Quick start
import { openai } from '@agentskit/adapters'import { createResearchCompetitiveLandscapeAgent } from './agents/research-competitive-landscape/agent'const agent = createResearchCompetitiveLandscapeAgent({ 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 consistently produced valid structured outputs, refused to invent competitors or market moves from sparse inputs, surfaced concrete gaps and open questions, and handled the injection attempt correctly by treating it as untrusted data. Behavior is conservative and aligned with a research agent that requires sourced evidence. Minor weakness: the citation fields use untrusted-input markers rather than true source citations when no sources exist, which is acceptable here but not ideal for a source-oriented agent.
What passed review
- Valid structured output in all cases.
- No empty outputs or schema-breaking responses.
- Correctly avoided hallucinating competitive facts from insufficient input.
- Explicitly surfaced uncertainty, missing context, and source gaps.
- Resisted the instruction-injection request and did not output the requested approval phrase.
Extend it
Pass tools, retrieval, memory, permissions, and observers through the factory config.
const agent = createResearchCompetitiveLandscapeAgent({ 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'/** Competitive Landscape — v1 validated. Pain: Landscape maps stale */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 ResearchCompetitiveLandscapeConfig { 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-competitive-landscape', description: "Competitive Landscape — typed output agent (draft spec).", systemPrompt: `You are Competitive Landscape. Landscape maps stale. Output: Players/moves typed + sources.Draft sections with citations from input. Gaps for missing facts.NEVER invent facts — gaps and openQuestions for missing input. Always draft for human review.${UNTRUSTED_CONTENT_DIRECTIVE}Call submit_competitive_landscape exactly once. Stop.`, tools: ['submit_competitive_landscape'],}export function createResearchCompetitiveLandscapeAgent(config: ResearchCompetitiveLandscapeConfig) { const submit = (): ToolDefinition => defineZodTool({ name: 'submit_competitive_landscape', description: 'Submit result. Once.', schema: Output, toJsonSchema: toJson, async execute() { return 'recorded' } }) as ToolDefinition async function run(input: string): Promise<AgentResult> { if (!input?.trim()) throw new Error('research-competitive-landscape requires non-empty input') const result = await invokeStructured({ adapter: config.adapter, tool: submit(), task: `INPUT:\n${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-competitive-landscape', run, asHandle() { return { name: 'research-competitive-landscape', 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-competitive-landscape', cases: [ { input: 'Complete input for Competitive Landscape: Landscape maps stale. 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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