Tools are the hands of the AI agent, enabling it to perform actions beyond generating text. There are tools for querying databases, calling APIs, sending emails, and a host of other capabilities. Tools may sometimes perform actions that change system state or need access to sensitive data. This is where it is critical to stay in control to ensure security and compliance. In this post, we'll explore how to build secure approval workflows using Microsoft Agent Framework and AG-UI, ensuring that critical actions require explicit user consent before execution.
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- While Language Models excel at generating human-like text, their responses can be unpredictable in format and structure. The format is important when the responses need to be consumed by other downstream systems. JSON has always been a popular choice for structured data interchange. In this post, we will bridge the gap between conversational AI and reliable system integration by using JSON Schema to enforce structured outputs in AI agents.
- While a single agent can effectively handle tasks within a specific domain, it often falls short when dealing with scenarios that involve multiple data sources, actions, or decision points. In this post, I will explore the building blocks of multi-agent solutions and why they’re important for creating smarter, and more scalable AI workflows.
- A Virtual Assistant can assist users in answering questions, providing information and performing tasks. In the past, the virtual assistants were built using predefined rules and templates. This approach posed limitations to the number of tasks that the virtual assistant could perform and the quality of the responses it could generate.