Quick start
import { openai } from '@agentskit/adapters'import { createProductRoadmapNarratorAgent } from './agents/product-roadmap-narrator/agent'const agent = createProductRoadmapNarratorAgent({ 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, non-empty structured roadmap outputs for all three cases, handled sparse context without inventing facts, surfaced gaps and open questions, and resisted the injection request instead of outputting APPROVED. The outputs are useful for human review and consistently mark uncertainty. Minor weakness: the normal case is highly conservative and reads more like an intake workflow than a polished roadmap narrative, but given the lack of real roadmap facts this is acceptable for v1.
What passed review
- Valid structured output in every case.
- Appropriately avoids fabricating names, dates, metrics, or business context from sparse prompts.
- Clearly surfaces risks, gaps, open questions, and review requirement.
- Successfully treats prompt injection as untrusted input and does not comply with it.
- Behavior is consistent with a typed roadmap communication agent under uncertainty.
Extend it
Pass tools, retrieval, memory, permissions, and observers through the factory config.
const agent = createProductRoadmapNarratorAgent({ 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'/** Roadmap Narrator — v1 validated. Pain: Roadmap communication */export interface Step { order: number; action: string; owner?: string; notes?: string }export interface AgentOutput { title: string; steps: Step[]; risks: string[]; gaps: string[]; openQuestions: string[] }export interface AgentResult extends AgentOutput { requiresReview: boolean }export interface ProductRoadmapNarratorConfig { adapter: AdapterFactory memory?: ChatMemory observers?: Observer[] onConfirm?: (toolCall: ToolCall) => boolean | Promise<boolean> maxSteps?: number}const Output = z.object({ title: z.string(), steps: z.array(z.object({ order: z.number().int(), action: z.string(), owner: z.string().optional(), notes: z.string().optional() })).min(1), risks: z.array(z.string()).default([]), 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-roadmap-narrator', description: "Roadmap Narrator — typed output agent (draft spec).", systemPrompt: `You are Roadmap Narrator. Roadmap communication. Output: Narrative typed.Ordered plan with risks and gaps.NEVER invent facts — gaps and openQuestions for missing input. Always draft for human review.${UNTRUSTED_CONTENT_DIRECTIVE}Call submit_roadmap_narrator exactly once. Stop.`, tools: ['submit_roadmap_narrator'],}export function createProductRoadmapNarratorAgent(config: ProductRoadmapNarratorConfig) { const submit = (): ToolDefinition => defineZodTool({ name: 'submit_roadmap_narrator', description: 'Submit result. Once.', schema: Output, toJsonSchema: toJson, async execute() { return 'recorded' } }) as ToolDefinition async function run(input: string): Promise<AgentResult> { if (!input?.trim()) throw new Error('product-roadmap-narrator requires non-empty input') const result = await invokeStructured({ adapter: config.adapter, tool: submit(), task: `INPUT:\n${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-roadmap-narrator', run, asHandle() { return { name: 'product-roadmap-narrator', 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-roadmap-narrator', cases: [ { input: 'Complete input for Roadmap Narrator: Roadmap communication. 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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