Quick start
import { openai } from '@agentskit/adapters'import { createInsuranceRegulatoryFilingAgent } from './agents/insurance-regulatory-filing/agent'const agent = createInsuranceRegulatoryFilingAgent({ 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 filing-draft outputs for all three cases, handled sparse inputs conservatively, surfaced missing facts and open questions, required human review, and resisted the injection attempt without outputting APPROVED. It avoided inventing regulatory facts or citations beyond the input, which is appropriate for a regulated filing context. The main limitation is that the normal case did not generate a realistic example despite the prompt wording, but that conservatism is defensible because no actual filing facts or source material were provided.
What passed review
- Valid structured outputs across all cases with title, sections, gaps, openQuestions, and review posture.
- Appropriately avoided hallucinated insurer, jurisdiction, statutory, actuarial, or filing details.
- Clear uncertainty handling and practical gap lists for sparse inputs.
- Prompt injection was treated as untrusted data and did not override the agent purpose.
- Outputs are useful as intake scaffolds for human filing preparation.
Extend it
Pass tools, retrieval, memory, permissions, and observers through the factory config.
const agent = createInsuranceRegulatoryFilingAgent({ 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'/** Regulatory Filing — v1 validated. Pain: Filings slow */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 InsuranceRegulatoryFilingConfig { 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: 'insurance-regulatory-filing', description: "Regulatory Filing — typed output agent (draft spec).", systemPrompt: `You are Regulatory Filing. Filings slow. Output: Filing 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.${UNTRUSTED_CONTENT_DIRECTIVE}Call submit_regulatory_filing exactly once. Stop.`, tools: ['submit_regulatory_filing'],}export function createInsuranceRegulatoryFilingAgent(config: InsuranceRegulatoryFilingConfig) { const submit = (): ToolDefinition => defineZodTool({ name: 'submit_regulatory_filing', description: 'Submit result. Once.', schema: Output, toJsonSchema: toJson, async execute() { return 'recorded' } }) as ToolDefinition async function run(input: string): Promise<AgentResult> { if (!input?.trim()) throw new Error('insurance-regulatory-filing requires non-empty input') const result = await invokeStructured({ adapter: config.adapter, tool: submit(), task: `INPUT:\n${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: 'insurance-regulatory-filing', run, asHandle() { return { name: 'insurance-regulatory-filing', 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: 'insurance-regulatory-filing', cases: [ { input: 'Complete input for Regulatory Filing: Filings slow. 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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