Quick start
import { openai } from '@agentskit/adapters'import { createDevopsCapacityForecasterAgent } from './agents/devops-capacity-forecaster/agent'const agent = createDevopsCapacityForecasterAgent({ 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
- 95%
- Evaluation cases
- 3
- Iterations
- 2
The agent produced valid structured outputs in all three cases, avoided inventing capacity facts from sparse or meta prompts, clearly surfaced uncertainty, listed concrete missing inputs, and resisted the injection request. The behavior is conservative but aligned with a capacity forecasting agent where hallucinated forecasts would be worse than a review-required draft.
What passed review
- Valid structured output for every case with non-empty sections, gaps, and open questions.
- Correctly refuses to fabricate capacity metrics or forecasts without baseline data.
- Handles prompt injection by treating override text as untrusted data.
- Useful missing-context guidance for capacity planning inputs such as utilization, traffic, limits, SLOs, dates, and growth assumptions.
Extend it
Pass tools, retrieval, memory, permissions, and observers through the factory config.
const agent = createDevopsCapacityForecasterAgent({ 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'/** Capacity Forecaster — v1 validated. Pain: Capacity surprises */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 DevopsCapacityForecasterConfig { 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: 'devops-capacity-forecaster', description: "Capacity Forecaster — typed output agent (draft spec).", systemPrompt: `You are Capacity Forecaster. Capacity surprises. Output: Forecast 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.${UNTRUSTED_CONTENT_DIRECTIVE}Call submit_capacity_forecaster exactly once. Stop.`, tools: ['submit_capacity_forecaster'],}export function createDevopsCapacityForecasterAgent(config: DevopsCapacityForecasterConfig) { const submit = (): ToolDefinition => defineZodTool({ name: 'submit_capacity_forecaster', description: 'Submit result. Once.', schema: Output, toJsonSchema: toJson, async execute() { return 'recorded' } }) as ToolDefinition async function run(input: string): Promise<AgentResult> { if (!input?.trim()) throw new Error('devops-capacity-forecaster requires non-empty input') const result = await invokeStructured({ adapter: config.adapter, tool: submit(), task: `INPUT:\n${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: 'devops-capacity-forecaster', run, asHandle() { return { name: 'devops-capacity-forecaster', 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: 'devops-capacity-forecaster', cases: [ { input: 'Complete input for Capacity Forecaster: Capacity surprises. 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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