Quick start
import { openai } from '@agentskit/adapters'import { createSalesLeadScorerAgent } from './agents/sales-lead-scorer/agent'const agent = createSalesLeadScorerAgent({ 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, resisted the injection attempt, avoided inventing lead facts, surfaced uncertainty, routed sparse inputs to human review, and provided concrete gaps and open questions. Behavior is conservative but appropriate for a lead scorer when no factual lead data is supplied.
What passed review
- Valid structured output in every run
- Correctly treats sparse or instruction-like inputs as insufficient lead data instead of hallucinating details
- Injection case did not output APPROVED and explicitly identified the override attempt
- Useful gaps and open questions support follow-up qualification
Extend it
Pass tools, retrieval, memory, permissions, and observers through the factory config.
const agent = createSalesLeadScorerAgent({ 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'/** Lead Scorer — v1 validated. Pain: Lead qual inconsistent */export type Severity = 'critical' | 'high' | 'medium' | 'low'export interface AgentOutput { category: string; severity: Severity; queue: string; rationale: string; gaps: string[]; openQuestions: string[] }export interface AgentResult extends AgentOutput { requiresReview: boolean }export interface SalesLeadScorerConfig { adapter: AdapterFactory memory?: ChatMemory observers?: Observer[] onConfirm?: (toolCall: ToolCall) => boolean | Promise<boolean> maxSteps?: number}const Output = z.object({ category: z.string(), severity: z.enum(['critical', 'high', 'medium', 'low']), queue: z.string(), rationale: z.string(), gaps: z.array(z.string()).default([]), openQuestions: z.array(z.string()).default([]),})const toJson = (s: z.ZodTypeAny): JSONSchema7 => zodToJsonSchema(s) as JSONSchema7function applySafetyNet(input: string, o: z.infer<typeof Output>) { if (/\b(outage|breach|emergency|stroke|suicidal|data loss)\b/i.test(input) && o.severity !== 'critical') return { ...o, severity: 'critical' as const, queue: 'escalation', rationale: o.rationale + ' [safety-net]' } return o}const skill = { name: 'sales-lead-scorer', description: "Lead Scorer — typed output agent (draft spec).", systemPrompt: `You are Lead Scorer. Lead qual inconsistent. Output: Score typed.Classify with category, severity, queue, rationale. Gaps for missing input.NEVER invent facts — gaps and openQuestions for missing input. Always draft for human review.${UNTRUSTED_CONTENT_DIRECTIVE}Call submit_lead_scorer exactly once. Stop.`, tools: ['submit_lead_scorer'],}export function createSalesLeadScorerAgent(config: SalesLeadScorerConfig) { const submit = (): ToolDefinition => defineZodTool({ name: 'submit_lead_scorer', description: 'Submit result. Once.', schema: Output, toJsonSchema: toJson, async execute() { return 'recorded' } }) as ToolDefinition async function run(input: string): Promise<AgentResult> { if (!input?.trim()) throw new Error('sales-lead-scorer requires non-empty input') const result = await invokeStructured({ adapter: config.adapter, tool: submit(), task: `INPUT:\n${fenceUntrustedContent(input)}`, parse: (a) => applySafetyNet(input, Output.parse(a)), skill, memory: config.memory, observers: config.observers, onConfirm: config.onConfirm, maxSteps: config.maxSteps ?? 4, }) return { ...result, requiresReview: true } } return { name: 'sales-lead-scorer', run, asHandle() { return { name: 'sales-lead-scorer', 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-lead-scorer', cases: [ { input: 'Complete input for Lead Scorer: Lead qual inconsistent. 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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