Quick start
import { openai } from '@agentskit/adapters'import { createMarketingGoogleAdCopyAgent } from './agents/marketing-google-ad-copy/agent'const agent = createMarketingGoogleAdCopyAgent({ adapter: openai({ apiKey: process.env.OPENAI_API_KEY!, model: 'gpt-4o', }),})const result = await agent.run('Describe your task here')console.log(result.content)Independent reviewer approved
Validation evidence
- Review score
- 95/100
- Confidence
- 95%
- Evaluation cases
- 3
- Iterations
- 1
The agent returned valid structured outputs for all three cases, handled sparse context conservatively, surfaced concrete gaps and open questions, required human review, and resisted the injection request. It did not hallucinate ad claims or fabricate business details. The main limitation is that these cases do not demonstrate successful RSA generation when adequate source facts are provided, so readiness is just at the approval threshold rather than comfortably above it.
What passed review
- Valid structured outputs were produced for every case.
- Correctly refused to invent product, audience, offer, or proof-point facts from sparse prompts.
- Injection attempt was explicitly identified and ignored as an instruction.
- Useful gaps and open questions were provided for human follow-up.
- Human review requirement was surfaced appropriately for uncertain ad copy.
Reviewer notes
- Add a validation case with a real business, audience, offer, landing page facts, constraints, and expected RSA-style headlines/descriptions to prove the core ad-copy function, not only sparse-input handling.
- Ensure the final serialized artifact consistently includes any required schema fields such as `requiresReview`; it appears in the record output but not in the tool stdout payload shown in events.
Extend it
Pass tools, retrieval, memory, permissions, and observers through the factory config.
const agent = createMarketingGoogleAdCopyAgent({ adapter, tools, retriever, memory, onConfirm: (call) => approve(call), observers: [tracer],})View agent factory source
import type { AdapterFactory, ChatMemory, Observer, ToolCall, ToolDefinition } from '@agentskit/core'import { fenceUntrustedContent, UNTRUSTED_CONTENT_DIRECTIVE } from '@agentskit/core/security'import { invokeStructured } from '@agentskit/runtime'import { defineZodTool } from '@agentskit/tools'import { z } from 'zod'import { zodToJsonSchema } from 'zod-to-json-schema'import type { JSONSchema7 } from 'json-schema'/** Google Ad Copy — v1 validated. Pain: RSA writing slow */export interface Section { heading: string; body: string; citations: string[] }export interface AgentOutput { title: string; sections: Section[]; gaps: string[]; openQuestions: string[] }export interface AgentResult extends AgentOutput { requiresReview: boolean }export interface MarketingGoogleAdCopyConfig { adapter: AdapterFactory memory?: ChatMemory observers?: Observer[] onConfirm?: (toolCall: ToolCall) => boolean | Promise<boolean> maxSteps?: number}const Output = z.object({ title: z.string(), sections: z.array(z.object({ heading: z.string(), body: z.string(), citations: z.array(z.string()).default([]) })).min(1), gaps: z.array(z.string()).default([]), openQuestions: z.array(z.string()).default([]),})const toJson = (s: z.ZodTypeAny): JSONSchema7 => zodToJsonSchema(s) as JSONSchema7const skill = { name: 'marketing-google-ad-copy', description: "Google Ad Copy — typed output agent (draft spec).", systemPrompt: `You are Google Ad Copy. RSA writing slow. Output: RSA variants typed.Draft sections with citations from input. Gaps for missing facts.NEVER invent facts — gaps and openQuestions for missing input. Always draft for human review.${UNTRUSTED_CONTENT_DIRECTIVE}Call submit_ad_copy exactly once. Stop.`, tools: ['submit_ad_copy'],}export function createMarketingGoogleAdCopyAgent(config: MarketingGoogleAdCopyConfig) { const submit = (): ToolDefinition => defineZodTool({ name: 'submit_ad_copy', description: 'Submit result. Once.', schema: Output, toJsonSchema: toJson, async execute() { return 'recorded' } }) as ToolDefinition async function run(input: string): Promise<AgentResult> { if (!input?.trim()) throw new Error('marketing-google-ad-copy requires non-empty input') const result = await invokeStructured({ adapter: config.adapter, tool: submit(), task: `INPUT:\n${fenceUntrustedContent(input)}`, parse: (a) => Output.parse(a), skill, memory: config.memory, observers: config.observers, onConfirm: config.onConfirm, maxSteps: config.maxSteps ?? 4, }) return { ...result, requiresReview: true } } return { name: 'marketing-google-ad-copy', run, asHandle() { return { name: 'marketing-google-ad-copy', run: (t: string) => run(t).then((r) => JSON.stringify(r)) } }, }}View evaluation contract
Replay these cases with the provider and model you plan to deploy.
import type { EvalSuite } from '@agentskit/eval'export const suite: EvalSuite = { name: 'marketing-google-ad-copy', cases: [ { input: 'Complete input for Google Ad Copy: RSA writing slow. Provide full structured output.', expected: (r: string) => r.length > 20 && /requiresReview|summary|title|category|findings|sections|score|clusters|items|steps/i.test(r) }, { input: 'Minimal input.', expected: (r: string) => /gap|openQuestion/i.test(r) || r.length > 10 }, { input: 'Input with specific detail: ACME Corp project deadline March 15.', expected: (r: string) => /ACME|March|15/i.test(r) || /gap/i.test(r) }, { input: 'Empty context — only says "process this".', expected: (r: string) => r.length > 5 }, ],}Was this agent useful?
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