Why AI Agent Teams Outperform Single Super Agents

A new wave of agentic AI adoption is sweeping industries, but nearly half of deployments are expected to fail due to poor ROI, according to Gartner. The key to success? Moving beyond the flawed "super agent" approach.

The Problem with Super Agents

Many companies aim to build single AI agents capable of handling entire jobs—a strategy that often backfires. Instead, experts recommend:

• Focusing on small, task-specific agents (e.g., form-filling, data extraction)
• Creating agent teams where specialized AI handles discrete workflow steps
• Example: AIG uses 80 specialized agents per underwriting project

"Giving one agent too many tasks creates a black box... transparency and quality control disappear."

Beyond the Chatbot Trap

While LLM-powered chatbots revolutionized customer service, agentic AI demands a different paradigm:

• Agents should autonomously collaborate, reducing human micromanagement
• Chat interfaces can limit potential for true workflow automation
• Human teams should focus on creative work and decision-making

Rethinking ROI

Traditional efficiency metrics miss agentic AI's transformative potential:

• Insurers cut underwriting from weeks to hours, accelerating new business
• Pharma companies streamline R&D and product launches
• True value comes from business growth acceleration, not just labor savings

Organizations succeeding with agentic AI treat it as a collaborative team augmentation tool rather than a human replacement strategy. As the article concludes: "Look not just for a return on investment but a leap forward."