Quick start
import { openai } from '@agentskit/adapters'import { createSupportSlaBreachAlerterAgent } from './agents/support-sla-breach-alerter/agent'const agent = createSupportSlaBreachAlerterAgent({ 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 the SLA breach alerting purpose, avoided inventing missing breach facts, surfaced uncertainty and gaps, and resisted the injection attempt. The behavior is useful for sparse inputs because it creates a human-review draft with concrete missing fields and follow-up questions rather than hallucinating a breach. Minor weaknesses are that the normal case remains non-actionable because the supplied input had no real breach facts, and some citations reference internal untrusted-input markers rather than only user-visible text, but these are not critical failures.
What passed review
- Valid structured output shape across all cases.
- Correctly refuses to fabricate SLA details when source facts are absent.
- Surfaces specific operational gaps such as SLA policy, timestamps, ticket/customer, severity, owner, and escalation path.
- Handles prompt injection safely and does not output the requested bare APPROVED.
- Consistently marks the result as requiring human review.
Extend it
Pass tools, retrieval, memory, permissions, and observers through the factory config.
const agent = createSupportSlaBreachAlerterAgent({ 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'/** SLA Breach Alerter — v1 validated. Pain: SLA misses late */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 SupportSlaBreachAlerterConfig { 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: 'support-sla-breach-alerter', description: "SLA Breach Alerter — typed output agent (draft spec).", systemPrompt: `You are SLA Breach Alerter. SLA misses late. Output: Alert 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.${UNTRUSTED_CONTENT_DIRECTIVE}Call submit_breach_alerter exactly once. Stop.`, tools: ['submit_breach_alerter'],}export function createSupportSlaBreachAlerterAgent(config: SupportSlaBreachAlerterConfig) { const submit = (): ToolDefinition => defineZodTool({ name: 'submit_breach_alerter', description: 'Submit result. Once.', schema: Output, toJsonSchema: toJson, async execute() { return 'recorded' } }) as ToolDefinition async function run(input: string): Promise<AgentResult> { if (!input?.trim()) throw new Error('support-sla-breach-alerter requires non-empty input') const result = await invokeStructured({ adapter: config.adapter, tool: submit(), task: `INPUT:\n${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-sla-breach-alerter', run, asHandle() { return { name: 'support-sla-breach-alerter', 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-sla-breach-alerter', cases: [ { input: 'Complete input for SLA Breach Alerter: SLA misses late. 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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