How To Build Autonomous Agents
Posted: Thu Feb 13, 2025 3:27 am
From a technology perspective, there are five elements that go into autonomous agent designs: the agent itself, for processing; tools, for interaction; prompt recipes, for prompting and planning; memory and context, for training and storing data; and APIs / user interfaces, for interaction.
The agent at the center of this infrastructure leverages one or more LLMs and the integrations with other services. You can build this integration framework yourself, or you can bring in one of the brazil whatsapp number data existing orchestration frameworks that have been created, such as LangChain or LlamaIndex. The framework should provide the low-level foundational model APIs that your service will support. It connects your agent to the resources that you will use as part of your overall agent, including everything from existing databases and external APIs, to other elements over time. It also has to take into account what use cases you intend to deliver with your agent, from chatbots to more complex autonomous tasks.
Existing orchestration frameworks can take care of a lot of the heavy lifting involved in managing LLMs, which makes it much easier and faster to build applications or services that use GenAI. For example, LangChain provides a popular open-source framework to build applications around LLMs by standardizing connections to other elements like prompt management, vector data stores, and other tools.
The agent at the center of this infrastructure leverages one or more LLMs and the integrations with other services. You can build this integration framework yourself, or you can bring in one of the brazil whatsapp number data existing orchestration frameworks that have been created, such as LangChain or LlamaIndex. The framework should provide the low-level foundational model APIs that your service will support. It connects your agent to the resources that you will use as part of your overall agent, including everything from existing databases and external APIs, to other elements over time. It also has to take into account what use cases you intend to deliver with your agent, from chatbots to more complex autonomous tasks.
Existing orchestration frameworks can take care of a lot of the heavy lifting involved in managing LLMs, which makes it much easier and faster to build applications or services that use GenAI. For example, LangChain provides a popular open-source framework to build applications around LLMs by standardizing connections to other elements like prompt management, vector data stores, and other tools.