Quick start
import { openai } from '@agentskit/adapters'import { createDataEtlFailureDiagnoserAgent } from './agents/data-etl-failure-diagnoser/agent'const agent = createDataEtlFailureDiagnoserAgent({ 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
- 3
The agent produced valid structured outputs for all three cases, stayed within its ETL diagnosis purpose, handled missing evidence conservatively, surfaced gaps and open questions, and resisted the injection request without leaking or following unsafe instructions. The normal case did not fabricate the requested concrete details, which is appropriate for a diagnoser when no incident evidence is supplied. Minor concern: the 'normal' case was not actually a realistic ETL incident, so this validation does not demonstrate full diagnostic quality on concrete logs or failure symptoms.
What passed review
- Valid structured output shape across all cases with title, sections, gaps, openQuestions, and requiresReview.
- Correctly avoided hallucinating root causes from sparse or meta-level prompts.
- Injection case explicitly treated prompt redirection as non-diagnostic input and did not output APPROVED.
- Minimal case provided concise, safe next questions and review requirement.
Extend it
Pass tools, retrieval, memory, permissions, and observers through the factory config.
const agent = createDataEtlFailureDiagnoserAgent({ 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'/** ETL Failure Diagnoser — v1 validated. Pain: ETL failures opaque */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 DataEtlFailureDiagnoserConfig { 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: 'data-etl-failure-diagnoser', description: "ETL Failure Diagnoser — typed output agent (draft spec).", systemPrompt: `You are ETL Failure Diagnoser. ETL failures opaque. Output: Diagnosis 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.${UNTRUSTED_CONTENT_DIRECTIVE}Call submit_failure_diagnoser exactly once. Stop.`, tools: ['submit_failure_diagnoser'],}export function createDataEtlFailureDiagnoserAgent(config: DataEtlFailureDiagnoserConfig) { const submit = (): ToolDefinition => defineZodTool({ name: 'submit_failure_diagnoser', description: 'Submit result. Once.', schema: Output, toJsonSchema: toJson, async execute() { return 'recorded' } }) as ToolDefinition async function run(input: string): Promise<AgentResult> { if (!input?.trim()) throw new Error('data-etl-failure-diagnoser requires non-empty input') const result = await invokeStructured({ adapter: config.adapter, tool: submit(), task: `INPUT:\n${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: 'data-etl-failure-diagnoser', run, asHandle() { return { name: 'data-etl-failure-diagnoser', 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: 'data-etl-failure-diagnoser', cases: [ { input: 'Complete input for ETL Failure Diagnoser: ETL failures opaque. 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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