The complete AI agent tech stack explained

AI agents are moving beyond simple chatbots to become autonomous systems capable of reasoning, using tools, accessing data, and completing multi-step tasks. But building a reliable agent requires more than choosing an LLM. The complete AI agent tech stack combines models, data, orchestration, integrations, infrastructure, security, and monitoring to turn an AI concept into a production-ready solution.

Understanding each layer is essential for companies investing in AI agents development and looking to create systems that deliver measurable business value.

1. Foundation models and LLMs

At the core of most AI agents is a large language model (LLM). Models from providers such as OpenAI, Anthropic, and Google provide the reasoning and language capabilities that allow an agent to interpret requests and generate responses.

However, the best model is not always the most powerful one. Cost, latency, context window, security, reliability, and use case requirements all influence the right choice.

This is where AI consulting can help businesses evaluate models and design an architecture aligned with their objectives.

2. Prompts and agent logic

An effective agent needs clear instructions and a structured decision-making process. Prompt engineering defines the agent's role, objectives, constraints, and expected behaviour.

More advanced AI agents also use reasoning loops and orchestration logic to decide which action to take next. Instead of simply generating an answer, the system can determine whether it needs to search a database, call an API, retrieve a document, or ask another agent for support.

For a deeper look at the fundamentals, explore what makes an AI agent successful.

3. Knowledge and data

LLMs have broad knowledge, but business agents usually need access to private and constantly changing information. Retrieval-augmented generation (RAG) allows an agent to retrieve relevant information from company data before generating a response.

This layer can include document ingestion, chunking, embeddings, vector databases, metadata, search, and data transformation pipelines.

Good data architecture is critical because even a sophisticated agent will produce unreliable results if the information it receives is incomplete, outdated, or poorly structured.

4. Tools and integrations

The ability to take action is what makes AI agents particularly valuable. Integrations connect agents to the systems where work actually happens.

Depending on the use case, an agent might interact with CRM platforms, ERP systems, Slack, Microsoft Teams, Jira, databases, websites, or internal APIs.

These connections allow agents to move from answering questions to completing workflows, such as creating tickets, updating records, retrieving information, or generating reports.

5. Orchestration and multi-agent systems

As workflows become more complex, one agent may not be enough. Orchestration frameworks coordinate different agents, tools, and processes.

A multi-agent architecture could include a research agent, an analysis agent, and a writing agent, with an orchestrator deciding how tasks should be distributed.

The architecture should remain as simple as possible, however. Adding agents increases complexity, latency, and potential failure points, so multi-agent systems should be introduced only when they provide a clear advantage.

6. Infrastructure and LLMOps

Moving from a prototype to production introduces another critical layer: infrastructure.

AI agents need scalable hosting, deployment pipelines, monitoring, version control, logging, security controls, and performance optimisation. They may also require autoscaling, model fallbacks, drift detection, and ongoing evaluation.

This is where AI Ops & Managed Services become particularly important. BySix supports production-grade AI systems with LLMOps capabilities across AWS, Azure, and GCP, helping organisations maintain secure, scalable, and reliable deployments.

7. Security, governance, and evaluation

Security cannot be added at the end of an AI project. AI agents may access sensitive company data and perform actions in business systems, making authentication, authorisation, data protection, audit trails, and compliance essential.

Evaluation is equally important. Teams should continuously measure accuracy, latency, cost, hallucination rates, tool-call success, and business outcomes.

A production agent is therefore not simply a model connected to an API. It is an engineered system with controls around every important interaction.

The complete AI agents stack in practice

A typical architecture can be viewed as seven connected layers:

LLM → prompts and reasoning → knowledge and RAG → tools and integrations → orchestration → infrastructure and LLMOps → security and evaluation

Each layer contributes to the overall reliability and business impact of the system. The right architecture depends on the workflow, data, integrations, risk profile, and expected scale.

Companies evaluating AI agents development should therefore start with the business problem rather than the technology. Identify the workflow, define the desired outcome, determine what the agent needs to know and do, and then select the appropriate components.

For practical examples, explore BySix's AI projects and use cases to see how production AI agents can be applied to customer support, e-commerce, product management, and other business workflows.

Build a production-ready AI agent stack with BySix

The success of AI agents depends on more than the underlying model. It requires the right combination of software engineering, data architecture, integrations, infrastructure, governance, and continuous optimisation.

BySix brings these capabilities together through AI agents development, AI consulting, and AI Ops & Managed Services, helping businesses move from AI strategy to production-ready solutions. If you are planning your next AI initiative, talk to BySix to design an AI agent stack built around your business goals.