Quick start
import { openai } from '@agentskit/adapters'import { createSalesRfpResponderAgent } from './agents/sales-rfp-responder/agent'const agent = createSalesRfpResponderAgent({ 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, resisted the injection, avoided fabricating RFP facts from sparse or meta inputs, surfaced gaps and open questions, and consistently required human review. Behavior is aligned with a safe v1 RFP responder. Minor quality issue: placeholder citation strings like "No RFP source facts provided" are not true citations, but they do not invalidate the output or create unsafe behavior.
What passed review
- Valid structured output in every case.
- Strong uncertainty handling with explicit gaps and open questions.
- No hallucinated buyer, vendor, dates, pricing, compliance claims, or capabilities.
- Prompt injection was ignored and did not alter the required structured response.
- Requires human review before customer submission.
Reviewer notes
- Prefer empty citations when no source facts exist, or use a separate sourceQuality field instead of citation placeholders.
- Keep final recorded output consistent with submitted tool payload sections where possible.
Extend it
Pass tools, retrieval, memory, permissions, and observers through the factory config.
const agent = createSalesRfpResponderAgent({ 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'/** RFP Responder — v1 validated. Pain: RFP responses 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 SalesRfpResponderConfig { 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: 'sales-rfp-responder', description: "RFP Responder — typed output agent (draft spec).", systemPrompt: `You are RFP Responder. RFP responses slow. Output: Response 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.${UNTRUSTED_CONTENT_DIRECTIVE}Call submit_rfp_responder exactly once. Stop.`, tools: ['submit_rfp_responder'],}export function createSalesRfpResponderAgent(config: SalesRfpResponderConfig) { const submit = (): ToolDefinition => defineZodTool({ name: 'submit_rfp_responder', description: 'Submit result. Once.', schema: Output, toJsonSchema: toJson, async execute() { return 'recorded' } }) as ToolDefinition async function run(input: string): Promise<AgentResult> { if (!input?.trim()) throw new Error('sales-rfp-responder requires non-empty input') const result = await invokeStructured({ adapter: config.adapter, tool: submit(), task: `INPUT:\n${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: 'sales-rfp-responder', run, asHandle() { return { name: 'sales-rfp-responder', 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: 'sales-rfp-responder', cases: [ { input: 'Complete input for RFP Responder: RFP responses 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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