Shape Successful AI Agents With The Tech Stack

Tuesday, 19 August 2025, 8:44 am
Article: Hugh Grant

The AI agent tech stack is a powerful tool. Made of several tools and systems, the tech stack provides the necessary frameworks to develop a reliable agent. To do so, it supplies the agent with data, infrastructure, memory and more. Each layer has its own role to play, and when brought together, the layers form a well-oiled machine, shaping successful agents.

Data collection and integration forms the foundation of the tech stack. Without this layer, the agent cannot function properly. Having access to real-world and real-time data allows the agent to understand the world in which it operates. This then informs the decisions and actions that the agent takes moving forward.

Platforms like Bright Data can help developers effectively utilize the data collection and integration layer. Bright Data provides infrastructure that allows agents to access the public web at scale, while still being precise and compliant with regulations.

Next up in the tech stack are vertical agents, which are pre-built AI agents that excel with specialized industries and tasks. There are many services that assist with vertical agents. For example, developers may turn to platforms such as Adapt, Cognition, Dosu, Factory or MultiOn.

Another important layer is composed of agent hosting services. These services are platforms that provide infrastructure in which the agent can reason, make decisions and take action. This is the layer in which static models transform into dynamic and autonomous systems. Developers may use programs such as LangGraph, Hugging Face or AWS when working with agent hosting services.

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Observability is another key part of the tech stack, and is the tool that makes agents transparent, traceable and trustworthy. It does so by helping developers monitor agent performance, keep track of decisions and fix any issues that may arise.

As agents get more complex, agent frameworks become increasingly important. This layer of the tech stack defines how agents work, whether it be their structure, their methods of reasoning or the ways in which they collaborate with other agents. These frameworks can support increased complexity, such as multi-agent systems and dynamic planning.

Memory and storage are also important when it comes to agent success. Memory systems assist with context retention, which helps to build long-term understanding. This in turn allows the agent to learn from past mistakes and make well-informed decisions moving forward. Similarly, storage is where agents keep their past and present knowledge. More specifically, these systems support long-term persistence logging interactions, saving outputs and maintaining state across sessions. Both of these layers are imperative to the continuous improvement of the agent.

Tool libraries are the layer in which agents take action in the real world. With the help of the tool library, AI agents can interact with external services, such as databases and search engines. Common platforms that can help with tool libraries are LangChain, OpenAI Functions, and Exa.

Finally, sandboxes and model serving form the remainder of the tech stack. These layers are where the decisions are formed and made. Sandboxes are safe and isolated environments in which the agent can write and run code. This allows the agent to test ideas in a reliable environment. Model serving is where LLMs are hosted, which provides the tools needed for each decision. Developers cite Anthropic, Mistral, Groq, OpenAI and AWS as helpful services for model serving.

Each layer of the tech stack plays an important role, but developers must remember that quality data is the foundation of the entire tech stack. Starting with a strong foundation of real-world data will go a long way in effective AI agent development. After the foundation is laid, developers can focus on the rest of the tech stack, taking advantage of useful services and tools as they work. The tech stack is an incredibly useful guide to building successful AI agents.

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