IntroIntro

AI agents are software systems that perform tasks, make decisions and interact with other systems by using reasoning and tool use. Unlike traditional software, agents can dynamically adapt their behavior based on input, context and goals.

For example, a customer support agent might receive a message like “I want a refund, ” determine the required steps, call an internal API to retrieve the order, check refund eligibility and respond with a resolution. All of this happens dynamically, without hard-coded instructions.

Agents are already used in enterprise settings to process support tickets, review contracts and automate workflows in finance, legal and sales. But once they’re deployed to production and begin to scale, new challenges start.

Production-grade agents must handle real users, unpredictable traffic and complex edge cases. They also need to integrate seamlessly with existing infrastructure. This is where most setups break down.

Teams often run into issues like:

• No visibility into cost or usage patterns
• Silent failures or hard-to-debug behaviors
• Lack of evaluation tools to track and improve performance
• Difficulty integrating agents into CI/CD or monitoring pipelines

Even well-designed agents can fail without solving these core problems.

To go from prototype to scalable agent, teams need four key components: LLMs, agent frameworks, evaluation methods and memory.

This article breaks down each piece and explains how to build reliable, production-grade AI agents for enterprise scale.

Core stack for building production-ready AI agentsCore stack for building production-ready AI agents

These four building blocks need to come together for production-ready AI agents, reliable LLMs for reasoning and generation, the right agent frameworks for orchestration, systematic evaluations for quality assurance and memory systems for persistent intelligence.

To work reliably at scale, AI agents need more than just a good model. AI agents rely on a small set of building blocks that support everything from task execution to ongoing improvements. These include:

• A strong model for handling inputs and generating responses
• A framework that connects the agent to tools and services
• A way to test and improve agent behavior
• A memory system that helps the agent retain useful information and contexts over time.

Each of these plays a specific role in helping teams move from simple demos to production-ready agents that are stable, cost-efficient and easy to maintain. We’re also going to discuss these additional features mentioned on the diagram above later

LLMsLLMs

Large Language Models (LLMs) act as the reasoning core of AI agents. They interpret goals, break down instructions, decide which tools to call and generate responses based on the current context. The LLM powers the agent’s ability to plan, adapt and operate in dynamic environments.

The effectiveness of an agent depends heavily on how well the LLM can understand input, maintain context and execute logic. A strong model means better decisions, fewer errors and more reliable outcomes.

An LLM runtime is the infrastructure layer that executes models at scale. It manages inference, handles latency, supports tool use and integrates with backend systems. A powerful LLM is not enough — teams also need a runtime that is fast, cost-efficient and reliable under load. Without a proper runtime, agents might risk delays, errors and failures. A good runtime ensures consistent performance, high throughput and seamless integration with CI/CD pipelines, APIs and observability tools.

The biggest challenge isn’t choosing the most capable model, it’s about getting fast, stable and affordable access to it.

Nebius AI StudioNebius AI Studio

AI Studio was built to avoid trade-offs between speed and cost, to eliminate rate limits that interrupt workflows and to enable scaling without excessive spending — in short, to avoid problems that often arise in the industry.

It gives teams access to more than 30 open-source AI models in one place, including Llama, Mistral, Flux, DeepSeek, Qwen and others. You can generate text, create images, embeddings and run multimodal tasks without switching platforms. It also supports tool call for various LLM models, which is needed for AI agents.

There are two pricing tiers designed for different needs:

• The fast tier gives sub-2-second response times, useful for live apps and chat systems.
• The base tier cuts costs in half for workloads where speed doesn’t matter as much.

According to Artificial Analysis benchmarks, AI Studio models hit competitive performance targets while keeping costs significantly lower than most alternatives. For example, the Fast option delivers output tokens at just $0.38 per million tokens for Qwen2.5 72B LLM, while generating 70+ tokens per second.

