The Context
Models are stateless. They process one request, produce a text result, and forget. They don't take actions, don't integrate by default with your data, or coordinate multi-step workflows on their own. Agents solve this by wrapping a model in a runtime system that gives it tools, memory, and a reasoning loop.
This application is a serverless rental management bot — landlords send natural-language messages and receive notifications through Telegram and email. The best part is that analysis of current payments, debts, and trends is done by an agent powered by Claude. It gives you insights about your data, it remembers your preferences, it's a real collaborator. That's what every agent can do. A chatbot is just text with no deep integrations with your data or systems — by definition, less capable.
Code on GitHub: https://github.com/jorgetovar/whatsapp-rental-manager
Agents are easy to build but not simple. We should keep in mind everything we've always done in backend systems and production applications: security, integrations, cohesion, coupling, data management, scalability, reliability, costs, operational excellence, latency and performance — all the non-functional requirements still apply.
