In a recently covered live stream featuring Shivay Lamba, Developer Evangelist at Couchbase, and Dylan Bristot, New Product Marketing Manager at Nebius AI Studio. They covered what are the building blocks for building AI agents in production.
Building and deploying AI agents in production requires a clear understanding of what AI agents are, how to choose the right tools and LLMs for the agentic application, and how to bring in security and observability for proper monitoring of the agent responses.
Watch: Building Production-Ready AI Agents With Couchbase and Nebius AI
What is an AI Agent?
An AI agent is a system powered by large language models (LLMs) that can take actions and act autonomously. Unlike a traditional LLM that only provides a response in text, AI agents understand context, use tools, connect with external data source, and remember past interactions via memory. They can take action on a user’s behalf. They can book meetings, research prospects, or even draft proposals.
AI agents can run fully autonomously with a set of tools available to them in order to perform long running tasks. AI agents have two jobs: they need to interact with people and then they must perform tasks. When talking to humans, AI agents can handle unstructured input, derive context, and explain concepts in human language. Taking the information from humans, AI agents then perform tasks for the human by calling APIs, learning from errors, and sometimes working with no human supervision. They orchestrate multiple steps to complete tasks in loops.
This combination of LLM plus the tools, memory and goals is what gives agents the capability to do more than just generate text.
Core building blocks of AI Agents
대규모 언어 모델(LLM)
LLMs can be considered as the brain of an AI agent. LLMs process inputs, generate responses, and make decisions about what to do next.
Tools and APIs
LLMs by themselves can only generate text. To interact with databases, APIs or any external process, they need the ability to interact with tools. For example, an agent planning travel might use a flight booking API or weather service.
메모리
Agents break work into steps (plan → search → call API → parse → write). Without memory, they lose track of multiple steps. They often repeat tool calls or fetch the same data again.
They forget preferences or rules (“always write tests”). If something fails, they can’t recover. They just start over. This means agents waste model tokens, take longer, and lead to inconsistent results.
In order to solve these issues, agents use memory. Using short- and long-term memory, agents automatically remember context (like tools you use, projects you’re on, people you work with), reduce token usage and costs by skipping repeated backstory
And they give consistent, personalized responses.
Orchestration and Reasoning
Agents need to manage workflows. This means deciding which tools to call, how to use past information, and how to adapt based on results or new inputs. Some agents use reactive architectures to think step-by-step or advanced reflection loops to evaluate and improve their responses.
Observability and Logging
Because agents can act autonomously, it’s extremely important to monitor what decisions they make at each step of their execution, and how accurate they are because of their non deterministic nature. Telemetry tools help keep track of agent health, debug failures, and gather analytics to improve performance.
Designing production AI Agents: best practices
Choosing the right LLM
The choice of LLM is extremely crucial because it affects the speed, reasoning quality, and the ability to use tools or handle multimodal inputs.
Here are some of the considerations when choosing the LLM:
Fast vs deep reasoning: Smaller models excel at quick retrieval; larger ones power complex multi-step reasoning logic.
Structured output formats: JSON or function-call style allows for easier tool integrations and validation.
Multimodal needs: Prepare for PDFs, images, voice—pick models that can multiple types of input formats.
Dylan showcased a demo of the Nebius AI Studio which offers a unified API to access multiple open-source LLMs. Users can fine-tune models without worrying about managing servers, and also control data privacy by opting out of data retention.
Monitor and secure your agents
AI agents make decisions that affect users and business outcomes. Observability tools track what’s happening under the hood. This includes performance metrics, decision logs, and error reports.
Security is also extremely important. It is extremely crucial to deploy AI agents in compliance with universal standards like GDPR and SOC 2. It is equally important to maintain data-retention policies that respect customer data privacy. Hosting models close to the data (for example storing the model in the same VPC as the data) reduces the latency and improves security of the agentic system.
Fine-grained access control ensures only authorized users and processes can use specific agent features or tools.
Agentic design patterns
There are different architectural patterns when designing AI agents. These patterns enable developers to build agentic systems to become capable of dynamic reasoning, tool orchestration, memory utilization, and multi-agent coordination.
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- Reactive agents: Think through actions methodically and adapt as new information comes in.
- Tool use agents: Improve LLM’s capability by allowing it to interact with external tools and resources to enhance its problem-solving abilities
- Reflection agents: Evaluate their own outputs, improving through self-feedback loops.
- Multi-agent collaboration: Multiple specialized agents communicate, dividing complex problems into parts.
Operational best practices
One should use structured output formats (e.g., JSON) for better tool interoperability. AI agents may have 100s of prompts and tools, which will evolve over time and be versioned controlled.
Managing them will be a huge challenge. Couchbase 상담원 카탈로그 is designed to help developers keep track of the tools, functions and system prompts they build to interact with various data sources, data types and different models.
Another big change with working with LLMs vs traditional applications is that the LLM responses can change over time. Often referred to as drifting. Agent catalog also stores detailed transcriptions of each prompt and conversation between the LLMs and the agents, with information on the prompts and tools used. This enables easier forensics via audit logs.
Demo use cases highlighted
Low-code agent-building with platforms like n8n which provides an intuitive low code platform with drag-and-drop workflows to automate tasks and running AI Agents.
Custom orchestration (parallel and sequential execution) of tools and agents for data gathering and synthesis.
결론
The speakers emphasized that while AI Agents unlock advanced reasoning capabilities, the key to productionization requires careful LLM selection, appropriate AI agent design pattern, secure data handling, and robust observability. Platforms like Nebius AI studio provide you with the capability to choose the appropriate open source LLM of your choice and Couchbase proves to be the critical database platform for critical agentic AI applications.
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