Source

AI Studio also works with existing tools out of the box. Our OpenAI-compatible API means teams can switch without rewriting their applications — just update the endpoint and API key. Default limits are high enough for production use, with support for up to 400K tokens per minute (tpm) by default, up to 200K–100K tpm per model, with room to scale further with custom limits.

Check out the rate limits here.

LiteLLMLiteLLM

LiteLLM acts as a universal adapter between agent frameworks and model providers. It lets you use a single OpenAI-compatible interface to call many different models, including Nebius, without having to rewrite your code for each one.

This unified interface is mainly useful when you’re building with OpenAI-style clients, but want the freedom to route calls across providers without losing compatibility or adding complexity.

To use AI Studio through LiteLLM, set your model name by using the nebius/ prefix.

Example:

from litellm import completion
import os

os.environ["NEBIUS_API_KEY"] = "your-nebius-key"

response = completion(
    model="nebius/Qwen/Qwen3-235B-A22B",
    messages=[{"role": "user", "content": "Explain the health benefits of apples."}],
    max_tokens=150,
    stream=False,
)
print(response)

With LiteLLM:

• You can switch to Nebius models by using the same API interface you already use with other model providers

• It supports text, chat, streaming and embeddings in a unified way.

• Since LiteLLM integrates natively with LangChain, LangGraph, CrewAI, Google ADK and other agent frameworks, you can build complex agent systems and switch model providers seamlessly as needed.

If you want to explore real-world implementations, check out this example repo showcasing LiteLLM integration with Nebius models and the Google Agent Development Kit (ADK), or explore the official Nebius AI Studio’s LiteLLM integration docs for a complete setup guide.

FrameworksFrameworks

A language model alone isn’t enough to run production-grade agents. To execute multi-step tasks, use external tools and recover from failures, you need an agent framework that can manage orchestration, error handling and integration with external systems.

Click to expand. Framework approaches: Structured workflows vs. autonomous agents (Source: LangGraph documentation)

Let’s look at some of the popular agent frameworks.

CrewAICrewAI

CrewAI makes it easier for teams to design agents. Instead of creating complex logic, you set up roles like researcher, writer or reviewer, and CrewAI manages their collaboration.

It includes an orchestration layer called Flows that manages event handling, task dependencies and agent coordination. The framework also supports runtime observability through integrations with tools like AgentOps and OpenLIT, helping teams monitor decision paths and memory usage.

If you’re curious how it works with Nebius, check out the LLM integration page.

Google ADKGoogle ADK

ADK powers Google’s own production agents like Agentspace. It’s built for large systems where multiple agents need to coordinate, with native support for the Agent2Agent (A2A) protocol, real-time streaming and built-in evaluation tools. With LiteLLM support, it works across model providers. We’ve tried implementing ADK agents with AI Studio and it worked smoothly — check this GitHub repo for example demos. It’s ideal for teams working at scale inside enterprise environments.

AgnoAgno

Agno is a full-stack framework purpose-built for developing sophisticated multi-agent systems that operate at scale. Whether you’re building a single reasoning agent or coordinating entire agent teams, Agno provides the tools to do it efficiently.

It supports five levels of agentic complexity, from tool-using agents to collaborative agent workflows with memory, state and determinism. If you’re planning to use AI Studio models with Agno, check this official page for example usage.

LangChainLangChain

LangChain is one of the most popular frameworks for building LLM-powered applications, from chat agents to full-fledged RAG pipelines and autonomous workflows. It now includes official integration with AI Studio models, meaning you can use any of Nebius’s open-source LLMs, like DeepSeek, Qwen and more, just like you would with OpenAI or Anthropic. With just a few lines of configuration, you can switch model providers, plug in new tools and scale agent workloads, all while using similar methods

You can seamlessly plug AI Studio into existing LangChain pipelines for chat, embeddings, retrieval or tool use, without major refactoring. Whether you’re building copilots, knowledge agents or orchestration flows, the Nebius-LangChain integration gives you flexibility and scale, with minimal overhead.

There are many other frameworks available, such as LlamaIndex and Strands Agents. You can pick frameworks based on your requirements.

