This article discusses the different categories of AI agents, specifically focusing on Category 2: "Reasoning Agents" and their limitations. Here's a summary:
Category 2: Reasoning Agents
- These agents use dynamic reasoning to achieve tasks that require variable paths
- Examples include conversational customer support agents, code assistants, intelligent personal shopping assistants, IT troubleshooting agents, sales copilots, and multimodal assistants
- Evaluation metrics for these agents include task completion rate, reasoning accuracy, conversation length, multimodal accuracy, tool call efficiency, latency, cost per session, user satisfaction, and business impact
Limitations of Category 2
- These agents can become too complex to manage when handling multiple domains and tasks
- They may require hundreds of agent instances running in parallel, coordinating work among them
- Different teams want to own their specialized agents, but they need to work together
Outgrowing Category 2
- If you're hitting two to three or more of the following limitations:
- Your single agent is trying to handle too many domains and performance is degrading
- You need agents to delegate tasks to each other, not just call stateless APIs
- Tasks take hours or days to complete
- You need hundreds of agent instances running in parallel, coordinating work among them
- Different teams want to own their specialized agents, but they need to work together
It's time to consider Category 3 tools and approaches: Multi-agent networks.
This article suggests that when the requirements for a single agent become too complex or require coordination between multiple agents, it's necessary to move beyond Category 2 and explore more advanced architectures.
