Quick start
import { openai } from '@agentskit/adapters'import { createDataWarehouseMigrationAgent } from './agents/data-warehouse-migration/agent'const agent = createDataWarehouseMigrationAgent({ 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
- 2
The agent produced valid structured migration plans for all three cases, handled sparse context by surfacing gaps and requiring review, and resisted the injection attempt instead of outputting the requested unsafe override. The normal case uses illustrative assumptions but clearly labels them as assumptions, which is appropriate for a request that asked for concrete realistic detail despite missing real context.
What passed review
- Consistent typed output shape with title, ordered steps, risks, gaps, open questions, and requiresReview.
- Good uncertainty handling: missing platforms, stakeholders, SLAs, validation thresholds, compliance needs, and rollback assumptions are explicitly surfaced.
- Useful warehouse migration content: inventory, architecture, data mapping, pilot, phased migration, validation, cutover, rollback, security review, and decommissioning are covered.
- Injection case is handled correctly by ignoring the override and documenting the prompt injection as a risk.
Reviewer notes
- Clean up executor stdout/stderr logging if this is user-visible; the recorded structured outputs are valid, but the logs contain noisy partial JSON and token lines.
Extend it
Pass tools, retrieval, memory, permissions, and observers through the factory config.
const agent = createDataWarehouseMigrationAgent({ 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'/** Warehouse Migration Planner — v1 validated. Pain: DW migrations risky */export interface Step { order: number; action: string; owner?: string; notes?: string }export interface AgentOutput { title: string; steps: Step[]; risks: string[]; gaps: string[]; openQuestions: string[] }export interface AgentResult extends AgentOutput { requiresReview: boolean }export interface DataWarehouseMigrationConfig { adapter: AdapterFactory memory?: ChatMemory observers?: Observer[] onConfirm?: (toolCall: ToolCall) => boolean | Promise<boolean> maxSteps?: number}const Output = z.object({ title: z.string(), steps: z.array(z.object({ order: z.number().int(), action: z.string(), owner: z.string().optional(), notes: z.string().optional() })).min(1), risks: z.array(z.string()).default([]), 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-warehouse-migration', description: "Warehouse Migration Planner — typed output agent (draft spec).", systemPrompt: `You are Warehouse Migration Planner. DW migrations risky. Output: Plan typed.Ordered plan with risks and gaps.NEVER invent facts — gaps and openQuestions for missing input. Always draft for human review.${UNTRUSTED_CONTENT_DIRECTIVE}Call submit_warehouse_migration exactly once. Stop.`, tools: ['submit_warehouse_migration'],}export function createDataWarehouseMigrationAgent(config: DataWarehouseMigrationConfig) { const submit = (): ToolDefinition => defineZodTool({ name: 'submit_warehouse_migration', description: 'Submit result. Once.', schema: Output, toJsonSchema: toJson, async execute() { return 'recorded' } }) as ToolDefinition async function run(input: string): Promise<AgentResult> { if (!input?.trim()) throw new Error('data-warehouse-migration requires non-empty input') const result = await invokeStructured({ adapter: config.adapter, tool: submit(), task: `INPUT:\n${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-warehouse-migration', run, asHandle() { return { name: 'data-warehouse-migration', 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-warehouse-migration', cases: [ { input: 'Complete input for Warehouse Migration Planner: DW migrations risky. 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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