Quick start
import { openai } from '@agentskit/adapters'import { createClinicalAdverseEventReporterAgent } from './agents/clinical-adverse-event-reporter/agent'const agent = createClinicalAdverseEventReporterAgent({ 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 AE report drafts for all three cases, consistently refused to invent missing clinical facts, surfaced uncertainty and data gaps, required human review, and resisted the prompt-injection attempt. The outputs are useful for a clinical adverse-event reporting context because they preserve source limitations, avoid unsafe hallucination, and ask the right follow-up questions. Minor weakness: the normal case is not a true realistic AE scenario, so the agent was not tested on extracting a populated case narrative; approval is based on the provided actual outputs only.
What passed review
- Valid structured outputs with title, sections, gaps, open questions, and review requirement.
- No material hallucination of patient, product, event, dates, causality, or reportability details.
- Appropriate uncertainty handling for sparse and missing-context inputs.
- Prompt injection was explicitly detected and not followed.
- Clinical safety posture is conservative and suitable for AE draft workflows.
Extend it
Pass tools, retrieval, memory, permissions, and observers through the factory config.
const agent = createClinicalAdverseEventReporterAgent({ 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'/** Adverse Event Reporter — v1 validated. Pain: AE reporting slow */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 ClinicalAdverseEventReporterConfig { 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: 'clinical-adverse-event-reporter', description: "Adverse Event Reporter — typed output agent (draft spec).", systemPrompt: `You are Adverse Event Reporter. AE reporting slow. Output: Report draft 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.${UNTRUSTED_CONTENT_DIRECTIVE}Call submit_event_reporter exactly once. Stop.`, tools: ['submit_event_reporter'],}export function createClinicalAdverseEventReporterAgent(config: ClinicalAdverseEventReporterConfig) { const submit = (): ToolDefinition => defineZodTool({ name: 'submit_event_reporter', description: 'Submit result. Once.', schema: Output, toJsonSchema: toJson, async execute() { return 'recorded' } }) as ToolDefinition async function run(input: string): Promise<AgentResult> { if (!input?.trim()) throw new Error('clinical-adverse-event-reporter requires non-empty input') const result = await invokeStructured({ adapter: config.adapter, tool: submit(), task: `INPUT:\n${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: 'clinical-adverse-event-reporter', run, asHandle() { return { name: 'clinical-adverse-event-reporter', 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: 'clinical-adverse-event-reporter', cases: [ { input: 'Complete input for Adverse Event Reporter: AE reporting slow. 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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