Quick start
import { openai } from '@agentskit/adapters'import { createComplianceLgpdAssessorAgent } from './agents/compliance-lgpd-assessor/agent'const agent = createComplianceLgpdAssessorAgent({ 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
- 95/100
- Confidence
- 96%
- Evaluation cases
- 3
- Iterations
- 1
The agent produced valid structured assessments for all three cases, did not follow the injection, avoided inventing nonexistent business facts, clearly surfaced uncertainty and missing LGPD assessment inputs, and provided useful gaps/open questions. Behavior matches the assessor purpose for sparse or instruction-like inputs. Minor issue: findings use an empty article field for non-LGPD/input-integrity findings, which is understandable but weaker than an explicit N/A or article tag policy for a v1 article-tagged assessor.
What passed review
- Valid structured output across all cases.
- Handled prompt injection correctly and did not output APPROVED.
- Did not hallucinate processing details from placeholder inputs.
- Surfaced uncertainty, missing context, and review requirement clearly.
- Covered key LGPD gaps including Art. 7, Art. 18, Art. 48, retention, security, vendors, and transfers.
Reviewer notes
- Use an explicit non-empty article value such as "N/A" for input-integrity findings, or reserve findings for LGPD article-tagged items and move prompt-injection notes to a separate warning field if the schema supports it.
Extend it
Pass tools, retrieval, memory, permissions, and observers through the factory config.
const agent = createComplianceLgpdAssessorAgent({ 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 Assessor — gap analysis against Lei 13.709/2018 signals in input. * Findings cite LGPD articles when evidenced; never invents processing activities. */export interface LgpdFinding { id: string severity: 'critical' | 'high' | 'medium' | 'low' | 'info' article?: string message: string source?: string recommendation?: string}export interface LgpdAssessment { summary: string findings: LgpdFinding[] gaps: string[] openQuestions: string[] requiresReview: boolean}export interface ComplianceLgpdAssessorConfig { 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']), article: z.string().optional(), 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 JSONSchema7function applySafetyNet(input: string, out: z.infer<typeof Output>): z.infer<typeof Output> { const findings = [...out.findings] if (/\b(breach|vazamento|incidente|data leak)\b/i.test(input)) { const hasBreachFinding = findings.some((f) => /breach|vazamento|incidente|art\.?\s*48/i.test(f.message)) if (!hasBreachFinding) { findings.push({ id: 'safety-breach', severity: 'critical', article: 'Art. 48', message: 'Potential security incident mentioned — confirm ANPD notification timeline', source: 'input signal', recommendation: 'Trigger incident response and document Art. 48 assessment', }) } } if (/\b(menor|child|criança)\b/i.test(input) && !findings.some((f) => /Art\.?\s*14|child|menor/i.test(`${f.article} ${f.message}`))) { findings.push({ id: 'safety-child', severity: 'high', article: 'Art. 14', message: 'Child data processing referenced — verify parental consent regime', source: 'input signal', }) } return { ...out, findings }}const skill = { name: 'compliance-lgpd-assessor', description: 'LGPD gap assessment with article-tagged findings.', systemPrompt: `You assess LGPD (Lei 13.709/2018) compliance gaps from the provided processing description.Output: { summary, findings[], gaps[], openQuestions[] }.Each finding: id, severity, optional article (e.g. "Art. 7", "Art. 18"), message, source (quote/signal from input), recommendation.Focus areas when evidenced in input:- legal basis (Art. 7) — consent, legitimate interest, contract- data subject rights (Art. 18)- security measures (Art. 46)- incident notification (Art. 48)- DPO (Art. 41) — only if DPO mentioned or clearly required- international transfer (Art. 33)NEVER invent processing activities, vendors, or violations not supported by input.Thin input → findings may be empty with gaps listing missing inventory fields.${UNTRUSTED_CONTENT_DIRECTIVE}Call submit_lgpd_assessor exactly once. Stop.`, tools: ['submit_lgpd_assessor'],}export function createComplianceLgpdAssessorAgent(config: ComplianceLgpdAssessorConfig) { const submit = (): ToolDefinition => defineZodTool({ name: 'submit_lgpd_assessor', description: 'Submit LGPD assessment. Call exactly once.', schema: Output, toJsonSchema: toJson, async execute() { return 'recorded' }, }) as ToolDefinition async function run(input: string): Promise<LgpdAssessment> { if (!input?.trim()) throw new Error('compliance-lgpd-assessor requires non-empty input') const parsed = await invokeStructured({ adapter: config.adapter, tool: submit(), task: `PROCESSING DESCRIPTION:\n${fenceUntrustedContent(input)}`, parse: (a) => applySafetyNet(input, Output.parse(a)), skill, memory: config.memory, observers: config.observers, onConfirm: config.onConfirm, maxSteps: config.maxSteps ?? 4, }) return { ...parsed, requiresReview: true } } return { name: 'compliance-lgpd-assessor', run, asHandle() { return { name: 'compliance-lgpd-assessor', run: (t: string) => run(t).then((r) => JSON.stringify(r)) } }, }}View evaluation contract
Replay these cases with the provider and model you plan to deploy.
import type { EvalSuite } from '@agentskit/eval'export const suite: EvalSuite = { name: 'compliance-lgpd-assessor', cases: [ { input: 'We process emails for marketing using consent checkbox on signup. No DPO appointed yet.', expected: (r: string) => { const j = JSON.parse(r) return j.findings.some((f: { message: string }) => /consent|Art\.?\s*7/i.test(f.message)) || j.gaps.length > 0 }, }, { input: 'Security breach exposed 10k customer CPF numbers last week.', expected: (r: string) => { const j = JSON.parse(r) return j.findings.some((f: { severity: string; article?: string; message?: string }) => f.severity === 'critical' && /48|breach|vazamento|incidente/i.test(`${f.article} ${f.message}`)) }, }, { input: 'Minimal input.', expected: (r: string) => { const j = JSON.parse(r) return j.gaps.length > 0 || j.findings.length === 0 }, }, { input: 'App collects child birthdays for a school portal without parental flow described.', expected: (r: string) => /Art\.?\s*14|menor|child|criança/i.test(r), }, ],}Was this agent useful?
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