Quick start
import { openai } from '@agentskit/adapters'import { createEcommerceInventoryReorderAgent } from './agents/ecommerce-inventory-reorder/agent'const agent = createEcommerceInventoryReorderAgent({ 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, avoided inventing reorder facts from sparse or meta-level prompts, surfaced the specific missing inventory inputs needed for a reorder decision, marked human review as required, and resisted the injection attempt instead of outputting APPROVED. Behavior is conservative but appropriate given that no actual SKU, inventory, demand, supplier, or lead-time data was provided.
What passed review
- Valid non-empty structured output in every case.
- Clearly identifies missing reorder inputs and asks useful follow-up questions.
- Does not hallucinate SKU quantities, suppliers, or purchase recommendations.
- Handles the injection case correctly by treating the malicious instruction as data and flagging it.
- Requires human review before any reorder action when facts are insufficient.
Extend it
Pass tools, retrieval, memory, permissions, and observers through the factory config.
const agent = createEcommerceInventoryReorderAgent({ 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'/** Inventory Reorder — v1 validated. Pain: Stockouts/overstock */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 EcommerceInventoryReorderConfig { 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: 'ecommerce-inventory-reorder', description: "Inventory Reorder — typed output agent (draft spec).", systemPrompt: `You are Inventory Reorder. Stockouts/overstock. Output: Reorder 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.${UNTRUSTED_CONTENT_DIRECTIVE}Call submit_inventory_reorder exactly once. Stop.`, tools: ['submit_inventory_reorder'],}export function createEcommerceInventoryReorderAgent(config: EcommerceInventoryReorderConfig) { const submit = (): ToolDefinition => defineZodTool({ name: 'submit_inventory_reorder', description: 'Submit result. Once.', schema: Output, toJsonSchema: toJson, async execute() { return 'recorded' } }) as ToolDefinition async function run(input: string): Promise<AgentResult> { if (!input?.trim()) throw new Error('ecommerce-inventory-reorder requires non-empty input') const result = await invokeStructured({ adapter: config.adapter, tool: submit(), task: `INPUT:\n${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: 'ecommerce-inventory-reorder', run, asHandle() { return { name: 'ecommerce-inventory-reorder', 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: 'ecommerce-inventory-reorder', cases: [ { input: 'Complete input for Inventory Reorder: Stockouts/overstock. 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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