An OpenAI Guide to Building Agents with LLMs
With the evolution of large language models (the well-known "LLMs"), we're entering a new era of intelligence-based automation.
More than just answering questions or completing sentences, these systems can now execute entire workflows with autonomy and adaptability. This brings us to the rise of agents — LLM-driven systems capable of reasoning, deciding, and acting in an iterative way.
Recently, OpenAI released "A Practical Guide to Building Agents", offering an in-depth look at best practices, architectural components, and operational challenges in building intelligent agents.
In today’s article, we’ll break down the key points from that guide, exploring how to architect, orchestrate, and secure LLM-based agents.
What is an AI agent?
An agent is a system that autonomously performs tasks on behalf of a user.
Unlike traditional software that automates processes based on fixed rules, agents can make decisions, recover from errors along the way, and interact with multiple systems through tools and APIs.
In short, an AI agent is an autonomous application with the ability to:
Make contextual and adaptive decisions
Execute multi-step workflows
Interact with external tools
Operate within well-defined guardrails
Agents take responsibility for controlling the flow of execution, reasoning in a loop until an exit condition is met.
When to use agents?
Agents are best suited for situations where deterministic approaches fall short due to complexity, for example:
Complex decision-making, such as analyzing reimbursements with exceptions and nuanced judgments.
Environments with hard-to-maintain rules, like vendor security reviews.
Interactions involving unstructured data, like interpreting text or engaging in natural dialogue with users.
Agent Architecture
An agent’s basic structure involves three core components:
1) Language Model (LLM)
Handles reasoning and action selection. Choosing the right model involves balancing:
Reasoning capability vs. latency and cost
Specialized models for specific tasks (e.g., classification, summarization)
Best practice: Start with a robust model (e.g., GPT-4o) to ensure performance, then gradually swap in smaller models for simpler tasks to optimize cost and latency.
2) Tools
External functions the agent can call (via APIs or wrappers), generally grouped into three categories:
Data: gather context (e.g., search databases or the web, read PDFs)
Action: perform tasks (e.g., send emails, log information)
Orchestration: allow agents to use other agents as tools
3) Instructions
Scripts that guide agent behavior, ideally derived from existing operational documentation. Recommended practices:
Be specific at each step (actions, messages, variables)
Handle exceptions and conditional branches
Use prompt templates with contextual variables
Orchestration: Single or Multi-Agent?
Single-Agent Systems
“Single-Agent Loop” systems are ideal for prototypes and low-complexity use cases. Logic runs in a loop until one of the following conditions is met:
The model returns a final answer
A termination tool call is triggered
An error or timeout ends the cycle
Multi-Agent Systems
More scalable and modular, ideal when there are many tools, distinct responsibilities, or complex rules.
Two main patterns:
Manager Pattern: A central agent coordinates specialized agents via tool calls (like a manager assigning tasks). Best for pipelines with centralized control.
Decentralized Pattern: Agents hand off tasks to one another, passing execution context without a central manager. Ideal for triage systems or specialized assistants.
Guardrails: Protecting Your Agent
Intelligent agents need clear boundaries to operate safely.
That’s where guardrails come in — protective mechanisms that validate the agent’s inputs, outputs, and decisions, helping prevent:
Leakage of sensitive information
Out-of-scope or dangerous behaviors
Decisions that affect finances or critical data
Types of guardrails:
Relevance and safety classifiers (detect scope deviations and jailbreaks)
PII filters and moderation APIs, for privacy and content safety
Output validation (ensures alignment with policy or brand guidelines)
Tool-specific risk controls (for irreversible or high-impact actions)
Deterministic rules (regex, blocklists, input/output limits)
Execution can follow an optimistic-with-exceptions approach: the agent proceeds normally, but guardrails are monitored in parallel and can interrupt execution if rules are violated.
Additionally, it's essential to implement fallback mechanisms so that humans can take control in situations like:
Repeated comprehension failures
High-risk actions (e.g., approving transactions or cancellations)
Lifecycle: From MVP to Production
Building agents isn’t an all-or-nothing effort. Start small:
Choose a complex workflow that resists traditional automation
Implement it with a simple agent and minimal tools
Test with real data and monitor interactions
Gradually add tools, conditional logic, and other agents as needed
Implement guardrails based on real-world failures
Automate logging, testing, and continuous evaluation metrics
Over time, you can scale to more sophisticated agents that intelligently and securely automate entire workflows.
Conclusion
LLM-based agents represent a new computational paradigm: intelligence driving execution, not just generating responses.
For engineers and solution architects, this marks a leap in how we build automation - moving from rigid flows to adaptive, contextual systems.
If your company deals with complex decisions, unstructured data, or workflows that are hard to automate with rules, it may be time to consider deploying an agent. And with the right best practices, you can do it confidently, efficiently, and with real impact.