EvaluationEvaluation

Testing agents isn’t like testing traditional software. You won’t always get the same answer twice, which makes it harder to spot when something’s gone wrong.

That’s why evaluation needs to be built into the system, not just for testing features. Without it, agents can get stuck in loops, make poor decisions or quietly break workflows in ways that are hard to detect.

A solid evaluation checks:

• If the task is routed to the right agent
• If the response is accurate or helpful
• If the agent avoids repeated mistakes or dead ends

One effective approach is to use other language models to review outputs. It’s faster than manual checks and scales better. Rotating between multiple reviewers helps reduce bias.

AI Studio adds real value here. Its flexible pricing and support for a wide range of models (from lightweight Llama 3.1–8B to large-scale Qwen3) lets teams test agents across different scenarios, without overspending. You can run faster tests with smaller models and use larger ones for higher accuracy where needed.

If you want to go deeper, AI Studio also has a support for Helicone and Keywords AI, a powerful layer for tracking token usage, response time and model behavior across agents. These integrations help teams track, debug and continuously improve agents in production.

Memory systemsMemory systems

One common limitation with agents is memory and context, or rather, the lack of it. Without proper context, every interaction starts from scratch. This makes agents feel robotic and forces users to repeat information again and again.

To make agents useful for longer tasks or multi-step workflows, they need a way to remember past context, decisions and preferences.

Vector databases and memory systems solve this by helping agents retain and retrieve relevant information across different conversations and tasks.

• Vector databases: Vector databases store and retrieve information based on meaning, not just keywords, making them essential for memory in AI agents. Qdrant is a high-performance vector database that helps agents remember things by storing data as vector embeddings, meaning the agent doesn’t just store what was said, but what it meant. It’s built for performance at scale, with optimizations like scalar and binary quantization. These features reduce memory usage and speed up retrieval, even when working with millions of entries.

• Smart memory: Qdrant stores information, but it won’t decide what’s important. Mem0 addresses this by filtering out irrelevant data and keeping only the useful parts, so agents don’t end up overloaded with unnecessary history. Instead of dumping the full chat log into the prompt each time (which gets expensive and slow), Mem0 extracts key facts and updates memory intelligently. Mem0’s graph-based approach also captures how facts connect, making long-term memory more structured and useful.

• Built-in structured memory: Letta is a another open source framework for building stateful agents, and it takes memory even further. It lets you create and launch AI agents that remember and keep context during long conversations. Build agents that learn and grow from interactions without starting over each time.

Letta’s advanced context management system, created by the researchers behind MemGPT, changes how agents remember and learn. Unlike simple agents that forget when their context window is full, Letta agents keep memories across sessions and keep getting better, even when they sleep.

Other featuresOther features

Modern production-ready agents can be extended with features like real-time web search, file and document processing, and API or tool calling for dynamic actions. You can also integrate multi-modal inputs such as images or PDFs, and design multi-agent workflows where agents collaborate with distinct roles. Adding human-in-the-loop reviews ensures reliability in critical tasks. These advanced capabilities make your AI agents more versatile, reliable and aligned with enterprise needs.

To provide agents with access to the latest information from the web, you can use search tools like LinkUp. LinkUp is a web search API specifically designed to connect AI applications to the internet, providing real-time, factual information.

Enable real-time web search in your AI agents by integrating LinkUp through function calling. This setup works seamlessly with all text generation models on Nebius, allowing agents to retrieve up-to-date, factual information when needed.

Example snippet to ground AI Studio LLMs with latest web data via LinkUp:

import os, json
from openai import OpenAI
from linkup import LinkupClient

# create the two API clients
llm = OpenAI(
    base_url="https://api.studio.nebius.ai/v1/",
    api_key=os.environ["NEBIUS_API_KEY"],
)

linkup = LinkupClient()

# function call, execution and results...

This tool will enable AI agents to ground responses with real-time data. For implementation details, refer to the official documentation or explore this agent-example that demonstrates LinkUp search integrated with AI Studio models.

