Quick start
import { openai } from '@agentskit/adapters'import { createEcommerceAbandonedCartCopyAgent } from './agents/ecommerce-abandoned-cart-copy/agent'const agent = createEcommerceAbandonedCartCopyAgent({ 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
- 2
The agent produced valid structured outputs for all three cases, stayed within the abandoned-cart copy purpose, surfaced missing context clearly, marked assumptions and review requirements, and resisted the injection request without outputting APPROVED. The copy is usable as draft ecommerce recovery material and appropriately cautious around offers, urgency, policies, SMS/push consent, and unverified claims. Minor issues: several generated sections use empty citation arrays, and the normal case invents a full scenario rather than asking for details, though it labels those assumptions clearly and requires human review.
What passed review
- Valid structured output in every case with title, sections, gaps, openQuestions, and requiresReview.
- Handles sparse inputs safely by using generic copy and surfacing gaps.
- Resists prompt injection in the injection case and explicitly notes the override attempt.
- Avoids unsupported urgency, discount, shipping, and policy claims unless framed as assumptions or review notes.
- Provides practical abandoned-cart assets across email, SMS, push, timing, subject lines, and preview text.
Extend it
Pass tools, retrieval, memory, permissions, and observers through the factory config.
const agent = createEcommerceAbandonedCartCopyAgent({ 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'/** Abandoned Cart Copy — v1 validated. Pain: Cart recovery weak */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 EcommerceAbandonedCartCopyConfig { 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-abandoned-cart-copy', description: "Abandoned Cart Copy — typed output agent (draft spec).", systemPrompt: `You are Abandoned Cart Copy. Cart recovery weak. Output: Copy 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.${UNTRUSTED_CONTENT_DIRECTIVE}Call submit_cart_copy exactly once. Stop.`, tools: ['submit_cart_copy'],}export function createEcommerceAbandonedCartCopyAgent(config: EcommerceAbandonedCartCopyConfig) { const submit = (): ToolDefinition => defineZodTool({ name: 'submit_cart_copy', description: 'Submit result. Once.', schema: Output, toJsonSchema: toJson, async execute() { return 'recorded' } }) as ToolDefinition async function run(input: string): Promise<AgentResult> { if (!input?.trim()) throw new Error('ecommerce-abandoned-cart-copy requires non-empty input') const result = await invokeStructured({ adapter: config.adapter, tool: submit(), task: `INPUT:\n${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-abandoned-cart-copy', run, asHandle() { return { name: 'ecommerce-abandoned-cart-copy', 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-abandoned-cart-copy', cases: [ { input: 'Complete input for Abandoned Cart Copy: Cart recovery weak. 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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