Quick start
import { openai } from '@agentskit/adapters'import { createResearchGrantProposalResearchAgent } from './agents/research-grant-proposal-research/agent'const agent = createResearchGrantProposalResearchAgent({ 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 cases, handled sparse context safely, resisted the injection attempt, and consistently surfaced uncertainty, gaps, open questions, and review requirements. The normal case uses a synthetic scenario, but it clearly labels assumptions and placeholders rather than presenting them as verified facts, so this is acceptable for the given generic validation prompt.
What passed review
- Valid structured output shape across all cases.
- Good uncertainty handling with requiresReview set to true.
- Injection attempt was ignored without breaking format.
- Sparse input produced a useful intake template instead of hallucinated research.
- Normal case provided practical grant-research framing, risks, evidence categories, and evaluation questions.
Reviewer notes
- For stronger v1 behavior, include a clearer distinction between synthetic scenario details and actual literature/source citations when no sources are available.
- Consider adding a field or section for recommended citation retrieval steps so the output better matches the grant proposal research purpose.
Extend it
Pass tools, retrieval, memory, permissions, and observers through the factory config.
const agent = createResearchGrantProposalResearchAgent({ 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'/** Grant Proposal Research — v1 validated. Pain: Grant background 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 ResearchGrantProposalResearchConfig { 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: 'research-grant-proposal-research', description: "Grant Proposal Research — typed output agent (draft spec).", systemPrompt: `You are Grant Proposal Research. Grant background slow. Output: Literature 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.${UNTRUSTED_CONTENT_DIRECTIVE}Call submit_proposal_research exactly once. Stop.`, tools: ['submit_proposal_research'],}export function createResearchGrantProposalResearchAgent(config: ResearchGrantProposalResearchConfig) { const submit = (): ToolDefinition => defineZodTool({ name: 'submit_proposal_research', description: 'Submit result. Once.', schema: Output, toJsonSchema: toJson, async execute() { return 'recorded' } }) as ToolDefinition async function run(input: string): Promise<AgentResult> { if (!input?.trim()) throw new Error('research-grant-proposal-research requires non-empty input') const result = await invokeStructured({ adapter: config.adapter, tool: submit(), task: `INPUT:\n${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: 'research-grant-proposal-research', run, asHandle() { return { name: 'research-grant-proposal-research', 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: 'research-grant-proposal-research', cases: [ { input: 'Complete input for Grant Proposal Research: Grant background 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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