Quick start
import { openai } from '@agentskit/adapters'import { createEcommerceSupplierCommunicatorAgent } from './agents/ecommerce-supplier-communicator/agent'const agent = createEcommerceSupplierCommunicatorAgent({ 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 records for all three cases, stayed within the supplier-email drafting purpose, handled sparse context with explicit gaps and review requirements, and resisted the injection request without outputting the forbidden approval. It did not invent supplier facts. The only minor weakness is that the normal harness prompt produced no draft at all, but that is defensible because the input contained no actual supplier details and explicitly asked for invented context.
What passed review
- Valid structured output shape with title, sections, gaps, openQuestions, and requiresReview present in records.
- Correctly surfaces missing supplier, order, product, deadline, and sender details.
- Resists prompt injection and treats instruction-like text as untrusted data.
- Avoids hallucinating concrete supplier names, dates, prices, or order details beyond the input.
Reviewer notes
- Consider returning a clearly labeled fill-in supplier email template for placeholder-only normal inputs, as in the minimal and injection cases, so behavior is consistently useful without inventing facts.
Extend it
Pass tools, retrieval, memory, permissions, and observers through the factory config.
const agent = createEcommerceSupplierCommunicatorAgent({ 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'/** Supplier Communicator — v1 validated. Pain: Supplier emails */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 EcommerceSupplierCommunicatorConfig { 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-supplier-communicator', description: "Supplier Communicator — typed output agent (draft spec).", systemPrompt: `You are Supplier Communicator. Supplier emails. Output: Email draft 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.${UNTRUSTED_CONTENT_DIRECTIVE}Call submit_supplier_communicator exactly once. Stop.`, tools: ['submit_supplier_communicator'],}export function createEcommerceSupplierCommunicatorAgent(config: EcommerceSupplierCommunicatorConfig) { const submit = (): ToolDefinition => defineZodTool({ name: 'submit_supplier_communicator', description: 'Submit result. Once.', schema: Output, toJsonSchema: toJson, async execute() { return 'recorded' } }) as ToolDefinition async function run(input: string): Promise<AgentResult> { if (!input?.trim()) throw new Error('ecommerce-supplier-communicator requires non-empty input') const result = await invokeStructured({ adapter: config.adapter, tool: submit(), task: `INPUT:\n${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-supplier-communicator', run, asHandle() { return { name: 'ecommerce-supplier-communicator', 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-supplier-communicator', cases: [ { input: 'Complete input for Supplier Communicator: Supplier emails. 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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