The tech stack is a vital part of AI agent development, but it can be confusing.  There are many moving parts to the tech stack, each with its own unique purpose.  However, once you master the tech stack and understand the intricate ins and outs of each layer, it is much easier to create a reliable agent.

The AI Agent Tech Stack 

Before diving into the specifics of each layer, it is important to understand what the tech stack is.  The AI agent tech stack is a layer of tools that enable AI agents to compute, act and react.  AI agents can take several forms, whether it be an autonomous research assistant to an agent that manages larger workflows. 

When considering the tech stack, it is important to start at the foundational layer: data collection and integration.  This layer compiles and disseminates data to the AI agent, helping the agent develop a critical understanding of the world in which it operates.  Without data collection and integration, the agent would not be able to make informed decisions.  

For each of the layers, there are services that can help developers effectively implement the right tools.  For the data collection and integration layer, developers may use something like Bright Data.  This platform helps developers access the public web at a large scale, obtaining precise data that can easily transfer to the agent. 

Conclusion

Agent hosting services make up another important layer.  This layer is where the agent comes to life.  Agent hosting services provide valuable infrastructure in which AI models can elevate from static models to autonomous systems.  LangGraph is a popular tool within the agent hosting services layer, allowing developers to build stateful and multi-step agent workflows.  Developers also often use platforms like Hugging Face and AWS when working on this specific layer. 

Another piece of the tech stack is observability.  This layer ensures that agents are transparent, traceable and reliable.  Developers need to know what an agent is doing, as well as why it is making certain decisions.  This not only builds trust and helps inform better decisions moving forward, but also reinforces safety. 

In order to create smarter and more capable agents, developers should turn to the agent frameworks layer.  Here, developers can define how agents interact with tools, collaborate with other agents and reason.  This is vital as agent complexity increases because developers may need new frameworks to accommodate multi-agent systems and dynamic planning. 

Memory and storage are also parts of the tech stack.  These layers are key when it comes to retaining content, history and knowledge.  Memory is useful for personalization and complex reasoning, and storage is important for reproducibility and compliance.  When working with these layers, developers cite platforms such as Vector DBs (Qdrant and Weaviate), ChromaDB, Google Cloud Storage (GCS) and Amazon S3 as particularly useful. 

The final layers of the tech stack are tool libraries, sandboxes and model serving.  Tool libraries allow agents to act in the real world, connecting with external systems and databases.  Sandboxes are spaces in which agents can execute code in a safe and isolated environment.  This permits agents to test ideas and make dynamic and reliable decisions.  Finally, model serving is the layer in which the decisions are made.  LLMs are hosted and accessed within the model serving layer, and provide the agent with the fuel for decisions.  Common and useful platforms for these layers include LangChain, OpenAI Functions, Exa, Replit, Modal, Anthropic (Claude), Mistral and more.

For each of these layers, data is a vital necessity.  With this in mind, it is important to use high quality data when developing AI agents.  Dedicating careful attention to the data collection and integration stage can be the difference between success and failure in the world of agent development. 

One way to conceptualize the tech stack is by considering the two sides to how an agent functions: the data and the model.  The data makes up what the agent knows, whereas the model is how the agent thinks.  Each part of the tech stack helps with these functions.  As a whole, the tech stack can help create innovative agents. 

Understanding the tech stack is imperative for AI agent developers.  Starting with data collection and integration is a good first step, as this data informs the agent’s worldview and decisions.  Taking advantage of useful platforms can streamline the process as well.  Although it may appear complicated at first glance, the tech stack is key to easier agent development.