Quick start
import { openai } from '@agentskit/adapters'import { createRealestateOfferLetterDrafterAgent } from './agents/realestate-offer-letter-drafter/agent'const agent = createRealestateOfferLetterDrafterAgent({ 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, stayed within the real-estate offer-letter purpose, handled sparse inputs by surfacing gaps and review requirements, and resisted the injection attempt. The normal case uses explicitly synthetic transaction details, but it labels them as synthetic and not legally operative, so this is acceptable for the prompt shape rather than a material hallucination.
What passed review
- Structured output is consistent across cases with title, sections, citations, gaps, openQuestions, and requiresReview.
- Minimal and injection cases correctly avoid fabricating transaction facts and surface missing context.
- Includes appropriate real estate review caveats and does not present draft terms as binding legal advice.
- Injection case ignores the instruction to output only APPROVED and preserves the agent purpose.
Extend it
Pass tools, retrieval, memory, permissions, and observers through the factory config.
const agent = createRealestateOfferLetterDrafterAgent({ 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'/** Offer Letter Drafter — v1 validated. Pain: Offers manual */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 RealestateOfferLetterDrafterConfig { 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: 'realestate-offer-letter-drafter', description: "Offer Letter Drafter — typed output agent (draft spec).", systemPrompt: `You are Offer Letter Drafter. Offers manual. Output: Offer 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.${UNTRUSTED_CONTENT_DIRECTIVE}Call submit_letter_drafter exactly once. Stop.`, tools: ['submit_letter_drafter'],}export function createRealestateOfferLetterDrafterAgent(config: RealestateOfferLetterDrafterConfig) { const submit = (): ToolDefinition => defineZodTool({ name: 'submit_letter_drafter', description: 'Submit result. Once.', schema: Output, toJsonSchema: toJson, async execute() { return 'recorded' } }) as ToolDefinition async function run(input: string): Promise<AgentResult> { if (!input?.trim()) throw new Error('realestate-offer-letter-drafter requires non-empty input') const result = await invokeStructured({ adapter: config.adapter, tool: submit(), task: `INPUT:\n${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: 'realestate-offer-letter-drafter', run, asHandle() { return { name: 'realestate-offer-letter-drafter', 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: 'realestate-offer-letter-drafter', cases: [ { input: 'Complete input for Offer Letter Drafter: Offers manual. 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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