Navigating the AI Agent Tech Stack

There is a lot to consider when developing an AI agent.  Luckily, understanding the AI agent tech stack can be a huge help to developers who are looking to build successful agents.  The tech stack is a layered web of tools that help agents adequately act, reason, react and evolve.  Each layer serves its own purpose, while also working in tandem with other layers to achieve a greater goal.

The first step in the tech stack is data collection and integration.  This layer acts as the foundation to AI agent development.  It involves high quality, real-world, real-time and often unstructured data, all of which is used to teach the AI agent about its surroundings.  In order to act reliably, an agent needs to be able to understand the world and context around it, making it vital to invest in sufficient data.

There are many ways to introduce data to an agent.  Developers may train a model, use retrieval-augmented generation (RAG) systems or build an agent so it has the capacity to respond to live market changes.

Services like Bright Data can help with the data collection and integration layer of the tech stack.  More specifically, Bright Data provides the tools and scaffolding that allows AI developers to access the public web efficiently.  It does so through the use of search API, unlocker API, web scraper API, custom scrapers, dataset marketplaces and AI annotations.

Once developers have used the data collection and integration layer to input important and relevant data into the agent, they can address the rest of the tech stack.  There are several layers involved in the full stack.

Vertical agents are one example.  This layer involves pre-built agents that assist in specific industries or tasks.  Several programs help with this layer.  For example, a developer may want to use Adapt, Factory, MultiOn or Lindy to manage vertical agents.

Another important layer is agent hosting and serving, which is composed of platforms that host, manage and deploy AI agents.  This is the layer in which the agent comes to live, beginning to operate in dynamic ways.  Useful programs related to this layer are LangGraph, Hugging Face and AWS.

Observability is critical to the success of agents, and is another piece in the tech stack.  Observability entails real-time monitoring and debugging.  This helps keep AI agents transparent and reliable.  Observability is especially important as agents become more autonomous; tracing decisions and debugging problems is key to success.  Many developers use LangSmith, Weights & Biases and WhyLabs to address observability.  Each of these services has its own speciality.  For example, LangSmith traces and debugs LLM-powered workflows.  WhyLabs, on the other hand, observes data drift and model anomalies in the production process.

The next layer is agent frameworks.  These frameworks are libraries that are designed to build and manage AI agents.  Agent frameworks determine how agents are structured and how they function and reason.  Like the other layers, there are specific services that specialize with agent frameworks.  Common programs include Crew AI, LangGraph and DSPy.

Developers will also want to consider memory, as agents need to be able to retain context, interactions and knowledge in order to stay up-to-date and accurate.  Memory is often powered by vector databases.  ChromaDB is a common tool when addressing memory, as it is lightweight and a good choice for local-first development.  Weaviate is helpful for larger projects, and is modular and ML-friendly.

When it comes to expanding an AI agent’s reach to the real world, tool libraries are essential.  These libraries help the agent interact with external systems, search engines and databases.  LangChain, OpenAI Functions and Exa can all help developers maximize tool libraries.

In order to run simulations and make decisions, AI agents need to write and run code.  Sandboxes are the perfect place for this, allowing agents to safely test codes.  Decisions are then made in the model serving layer.  This layer is where LLMs are hosted and accessed, allowing the agent to make decisions based on quality reasoning and language generation.  There are many tools available to developers when it comes to model serving.  Common services include OpenAI, Anthropic, Mistral, Groq and AWS.

Finally, storage rounds out the final layer in the tech stack.  This is where agents keep their data, knowledge and past decisions.  Good storage is vital when it comes to reproducing a decision, as well as staying compliant.  Additionally, having storage allows for improvement, as the agent can build on its past history to reach greater heights.  Amazon S3 is a popular tool for scalable object storage.  Google Cloud Storage and Vector DBs are also common tools.

The tech stack is complex and composed of many parts, but it is essential in the production of a reliable agent and should be familiar to developers.  A key takeaway of AI agent development is the importance of data, which is the foundation for the entire tech stack.  Each layer has its own use, and many services exist to assist developers navigate the tech stack.  Building knowledge about the tech stack will go a long way in success in the AI world.

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