🧱 The Agentic AI Infrastructure Stack: From GPUs to Protocols

December 9, 2025

4 min read

This post walks through each layer of an Agentic AI infrastructure stack and explains the core challenges, notable vendors, and practical considerations associated with building real-world Agentic Systems.

This visual representation may become outdated over time, as the Agentic AI ecosystem is evolving rapidly and new tools, platforms, and frameworks are continuously emerging.

⚙️ GPU / CPU Hardware

The challenge: Accelerated compute R&D, manufacturing, and supply.

Notable vendors and frameworks

R&D and manufacturing: NVIDIA, Groq, Google, AWS
Supply: NVIDIA, Groq, Google, AWS, Azure, Coreweave

Extra notes: With the increase in compute requirements for inference (both regular serving and test-time compute), even the big clouds are not capable of keeping up with demand.

🏗️ Base Infrastructure

The challenge: Efficient and scalable model deployment on single-node and multi-node clusters.

Notable vendors and frameworks

vLLM
Kubernetes
Slurm

Extra notes: Vendors providing both Proprietary and Open Model APIs are leveraging this infrastructure for serving the models for the public.

🧠 Foundation Models

The challenge: The need for general, task-specific, and multi-modal models increasingly capable of solving difficult problems with high precision.

Notable vendors and frameworks

OpenAI
Anthropic
Google
Mistral
Open Source community

💿 Data Storage

Internal data is considered to be the main differentiator for companies that build Agentic Systems nowadays.

The challenge: Most production-ready enterprise Agentic Systems rely on internal context available within the enterprise. There is a requirement for integration with a variety of data sources and efficient capabilities for retrieval of this data.

Notable vendors and frameworks

Qdrant
Weaviate
MongoDB

Extra notes: Vector databases are just a piece of the puzzle. Efficient information retrieval systems should be comprised of relational, graph, key value, document and vector databases. It all depends on the use case and as an AI Engineer you should not always default to vector.

Observability and Evaluation

⏿ 📊 Observability

The challenge: Agentic Systems are non-deterministic and can seem like black boxes from the outside. We need to be able to efficiently trace all of the actions that are happening within the application by instrumenting the code and then perform analytics on the traced data. Also, there is a need for LLMOps practice implementation like prompt versioning. It should also happen on the Observability layer.

Notable vendors and frameworks

LangSmith
Langfuse
Arize

Extra notes: Observability platforms often come with their own instrumentation SDKs. Some Evaluation as well as Versioning capabilities are part of these platforms too.

🧪 🔍 Evaluation

The challenge: Agentic Systems are non-deterministic; there is a need to manage exact and non-exact evaluation rules that would be run against the data produced by the system. It is the only way to make sure that the system you are building and evolving behaves as expected.

Notable vendors and frameworks

Ragas
Arize
Galileo

Extra notes: While the vendors do provide some out-of-the-box evaluations, in most cases you will have to define your own evaluation rules. Also, you can’t blindly rely on evals defined in different platforms — even though the naming of the eval rules can match, the implementation is most likely different.

Orchestration and Model Routing

⧡ Orchestration

The challenge: Agentic Systems often take form of complex non-deterministic chains of LLM or other GenAI model calls. There is a need for frameworks that would help developers quickly build these systems and manage the complexity as they are being evolved.

Notable vendors and frameworks

LangGraph
CrewAI
LlamaIndex

Extra notes: Very often simple wrapper clients like instructor are enough to start off without the need to adopt any dedicated LLM Orchestration Framework. Also, these frameworks hide some low level implementation details that you would want to tweak to achieve the best performance of your application so it might make sense to drop the framework when your application becomes complex enough. Having said that, frameworks like LangGraph are moving in the right direction by allowing low level tweaks if needed.

🚏 Model Routing

The challenge: Not all model APIs that you will be using will be stable enough to handle your production traffic. There is a need for a routing layer that would allow falling back to a different model provider if there are issues with the main one.

Notable vendors and frameworks

LiteLLM
OrqAI
Portkey

Extra notes: Properly switching between models is harder than it might look. As an example, a prompt that works for OpenAI might not work well for Claude family of models. Together with the fallback models you should configure fallback prompts. You should also closely tie Routing with Observability.

LLM Security, Agent Memory and Communication Protocols

🛡️ Security

The challenge: Real Agentic Systems have agency over some of our internal systems (e.g. data retrieval, automated ticket creation etc.). Malicious actors can manipulate natural language based interfaces to extract sensitive data or perform unintended actions within your infrastructure. We need safety guardrails that can prevent this and help us identify existing vulnerabilities.

Notable vendors and frameworks

splxAI
Lakera
WhyLabs

Extra notes: LLM Security can be split into multiple categories like: AI Application Red Teaming — continuous attempt to jailbreak your application; Guardrails — making sure that no unexpected data reaches an LLM or is exposed to the user of your application (e.g. PII data).

🧠 Agent Memory

The challenge: Effective reasoning and planning capabilities of Agentic Systems strongly rely on the actions that the system has already taken as well as on the context available to the organisations internally and externally. We are used to modelling this memory by splitting it into short-term and long-term. There is a need of a layer that helps efficiently manage and retrieve relevant memories on-demand.

Notable vendors and frameworks

mem0
Cognee
Letta

Extra notes: These memory layers are not just databases, but rather frameworks for efficient memory management and retrieval.

📡 Communication Protocols

The challenge: As we are entering the era of IoA (Internet of Agents) where AI Agents are distributed over the network and developed by different organisations, we need standards of how the communication between these systems should be handled.

Notable vendors and frameworks

MCP
Google - A2A Protocol
Agentcy

Extra notes: Open protocols for Agent communication are important but they are just a piece of the picture, there will be a need for standards that govern all of the existing protocols and other missing pieces. For example:

✅ Summary

Each layer in this Agentic AI stack solves a specific challenge: from compute supply and scalable deployment, to foundation models, internal data, observability, evaluation, orchestration, routing, security, memory, and communication protocols.

Together, these layers form the foundation required to reliably move from simple LLM applications to full Agentic Systems that can operate safely and autonomously in complex enterprise environments.