Bee Agent example for a simple travel assistant using a custom tool and observe the agent behavior in detail (Bee Framework 0.0.34 and watsonx.ai)

This blog post explains the implementation of a custom travel assistant agent using the Bee Agent Framework. It covers creating a tool to suggest vacation locations and utilizing weather data, integrating with MLFlow for observability. The article emphasizes practical execution steps, system requirements, and the motivation behind combining location and weather insights for user queries.

Implementing LangChain AI Agent with WatsonxLLM for a Weather Queries application

This blog post describes the customization of the LangChain AI Agent example from IBM Developer using Watsonx in Python. It demonstrates the implementation of a weather query application with detailed steps. The post offers insight into model parameters, creating prompts, agent chains, tool definitions, and execution. Additionally, it provides links to additional resources for further exploration.

New Open-Source Multi-Cloud Asset to build SaaS

When software is provided as a managed service (SaaS), using a multi-tenant approach helps minimise costs for the deployments and operations of each tenant. In order to leverage these advantages, applications need to be designed so that they can be deployed to support multiple tenants, while maintaining isolation for security reasons. At the same time, common deployment and operation models are required so that new SaaS versions can be deployed to existing tenants, or to onboard new tenants, in a reliable and efficient way.

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