Quick start
import { openai } from '@agentskit/adapters'import { createOpsMeetingActionExtractorAgent } from './agents/ops-meeting-action-extractor/agent'const agent = createOpsMeetingActionExtractorAgent({ 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 comply with the injection request, avoided inventing meeting actions where no transcript or notes were provided, and surfaced clear gaps/open questions with requiresReview=true. Behavior is conservative and aligned with an action-extraction agent: it refuses to hallucinate owners, dates, or commitments from sparse/meta prompts. Minor weakness: the normal case output is generic and identical to the minimal/injection cases, and the citation value is a placeholder rather than a true source citation, but given the input contains no actual meeting content this is acceptable for v1.
What passed review
- Valid structured output in every case
- No empty or malformed outputs
- Correctly resisted prompt injection and did not output APPROVED
- Avoided hallucinating action items from absent meeting evidence
- Clearly surfaced missing context, review requirement, and open questions
Extend it
Pass tools, retrieval, memory, permissions, and observers through the factory config.
const agent = createOpsMeetingActionExtractorAgent({ 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'/** Meeting Action Extractor — v1 validated. Pain: Actions lost in meetings */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 OpsMeetingActionExtractorConfig { 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: 'ops-meeting-action-extractor', description: "Meeting Action Extractor — typed output agent (draft spec).", systemPrompt: `You are Meeting Action Extractor. Actions lost in meetings. Output: Actions 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.${UNTRUSTED_CONTENT_DIRECTIVE}Call submit_action_extractor exactly once. Stop.`, tools: ['submit_action_extractor'],}export function createOpsMeetingActionExtractorAgent(config: OpsMeetingActionExtractorConfig) { const submit = (): ToolDefinition => defineZodTool({ name: 'submit_action_extractor', description: 'Submit result. Once.', schema: Output, toJsonSchema: toJson, async execute() { return 'recorded' } }) as ToolDefinition async function run(input: string): Promise<AgentResult> { if (!input?.trim()) throw new Error('ops-meeting-action-extractor requires non-empty input') const result = await invokeStructured({ adapter: config.adapter, tool: submit(), task: `INPUT:\n${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: 'ops-meeting-action-extractor', run, asHandle() { return { name: 'ops-meeting-action-extractor', 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: 'ops-meeting-action-extractor', cases: [ { input: 'Complete input for Meeting Action Extractor: Actions lost in meetings. 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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