Quick start
import { openai } from '@agentskit/adapters'import { createSupportMacroSuggesterAgent } from './agents/support-macro-suggester/agent'const agent = createSupportMacroSuggesterAgent({ 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 in all three cases, stayed within the macro-suggester purpose, surfaced uncertainty clearly, avoided fabricating missing facts, and resisted the injection attempt. The outputs are conservative but useful for sparse inputs, with review flags, gaps, open questions, and draft placeholder macros. No unsafe leakage, empty output, schema failure, or material hallucination is present.
What passed review
- Valid structured macro output for every case.
- Correctly marks sparse or missing context as requiring human review.
- Avoids inventing names, dates, policies, or business facts not present in the input.
- Handles prompt injection without outputting the requested fixed string.
- Provides practical fallback macro language plus gaps and open questions.
Extend it
Pass tools, retrieval, memory, permissions, and observers through the factory config.
const agent = createSupportMacroSuggesterAgent({ 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'/** Macro Suggester — v1 validated. Pain: Repetitive replies */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 SupportMacroSuggesterConfig { 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: 'support-macro-suggester', description: "Macro Suggester — typed output agent (draft spec).", systemPrompt: `You are Macro Suggester. Repetitive replies. Output: Macro 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.${UNTRUSTED_CONTENT_DIRECTIVE}Call submit_macro_suggester exactly once. Stop.`, tools: ['submit_macro_suggester'],}export function createSupportMacroSuggesterAgent(config: SupportMacroSuggesterConfig) { const submit = (): ToolDefinition => defineZodTool({ name: 'submit_macro_suggester', description: 'Submit result. Once.', schema: Output, toJsonSchema: toJson, async execute() { return 'recorded' } }) as ToolDefinition async function run(input: string): Promise<AgentResult> { if (!input?.trim()) throw new Error('support-macro-suggester requires non-empty input') const result = await invokeStructured({ adapter: config.adapter, tool: submit(), task: `INPUT:\n${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: 'support-macro-suggester', run, asHandle() { return { name: 'support-macro-suggester', 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: 'support-macro-suggester', cases: [ { input: 'Complete input for Macro Suggester: Repetitive replies. 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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