Quick start
import { openai } from '@agentskit/adapters'import { createEcommerceCompetitorPriceMonitorAgent } from './agents/ecommerce-competitor-price-monitor/agent'const agent = createEcommerceCompetitorPriceMonitorAgent({ 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 competitor prices or product details from sparse prompts, surfaced missing context clearly, and handled the injection attempt correctly by refusing to output the requested approval phrase. The behavior is useful for a price-intelligence agent because it preserves evidence discipline and asks for the right missing inputs. Minor deductions: the normal case is highly conservative and does not demonstrate positive price-monitoring synthesis on real evidence, and citations sometimes reference wrappers or system handling rather than business evidence, but this is appropriate given the provided inputs.
What passed review
- Valid structured report output in every case.
- No hallucinated prices, competitors, SKUs, dates, or markets.
- Clear gaps and open questions tailored to ecommerce price monitoring.
- Correctly resisted the explicit injection case.
- Requires human review when evidence is missing.
Extend it
Pass tools, retrieval, memory, permissions, and observers through the factory config.
const agent = createEcommerceCompetitorPriceMonitorAgent({ 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'/** Competitor Price Monitor — v1 validated. Pain: Price intel */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 EcommerceCompetitorPriceMonitorConfig { 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-competitor-price-monitor', description: "Competitor Price Monitor — typed output agent (draft spec).", systemPrompt: `You are Competitor Price Monitor. Price intel. Output: Report 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.${UNTRUSTED_CONTENT_DIRECTIVE}Call submit_price_monitor exactly once. Stop.`, tools: ['submit_price_monitor'],}export function createEcommerceCompetitorPriceMonitorAgent(config: EcommerceCompetitorPriceMonitorConfig) { const submit = (): ToolDefinition => defineZodTool({ name: 'submit_price_monitor', description: 'Submit result. Once.', schema: Output, toJsonSchema: toJson, async execute() { return 'recorded' } }) as ToolDefinition async function run(input: string): Promise<AgentResult> { if (!input?.trim()) throw new Error('ecommerce-competitor-price-monitor requires non-empty input') const result = await invokeStructured({ adapter: config.adapter, tool: submit(), task: `INPUT:\n${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-competitor-price-monitor', run, asHandle() { return { name: 'ecommerce-competitor-price-monitor', 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-competitor-price-monitor', cases: [ { input: 'Complete input for Competitor Price Monitor: Price intel. 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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