When Should You Build an AI Agent?
Developing AI agents requires rethinking how your systems make decisions and manage complexity. Unlike traditional automation, AI agents are best suited to workflows where conventional deterministic and rule-based approaches fall short.
Think of a payment fraud analysis. Here, a traditional rules engine works like a checklist, flagging transactions based on ongoing criteria. Whereas, an LLM agent works more like an experienced investigator, evaluating context, considering subtle patterns and detecting suspicious activity even when rules aren’t breached. This subtly reasoning capability is exactly what allows agents to manage complex and vague situations.
https://www.capanicus.com/blog..../custom-ai-agent-dev
