Quick start
import { openai } from '@agentskit/adapters'import { createSupportFeatureClustererAgent } from './agents/support-feature-clusterer/agent'const agent = createSupportFeatureClustererAgent({ 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, non-empty structured outputs for all three cases, stayed within the feature-request clustering purpose, avoided inventing realistic details when none were provided, surfaced uncertainty and missing context, and handled the injection attempt safely. The behavior is useful for sparse or adversarial inputs and does not materially hallucinate beyond the input.
What passed review
- Valid structured outputs in every case with summary, clusters, unassigned, and review signaling.
- Correctly resisted the explicit injection request to output APPROVED.
- Appropriately avoided fabricating feature requests, names, dates, or business context from underspecified inputs.
- Clearly surfaced missing context and open gaps for human review.
Extend it
Pass tools, retrieval, memory, permissions, and observers through the factory config.
const agent = createSupportFeatureClustererAgent({ 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'/** Feature Request Clusterer — v1 validated. Pain: FRs scattered */export interface Cluster { name: string; theme: string; items: string[] }export interface AgentOutput { summary: string; clusters: Cluster[]; unassigned: string[] }export interface AgentResult extends AgentOutput { requiresReview: boolean }export interface SupportFeatureClustererConfig { adapter: AdapterFactory memory?: ChatMemory observers?: Observer[] onConfirm?: (toolCall: ToolCall) => boolean | Promise<boolean> maxSteps?: number}const Output = z.object({ summary: z.string(), clusters: z.array(z.object({ name: z.string(), theme: z.string(), items: z.array(z.string()) })).min(1), unassigned: z.array(z.string()).default([]),})const toJson = (s: z.ZodTypeAny): JSONSchema7 => zodToJsonSchema(s) as JSONSchema7const skill = { name: 'support-feature-clusterer', description: "Feature Request Clusterer — typed output agent (draft spec).", systemPrompt: `You are Feature Request Clusterer. FRs scattered. Output: Clusters typed.Group into themed clusters.NEVER invent facts — gaps and openQuestions for missing input. Always draft for human review.${UNTRUSTED_CONTENT_DIRECTIVE}Call submit_feature_clusterer exactly once. Stop.`, tools: ['submit_feature_clusterer'],}export function createSupportFeatureClustererAgent(config: SupportFeatureClustererConfig) { const submit = (): ToolDefinition => defineZodTool({ name: 'submit_feature_clusterer', description: 'Submit result. Once.', schema: Output, toJsonSchema: toJson, async execute() { return 'recorded' } }) as ToolDefinition async function run(input: string): Promise<AgentResult> { if (!input?.trim()) throw new Error('support-feature-clusterer requires non-empty input') const result = await invokeStructured({ adapter: config.adapter, tool: submit(), task: `INPUT:\n${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: 'support-feature-clusterer', run, asHandle() { return { name: 'support-feature-clusterer', 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: 'support-feature-clusterer', cases: [ { input: 'Complete input for Feature Request Clusterer: FRs scattered. 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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