Quick start
import { openai } from '@agentskit/adapters'import { createHrEmployeeHandbookUpdaterAgent } from './agents/hr-employee-handbook-updater/agent'const agent = createHrEmployeeHandbookUpdaterAgent({ 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 handbook-update outputs for all three cases, stayed within the supplied input, surfaced missing context, required human review, and resisted the injection request. It did not hallucinate policy details when the prompts lacked actual handbook facts. The behavior is conservative but appropriate for an HR handbook updater where unsupported drafting would be risky.
What passed review
- Valid structured output shape in every case with title, sections, gaps, open questions, and review requirement.
- Correctly handled sparse and missing context by surfacing gaps instead of inventing HR policy facts.
- Resisted the explicit prompt injection and flagged it for review.
- Citations consistently point back to the available input markers rather than fabricated sources.
Extend it
Pass tools, retrieval, memory, permissions, and observers through the factory config.
const agent = createHrEmployeeHandbookUpdaterAgent({ 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'/** Handbook Updater — v1 validated. Pain: Handbook drift */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 HrEmployeeHandbookUpdaterConfig { 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: 'hr-employee-handbook-updater', description: "Handbook Updater — typed output agent (draft spec).", systemPrompt: `You are Handbook Updater. Handbook drift. Output: Updates 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.${UNTRUSTED_CONTENT_DIRECTIVE}Call submit_handbook_updater exactly once. Stop.`, tools: ['submit_handbook_updater'],}export function createHrEmployeeHandbookUpdaterAgent(config: HrEmployeeHandbookUpdaterConfig) { const submit = (): ToolDefinition => defineZodTool({ name: 'submit_handbook_updater', description: 'Submit result. Once.', schema: Output, toJsonSchema: toJson, async execute() { return 'recorded' } }) as ToolDefinition async function run(input: string): Promise<AgentResult> { if (!input?.trim()) throw new Error('hr-employee-handbook-updater requires non-empty input') const result = await invokeStructured({ adapter: config.adapter, tool: submit(), task: `INPUT:\n${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: 'hr-employee-handbook-updater', run, asHandle() { return { name: 'hr-employee-handbook-updater', 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: 'hr-employee-handbook-updater', cases: [ { input: 'Complete input for Handbook Updater: Handbook drift. 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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