Route

Create src/app/api/agents/<name>/route.ts:

import { NextRequest } from 'next/server';
import { createServerClient } from '@/lib/supabase/server';
import { createEnhancedAgentBridge } from '@/agents/shared/enhanced-agent-bridge';
import { nonEmpty, maxLength, contentFilter } from '@/agents/shared/guardrails';
import { noErrors } from '@/agents/shared/guardrails';
import { defaultDynamicInstructions } from '@/agents/shared/dynamic-instructions';
import { <name>Agent } from '@/agents/<name>';

export const maxDuration = 60;

export async function POST(req: NextRequest) {
  const supabase = await createServerClient();
  const { data: { user } } = await supabase.auth.getUser();
  if (!user) return new Response('Unauthorized', { status: 401 });

  const bridge = createEnhancedAgentBridge(<name>Agent, {
    agentId: '<name>',
    inputGuardrails: [nonEmpty(), maxLength(10000), contentFilter()],
    outputGuardrails: [nonEmpty(), noErrors()],
    dynamicInstructions: defaultDynamicInstructions,
    enableHandoffs: false,
    tracing: { workflowName: '<name>-agent' },
  });

  return bridge.handleRequest(req);
}

Step 5: Register in Agent Registry (if handoffs needed)

Add to src/agents/shared/handoffs.ts:

import { <name>Agent } from '@/agents/<name>';
agentRegistry.register('<name>', <name>Agent);

Step 6: Export from Index

Create or update src/agents/index.ts to export the new agent.

Rules

• Use tenantConfig for any tenant-specific strings in the system prompt — never hardcode
• Agent instructions should reference the thought leader's voice and content when relevant
• Always wire through EnhancedAgentBridge for guardrails, tracing, and metrics
• Use streaming (SSE) responses via the bridge — never return plain JSON for chat
• Follow the Result pattern in any service calls within tools
• Check OpenAI docs MCP for any SDK changes before generating agent code