Connecting AI Studio to your agent frameworkConnecting AI Studio to your agent framework

With the foundational components understood, teams need to integrate AI Studio with their agent frameworks. Our OpenAI-compatible API makes this straightforward.

Drop-in replacementDrop-in replacement

The simplest integration path requires changing only your base URL and API key to switch from OpenAI or any compatible provider:

  import openai
  import os

  # Replace OpenAI with AI Studio - change 2 lines
  client = openai.OpenAI(
      api_key=os.environ.get("NEBIUS_API_KEY"),
      base_url='https://api.studio.nebius.com/v1'
  )

  completion = client.chat.completions.create(
      messages=[{
          'role': 'user',
          'content': 'What is the answer to all questions?'
      }],
      model='meta-llama/Meta-Llama-3.1-8B-Instruct-fast'
  )

Framework examplesFramework examples

Each major framework handles an AI Studio integration slightly differently, but they all follow the same basic pattern.

CrewAICrewAI

Crew AI accepts OpenAI-compatible endpoints directly through its LLM class configuration:

from crewai import LLM
import os

llm = LLM(
    model="nebius/Qwen/Qwen3-30B-A3B"
)

Google ADKGoogle ADK

Google’s Agent Development Kit (ADK) is designed for building reliable, production-grade agents that can collaborate, hand off tasks and operate at scale. It comes with built-in streaming, native evaluation tools and structured orchestration. It’s a strong choice for enterprise-grade systems that need reliability across multi-agent workflows.

Example setup with AI Studio through LiteLLM:

from google.adk.agents import Agent
from google.adk.models.lite_llm import LiteLlm
import os

# Initialize AI Studio model through LiteLLM
model = LiteLlm(
    model="openai/meta-llama/Meta-Llama-3.1-8B-Instruct",
    api_base=os.getenv("NEBIUS_API_BASE"),
    api_key=os.getenv("NEBIUS_API_KEY")
)

# Create agent with AI Studio model
agent = Agent(
    name="MyAgent",
    model=model,
    description="Agent powered by Nebius AI Studio"
)

For a real-world use case, check out the Job-Finder Agent, a full AI job search pipeline built entirely using ADK.

AgnoAgno

Agno includes Nebius AI Studio as a natively supported model provider in its recent releases. The direct integration means you get optimized performance without any wrapper overhead.

from agno.models.nebius import Nebius
import os

model = Nebius(
    id="Qwen/Qwen3-30B-A3B",
    api_key=os.getenv("NEBIUS_API_KEY")
)

PydanticPydantic

Pydantic AI uses a provider pattern that separates the model interface from the underlying service. This design lets you swap between different providers while keeping the same agent code.

from pydantic_ai.models.openai import OpenAIModel
from pydantic_ai.providers.openai import OpenAIProvider
import os

model = OpenAIModel(
    model_name='meta-llama/Meta-Llama-3.1-70B-Instruct',
    provider=OpenAIProvider(
        base_url='https://api.studio.nebius.com/v1/',
        api_key=os.environ['NEBIUS_API_KEY']
    )
)

OpenAI Agents SDKOpenAI Agents SDK

OpenAI Agents SDK accepts any OpenAI-compatible endpoint through its AsyncOpenAI client. The framework treats AI Studio like any other OpenAI-compatible provider, so existing code works without modification.

from agents import OpenAIChatCompletionsModel, AsyncOpenAI
import os

model = OpenAIChatCompletionsModel(
    model="openai/meta-llama/Meta-Llama-3.1-8B-Instruct",
    openai_client=AsyncOpenAI(
        base_url="https://api.studio.nebius.ai/v1", 
        api_key=os.getenv("NEBIUS_API_KEY")
    )
)

LlamaIndexLlamaIndex

LlamaIndex provides native AI Studio integration through a dedicated LLM class. This eliminates the need for OpenAI compatibility layers and gives you direct access to Nebius-specific features.

