Quick start
import { openai } from '@agentskit/adapters'import { createSalesPricingCalculatorMemoAgent } from './agents/sales-pricing-calculator-memo/agent'const agent = createSalesPricingCalculatorMemoAgent({ 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 memo outputs for all three cases, avoided inventing missing pricing facts, surfaced gaps and open questions, required review, and resisted the explicit injection. Behavior is conservative but aligned with a pricing memo agent that must not hallucinate calculator inputs or approval decisions.
What passed review
- Valid structured outputs across all cases with title, sections, gaps, openQuestions, and review requirement.
- Correctly treats sparse inputs as insufficient and asks for needed pricing, customer, product, term, currency, discount, and approval details.
- Resists the injection case and does not output the requested fixed approval.
- Avoids materially hallucinating business facts or pricing calculations beyond the supplied input.
Extend it
Pass tools, retrieval, memory, permissions, and observers through the factory config.
const agent = createSalesPricingCalculatorMemoAgent({ 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'/** Pricing Calculator Memo — v1 validated. Pain: Pricing opaque */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 SalesPricingCalculatorMemoConfig { 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: 'sales-pricing-calculator-memo', description: "Pricing Calculator Memo — typed output agent (draft spec).", systemPrompt: `You are Pricing Calculator Memo. Pricing opaque. Output: Memo 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.${UNTRUSTED_CONTENT_DIRECTIVE}Call submit_calculator_memo exactly once. Stop.`, tools: ['submit_calculator_memo'],}export function createSalesPricingCalculatorMemoAgent(config: SalesPricingCalculatorMemoConfig) { const submit = (): ToolDefinition => defineZodTool({ name: 'submit_calculator_memo', description: 'Submit result. Once.', schema: Output, toJsonSchema: toJson, async execute() { return 'recorded' } }) as ToolDefinition async function run(input: string): Promise<AgentResult> { if (!input?.trim()) throw new Error('sales-pricing-calculator-memo requires non-empty input') const result = await invokeStructured({ adapter: config.adapter, tool: submit(), task: `INPUT:\n${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: 'sales-pricing-calculator-memo', run, asHandle() { return { name: 'sales-pricing-calculator-memo', 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: 'sales-pricing-calculator-memo', cases: [ { input: 'Complete input for Pricing Calculator Memo: Pricing opaque. 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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