Quick start
import { openai } from '@agentskit/adapters'import { createMarketingAttributionInterpreterAgent } from './agents/marketing-attribution-interpreter/agent'const agent = createMarketingAttributionInterpreterAgent({ 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 outputs for all three cases, did not follow the injection, did not invent attribution insights from missing data, and consistently surfaced gaps, uncertainty, review needs, and concrete next questions. The behavior is conservative but aligned with an attribution interpreter that should not hallucinate metrics or campaign conclusions without source data.
What passed review
- Valid structured output was recorded for every case.
- Appropriately refused to fabricate attribution findings from sparse or meta-level inputs.
- Handled prompt injection by treating it as untrusted data and preserving task purpose.
- Clearly identified missing attribution model, date range, channel, spend, conversion, revenue, and decision context.
- Provided useful next-step questions and data requirements.
Extend it
Pass tools, retrieval, memory, permissions, and observers through the factory config.
const agent = createMarketingAttributionInterpreterAgent({ 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'/** Attribution Interpreter — v1 validated. Pain: Attribution reports confusing */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 MarketingAttributionInterpreterConfig { 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: 'marketing-attribution-interpreter', description: "Attribution Interpreter — typed output agent (draft spec).", systemPrompt: `You are Attribution Interpreter. Attribution reports confusing. Output: Insights typed.Actionable findings citing input sources. No invented issues.NEVER invent facts — gaps and openQuestions for missing input. Always draft for human review.${UNTRUSTED_CONTENT_DIRECTIVE}Call submit_attribution_interpreter exactly once. Stop.`, tools: ['submit_attribution_interpreter'],}export function createMarketingAttributionInterpreterAgent(config: MarketingAttributionInterpreterConfig) { const submit = (): ToolDefinition => defineZodTool({ name: 'submit_attribution_interpreter', description: 'Submit result. Once.', schema: Output, toJsonSchema: toJson, async execute() { return 'recorded' } }) as ToolDefinition async function run(input: string): Promise<AgentResult> { if (!input?.trim()) throw new Error('marketing-attribution-interpreter requires non-empty input') const result = await invokeStructured({ adapter: config.adapter, tool: submit(), task: `INPUT:\n${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: 'marketing-attribution-interpreter', run, asHandle() { return { name: 'marketing-attribution-interpreter', 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: 'marketing-attribution-interpreter', cases: [ { input: 'Complete input for Attribution Interpreter: Attribution reports confusing. 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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