The AI Agent Tech Stack: Models, Frameworks and Tools
The building blocks a business AI agent needs: language model, framework, tool integrations, memory and monitoring, with notes on GDPR and operations.
In short: An AI agent has five layers: a language model, an orchestration layer that plans and runs steps, tools and APIs that connect it to your systems, memory for knowledge and history, and monitoring with tests. For most companies the choice of model matters less than clean integration with the CRM, ERP and data sources. Start with one narrowly scoped agent and log everything it does from day one, and you will reach production much faster.
How an AI agent differs from a chatbot
A chatbot answers questions. An agent completes tasks. It reads a request, decides what information it needs, calls tools to get it (a CRM search, a calendar API), evaluates the result and then acts. That loop of reasoning, acting and checking is the core of an agent, and it shapes the stack: besides the model, you need components that control the loop, constrain it and make it traceable.
For the business side of where agents fit, see our practical introduction to AI agents for business. This post covers the technology underneath.
The layers of an AI agent tech stack
The language model
The major commercial models from OpenAI, Anthropic and Google handle tool calling reliably and are available through APIs right away. Mistral is a European provider, and open-weight models such as Llama or Qwen can run on your own infrastructure with tools like vLLM or Ollama. For German companies the key question is often where data is processed. Azure OpenAI, AWS Bedrock and Google Vertex AI all offer EU regions, which makes the GDPR conversation much simpler.
In practice it pays to keep the model swappable. A cheap, fast model handles classification and routing, and a stronger model takes the hard decisions.
Orchestration and frameworks
Orchestration defines how the agent plans steps, calls tools and handles errors. Common frameworks include LangGraph (from the LangChain ecosystem), LlamaIndex with its focus on documents, the OpenAI Agents SDK, the Claude Agent SDK and the Vercel AI SDK for TypeScript projects. For many business processes a workflow tool like n8n is enough, combining AI steps with fixed rules. Our article on building intelligent workflows with n8n and AI APIs shows what that looks like.
Our rule of thumb: the clearer the process, the less freedom the agent needs. A defined workflow with a few AI steps is far easier to test than an agent that picks every step on its own.
Tools and integrations
An agent is only as useful as the systems it can reach. Tools are functions with a clear description that the model is allowed to call: find a customer, create an appointment, generate a quote. The Model Context Protocol (MCP) has become an open standard for exposing such tools in a consistent way. Give each tool only the permissions it needs. An agent that reads leads has no business deleting invoices.
Memory and knowledge
For company knowledge such as manuals, product data or contracts, retrieval-augmented generation (RAG) is the usual approach: documents are split into chunks, stored as vectors and retrieved when needed. If you already run PostgreSQL, the pgvector extension covers a lot of ground. Dedicated vector databases like Qdrant or Weaviate make sense at larger scale. On top of that comes short-term memory for the conversation and, where needed, long-term memory per customer.
Monitoring, tests and guardrails
This is the layer teams underestimate most. Tools like Langfuse (which you can self-host) or LangSmith record every model call, every tool call and the cost. Without those traces you cannot tell why an agent gave a wrong answer. Add automated tests built from real example cases, and guardrails: critical actions such as payments or customer emails go to a human for approval before they are sent.
Common mistakes when building agents
The most common mistake is starting with the most powerful framework before the process is clear. The second is giving the agent too many tools at once. Models choose more reliably between a few clearly named functions. The third is data protection. If personal data flows into the model, you need a data processing agreement with the provider, and once the agent makes decisions that affect people, a review under the EU AI Act.
Frequently asked questions (FAQ)
Which framework should we use for our first AI agent?
It depends on your team. If they work in TypeScript, the Vercel AI SDK is a lean start. For Python, LangGraph or the model providers' own SDKs are the obvious choices. For processes with many system integrations and little custom logic, n8n often gets to production faster than any code framework.
Can we run AI agents in a GDPR-compliant way?
Yes, if the architecture is designed for it. Use EU regions or self-hosted models, sign data processing agreements and give the agent only the data it needs for the task. Logs should mask personal data or delete it after a fixed retention period.
Do we need a dedicated vector database?
Usually not at the start. If you already use PostgreSQL, pgvector handles many use cases. A dedicated vector database becomes worthwhile when you search very large document sets or need special filtering and scaling.
How do we stop an agent from taking wrong actions?
Narrow tool permissions, approval steps for critical actions and complete logging. Also maintain a set of real test cases and run them automatically after every change to the prompt or the model.
Conclusion
A good AI agent comes less from picking the newest model and more from clean integrations, tight permissions and monitoring from the first day. With those foundations in place you can swap models later without rebuilding the agent. If you are planning a first agent for a specific process, we can help with AI automation and agent development.
Related posts
- AI Agents for Business: A Practical Introduction
How AI agents differ from chatbots and classic automation, where they help small and mid-sized companies, and how to introduce a first agent safely.
- Workflow automation with n8n: a getting started guide
Getting started with n8n: cloud or self-hosting, triggers and nodes, a first workflow, error handling and what to settle before you run it in production.
- Building custom AI chatbots: from GPT prototype to production
What makes an AI chatbot production-ready: grounding with RAG, guardrails, human hand-off, test sets, monitoring and GDPR for business use.
- LLM integration for enterprise: architecture and best practices
How to integrate language models into business software safely: architecture, RAG, structured outputs, data protection, cost control and quality assurance.