from llama_index.llms.nebius import NebiusLLM
import os

llm = NebiusLLM(
    model="Qwen/Qwen3-235B-A22B",
    api_key=os.getenv("NEBIUS_API_KEY")
)

Cost optimizationCost optimization

Depending on how fast or cost-efficient your application needs to be, AI Studio offers two performance tiers:

# Fast tier: Real-time applications
model='meta-llama/Meta-Llama-3.1-8B-Instruct-fast'

# Base tier: Batch processing (50% discount)
model='meta-llama/Meta-Llama-3.1-8B-Instruct'

The fast tier is built for interactive use, live-chat interfaces, agent feedback loops or real-time decision flows.

The base tier is better suited for non-urgent tasks like large-scale content generation, evaluation runs or data processing, offering up to 50% cost savings automatically.

The reality of deploying production agentsThe reality of deploying production agents

Agent demo apps work great until you ship them to real users, and then everything breaks. This happens to most developers deploying AI agents because users ask questions you never thought of, and agents give confident, wrong answers by default.

Common agent failure modesCommon agent failure modes

Real users are unpredictable, and that’s what breaks agents.

Someone might paste 10,000 words into a chat box and burn through your monthly token limit in one go.

Or maybe your agent depends on an external API that suddenly fails, leaving users staring at a blank screen.

Agents that hold up in production environments aren’t just smart, they’re protected.

• Add input validation to avoid runaway costs or garbage inputs.
• Set confidence checks so agents say “I’m not sure” instead of guessing.
• Implement retry and fallback logic so API hiccups don’t crash the user experience.

The right agent frameworks provide a structure to apply these protections, without starting from scratch.

Agno comes with built-in debugging, event tracking and evaluation tools that help prevent and diagnose failures before they reach your users. You can stream reasoning steps in real-time, cap runaway tool calls and evaluate agents on accuracy, reliability and performance, all with a few lines of config.

LangGraph lets you design structured workflows with pause-and-resume points and fallback branches, ideal for gracefully recovering from tool errors or low-confidence responses.

Your application code handles validations and decisions, and AI Studio handles the reliable, scalable model execution behind it all.

Agent performance monitoringAgent performance monitoring

The biggest issues often don’t show up in test environments. Agents may freeze during peak traffic, deliver different answers to the same prompt depending on server conditions or become noticeably less accurate when multiple users interact at once.

Most teams spend their time optimizing response times while completely missing that their agents are starting to hallucinate more frequently under real-world conditions.

To spot these problems early, you need monitoring in place.

• Keywords AI offers real-time agent tracing, LLM logging and monitoring that can allow you to spot failure points as they occur. AI Studio models are supported via an official integration.

• Google ADK provides built-in evaluation tooling, letting teams measure both the final response quality and the agent’s full reasoning trajectory against predefined test cases. It also supports real-time streaming logs and traceable event flows that help debug behavior under load.

• Helicone is a powerful LLM observability tool that tracks everything, requests, tokens, tool calls, latency and even reasoning paths. Its session-based tracking lets you see exactly how agents think, act and fail across multi-step workflows. You can replay sessions, debug slowdowns, A/B test prompts and catch hidden errors fast. You can use Helicone with AI Studio-based models via our official integration.

• AgentOps is built for monitoring and debugging complex AI agents. With minimal setup, it gives you full visibility into agent workflows — tool calls, LLM usage, reasoning steps and failures. It supports frameworks like OpenAI Agents, CrewAI and LangChain, making it ideal for multi-agent orchestration and production-grade reliability. You can use AI Studio models as well via LiteLLM support.

Early visibility into performance issues means fewer surprises and fewer support complaints.

Human-in-the-loop implementationHuman-in-the-loop implementation

No matter how well you design your agents, there will be moments when they misfire, especially in high-stakes areas like finance, healthcare or legal services. One bad response in these domains can cost more than your entire automation budget.