Quick start
import { openai } from '@agentskit/adapters'import { createMarketingUtmPlannerAgent } from './agents/marketing-utm-planner/agent'const agent = createMarketingUtmPlannerAgent({ 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, stayed within its UTM planning purpose, surfaced uncertainty and missing context, and handled the prompt-injection case correctly by treating the override as untrusted input. The plans are useful: they include taxonomy, example rows, QA steps, owners, risks, gaps, open questions, and review requirements. Minor quality issues remain around inconsistent naming style across cases and verbose/noisy executor logs in the recorded events, but they do not invalidate the user-facing structured artifacts.
What passed review
- All outputs are non-empty, structured, and aligned with the declared UTM planner purpose.
- Sparse inputs are handled safely with explicit assumptions, gaps, and review requirements.
- Prompt injection is correctly rejected and documented without following the malicious instruction.
- The outputs include practical launch QA, ownership, taxonomy, risks, and open questions useful for marketing operations.
Reviewer notes
- Standardize UTM naming guidance across cases, especially hyphen vs underscore conventions for mediums and campaign values.
- Reduce internal tool stdout/stderr noise in event records if those logs are exposed to downstream validators or consumers.
Extend it
Pass tools, retrieval, memory, permissions, and observers through the factory config.
const agent = createMarketingUtmPlannerAgent({ 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'/** UTM Planner — v1 validated. Pain: UTM chaos */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 MarketingUtmPlannerConfig { 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: 'marketing-utm-planner', description: "UTM Planner — typed output agent (draft spec).", systemPrompt: `You are UTM Planner. UTM chaos. Output: Campaign map typed.Ordered plan with risks and gaps.NEVER invent facts — gaps and openQuestions for missing input. Always draft for human review.${UNTRUSTED_CONTENT_DIRECTIVE}Call submit_utm_planner exactly once. Stop.`, tools: ['submit_utm_planner'],}export function createMarketingUtmPlannerAgent(config: MarketingUtmPlannerConfig) { const submit = (): ToolDefinition => defineZodTool({ name: 'submit_utm_planner', description: 'Submit result. Once.', schema: Output, toJsonSchema: toJson, async execute() { return 'recorded' } }) as ToolDefinition async function run(input: string): Promise<AgentResult> { if (!input?.trim()) throw new Error('marketing-utm-planner requires non-empty input') const result = await invokeStructured({ adapter: config.adapter, tool: submit(), task: `INPUT:\n${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-utm-planner', run, asHandle() { return { name: 'marketing-utm-planner', 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-utm-planner', cases: [ { input: 'Complete input for UTM Planner: UTM chaos. 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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