{
  "id": 591500,
  "title": "I Built a Team of AI Agents to Find Startup Opportunities",
  "url": "https://urgent.news/2026/08/11/i-built-a-team-of-ai-agents-to-find-startup-opportunities",
  "topic": "ai",
  "section": "AI",
  "published": "2026-08-11T18:37:09.000Z",
  "source": {
    "name": "Dev.to",
    "slug": "dev-to",
    "url": "https://dev.to/vivek_shetye/i-built-a-team-of-ai-agents-to-find-startup-opportunities-3309"
  },
  "original_language": "en",
  "account": "Most people use AI for startup research by requesting a list of promising AI startup ideas. However, these AI-generated lists often lack context, making it hard to distinguish between evidence-based conclusions, assumptions, and model-generated connections. To address this issue, a Startup Intelligence team was created using Hermes Agent, which employs four specialized AI agents to research markets, investigate competitors, audit evidence, challenge each other's conclusions, and ultimately rank B2B AI SaaS opportunities.\n\nThe system produces structured research containing market opportunity scores, companies and competitors, customer pain points, evidence-backed claims, source URLs, supporting passages, AI advantages, and low-cost validation experiments. The process starts with a four-agent workflow designed to reduce uncertainty in startup research rather than generate ideas optimally.\n\nThe four agents include the Startup Director, Market Researcher, Competition & Signals Analyst, and Skeptic Editor. The Startup Director acts as the project lead, transforming a vague founder question into a well-defined research problem. This director also generates two important artifacts: a brief that outlines the research scope, deliverables, and unknowns, and a rubric that establishes the rules for evaluating research output.\n\nThe Market Researcher's role is to determine whether there is meaningful evidence that customers have the problem. This agent searches for signals such as buyer pain, willingness to pay, customer demand, company formation, funding, adoption, and traction. It is crucial that the source of each claim is preserved for later auditing purposes.\n\nThe Competition & Signals Analyst investigates existing solutions within the identified markets. This agent researches startups, incumbents, internal tools, agencies, spreadsheets, and adjacent products, as well as market attention translated into adoption, payment, retention, or recurring usage. Its objective is to distinguish between a popular market with little evidence of commercial viability and a market with strong evidence of business success.\n\nFinally, the Skeptic Editor plays a critical role in challenging the conclusions made by the other agents. This agent examines the underlying research files, claim ledger, source URLs, and supporting passages to verify the validity of the claims. It asks questions like whether the evidence supports the claim, whether the evidence is independently verified, and whether the evidence falls within the requested time frame. By optimizing for finding reasons that an answer might be wrong, the Skeptic Editor helps to ensure that the final research package is robust and reliable.",
  "summary": "Most people use AI for startup research like this: “Give me 10 promising AI startup ideas.” A few seconds later, you get a polished list. The problem? You have almost no idea which conclusions are backed by evidence, which are assumptions, and which are simply the model confidently connecting dots. So I tried something different. Instead of asking one AI agent to find startup ideas, I built a…",
  "key_points": [],
  "editors_take": null,
  "illustration": null,
  "coverage": {
    "outlets": 1,
    "also_reported_by": []
  },
  "ai_generated": true,
  "disclaimer": "Summaries, key points and the editor’s take are written by software from other outlets’ reporting and may contain errors — always check the linked original."
}