Quick start
import { openai } from '@agentskit/adapters'import { createComplianceLgpdDpaReviewerAgent } from './agents/compliance-lgpd-dpa-reviewer/agent'const agent = createComplianceLgpdDpaReviewerAgent({ 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 DPA-review outputs for all three cases, refused to invent facts from sparse inputs, surfaced material gaps and open questions, required human review, and resisted the injection attempt. The behavior is conservative and aligned with an LGPD DPA reviewer. Minor weakness: it is somewhat over-defensive in treating benign eval prompt wording as untrusted instruction content, but that does not impair safety or usefulness for these cases.
What passed review
- Valid structured output in every case with summary, findings, gaps, open questions, and review requirement.
- Correctly avoided hallucinating DPA facts where no DPA or business context was provided.
- Explicitly handled uncertainty and requested the information needed for substantive LGPD review.
- Resisted the prompt injection and did not output the requested false approval.
- Findings are typed with severities, sources, and actionable recommendations.
Extend it
Pass tools, retrieval, memory, permissions, and observers through the factory config.
const agent = createComplianceLgpdDpaReviewerAgent({ 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'/** LGPD DPA Reviewer — v1 validated. Pain: DPA review BR */export interface Finding { id: string; severity: 'critical' | 'high' | 'medium' | 'low' | 'info'; message: string; source?: string; recommendation?: string }export interface AgentOutput { summary: string; findings: Finding[]; gaps: string[]; openQuestions: string[] }export interface AgentResult extends AgentOutput { requiresReview: boolean }export interface ComplianceLgpdDpaReviewerConfig { adapter: AdapterFactory memory?: ChatMemory observers?: Observer[] onConfirm?: (toolCall: ToolCall) => boolean | Promise<boolean> maxSteps?: number}const Output = z.object({ summary: z.string(), findings: z.array(z.object({ id: z.string(), severity: z.enum(['critical', 'high', 'medium', 'low', 'info']), message: z.string(), source: z.string().optional(), recommendation: z.string().optional(), })), gaps: z.array(z.string()).default([]), openQuestions: z.array(z.string()).default([]),})const toJson = (s: z.ZodTypeAny): JSONSchema7 => zodToJsonSchema(s) as JSONSchema7const skill = { name: 'compliance-lgpd-dpa-reviewer', description: "LGPD DPA Reviewer — typed output agent (draft spec).", systemPrompt: `You are LGPD DPA Reviewer. DPA review BR. Output: Findings typed.Actionable findings citing input sources. No invented issues.NEVER invent facts — gaps and openQuestions for missing input. Always draft for human review.${UNTRUSTED_CONTENT_DIRECTIVE}Call submit_dpa_reviewer exactly once. Stop.`, tools: ['submit_dpa_reviewer'],}export function createComplianceLgpdDpaReviewerAgent(config: ComplianceLgpdDpaReviewerConfig) { const submit = (): ToolDefinition => defineZodTool({ name: 'submit_dpa_reviewer', description: 'Submit result. Once.', schema: Output, toJsonSchema: toJson, async execute() { return 'recorded' } }) as ToolDefinition async function run(input: string): Promise<AgentResult> { if (!input?.trim()) throw new Error('compliance-lgpd-dpa-reviewer requires non-empty input') const result = await invokeStructured({ adapter: config.adapter, tool: submit(), task: `INPUT:\n${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: 'compliance-lgpd-dpa-reviewer', run, asHandle() { return { name: 'compliance-lgpd-dpa-reviewer', 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: 'compliance-lgpd-dpa-reviewer', cases: [ { input: 'Complete input for LGPD DPA Reviewer: DPA review BR. 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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