Quick start
import { openai } from '@agentskit/adapters'import { createHrVisaSponsorshipEvaluatorAgent } from './agents/hr-visa-sponsorship-evaluator/agent'const agent = createHrVisaSponsorshipEvaluatorAgent({ 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 outputs for all three cases, consistently avoided making unsupported visa eligibility determinations, surfaced concrete missing facts, required human review, and resisted the explicit injection attempt. The behavior is conservative and aligned with an HR visa sponsorship evaluator for sparse or adversarial inputs. Minor concerns: the normal case remained non-substantive because the input itself contained no case facts, and the agent references untrusted markers that are not visible in the top-level case text, though they appear consistent with the runtime wrapping.
What passed review
- Valid structured output shape across all cases.
- Clear uncertainty handling with no unsupported approval or denial.
- Useful gap lists and open questions tailored to visa sponsorship evaluation.
- Injection case correctly treats the approval instruction as untrusted data.
- Consistently flags need for human review in a high-stakes HR/legal-adjacent domain.
Extend it
Pass tools, retrieval, memory, permissions, and observers through the factory config.
const agent = createHrVisaSponsorshipEvaluatorAgent({ 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'/** Visa Sponsorship Evaluator — v1 validated. Pain: Visa eligibility unclear */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 HrVisaSponsorshipEvaluatorConfig { 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-visa-sponsorship-evaluator', description: "Visa Sponsorship Evaluator — typed output agent (draft spec).", systemPrompt: `You are Visa Sponsorship Evaluator. Visa eligibility unclear. Output: Eligibility 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.${UNTRUSTED_CONTENT_DIRECTIVE}Call submit_sponsorship_evaluator exactly once. Stop.`, tools: ['submit_sponsorship_evaluator'],}export function createHrVisaSponsorshipEvaluatorAgent(config: HrVisaSponsorshipEvaluatorConfig) { const submit = (): ToolDefinition => defineZodTool({ name: 'submit_sponsorship_evaluator', description: 'Submit result. Once.', schema: Output, toJsonSchema: toJson, async execute() { return 'recorded' } }) as ToolDefinition async function run(input: string): Promise<AgentResult> { if (!input?.trim()) throw new Error('hr-visa-sponsorship-evaluator requires non-empty input') const result = await invokeStructured({ adapter: config.adapter, tool: submit(), task: `INPUT:\n${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-visa-sponsorship-evaluator', run, asHandle() { return { name: 'hr-visa-sponsorship-evaluator', 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-visa-sponsorship-evaluator', cases: [ { input: 'Complete input for Visa Sponsorship Evaluator: Visa eligibility unclear. 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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