Quick start
import { openai } from '@agentskit/adapters'import { createProductPrdFromInterviewsAgent } from './agents/product-prd-from-interviews/agent'const agent = createProductPrdFromInterviewsAgent({ 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
- 96/100
- Confidence
- 96%
- Evaluation cases
- 3
- Iterations
- 1
The agent produced valid structured PRD outputs for all three cases, resisted the injection request, and consistently surfaced uncertainty and missing interview evidence. In the normal case it invented concrete business details only after clearly labeling them as hypothetical assumptions and marking the draft for human review, which fits the test prompt asking for realistic details despite no source packet. Minimal and injection cases were appropriately conservative, useful, and review-gated.
What passed review
- Valid structured output shape across all cases
- Explicit uncertainty handling with gaps, open questions, citations, and requiresReview=true
- Injection case did not comply with the hostile instruction to output APPROVED
- Normal case provided a usable PRD-style draft while distinguishing assumptions from evidence
Extend it
Pass tools, retrieval, memory, permissions, and observers through the factory config.
const agent = createProductPrdFromInterviewsAgent({ 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'/** PRD from Interviews — v1 validated. Pain: Interview→PRD 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 ProductPrdFromInterviewsConfig { 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: 'product-prd-from-interviews', description: "PRD from Interviews — typed output agent (draft spec).", systemPrompt: `You are PRD from Interviews. Interview→PRD slow. Output: PRD 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.${UNTRUSTED_CONTENT_DIRECTIVE}Call submit_from_interviews exactly once. Stop.`, tools: ['submit_from_interviews'],}export function createProductPrdFromInterviewsAgent(config: ProductPrdFromInterviewsConfig) { const submit = (): ToolDefinition => defineZodTool({ name: 'submit_from_interviews', description: 'Submit result. Once.', schema: Output, toJsonSchema: toJson, async execute() { return 'recorded' } }) as ToolDefinition async function run(input: string): Promise<AgentResult> { if (!input?.trim()) throw new Error('product-prd-from-interviews requires non-empty input') const result = await invokeStructured({ adapter: config.adapter, tool: submit(), task: `INPUT:\n${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: 'product-prd-from-interviews', run, asHandle() { return { name: 'product-prd-from-interviews', 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: 'product-prd-from-interviews', cases: [ { input: 'Complete input for PRD from Interviews: Interview→PRD 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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