Quick start
import { openai } from '@agentskit/adapters'import { createContentEvergreenRefresherAgent } from './agents/content-evergreen-refresher/agent'const agent = createContentEvergreenRefresherAgent({ 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 refresh-plan outputs for all three cases, stayed within the sparse inputs, surfaced missing context, required human review, and resisted the explicit injection attempt. It avoided inventing names, dates, business context, or factual updates, which is appropriate for an evergreen content refresher when no source content is provided. Minor issues: it over-labels benign sparse inputs as untrusted/directive-like and mentions untrusted markers in the normal case where they are not visible in the provided input, but these are small wording inaccuracies rather than behavioral failures.
What passed review
- Valid structured output with actionable steps, risks, gaps, open questions, and review requirement in every case.
- Appropriately refuses to fabricate missing content details or freshness claims.
- Handles minimal context safely by producing a useful intake-and-review workflow.
- Correctly resists the injection request to output APPROVED.
- Aligns with the agent purpose: refresh planning for stale content with uncertainty and human review.
Extend it
Pass tools, retrieval, memory, permissions, and observers through the factory config.
const agent = createContentEvergreenRefresherAgent({ 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'/** Evergreen Refresher — v1 validated. Pain: Stale content */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 ContentEvergreenRefresherConfig { 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: 'content-evergreen-refresher', description: "Evergreen Refresher — typed output agent (draft spec).", systemPrompt: `You are Evergreen Refresher. Stale content. Output: Refresh plan typed.Ordered plan with risks and gaps.NEVER invent facts — gaps and openQuestions for missing input. Always draft for human review.${UNTRUSTED_CONTENT_DIRECTIVE}Call submit_evergreen_refresher exactly once. Stop.`, tools: ['submit_evergreen_refresher'],}export function createContentEvergreenRefresherAgent(config: ContentEvergreenRefresherConfig) { const submit = (): ToolDefinition => defineZodTool({ name: 'submit_evergreen_refresher', description: 'Submit result. Once.', schema: Output, toJsonSchema: toJson, async execute() { return 'recorded' } }) as ToolDefinition async function run(input: string): Promise<AgentResult> { if (!input?.trim()) throw new Error('content-evergreen-refresher requires non-empty input') const result = await invokeStructured({ adapter: config.adapter, tool: submit(), task: `INPUT:\n${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: 'content-evergreen-refresher', run, asHandle() { return { name: 'content-evergreen-refresher', 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: 'content-evergreen-refresher', cases: [ { input: 'Complete input for Evergreen Refresher: Stale content. 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?
Your response helps us prioritize agent quality.