Based on OpenAI’s Agent Framework
OpenAI recently released a 34-page technical guide on building autonomous AI agents.
This post breaks it down into a practical, easy-to-follow framework for agencies and developers who want to start building useful AI agents—without the fluff.
What Is an AI Agent?
Let’s start with the fundamentals.
An AI assistant waits for instructions.
An AI agent acts on a goal.
OpenAI defines an agent as an “autonomous system that achieves high-level goals with minimal human input.” We think of it as the difference between a helpful coworker and a self-directed employee.
Gemini’s definition: Autonomous AI agents are advanced AI systems designed to operate independently, taking actions and making decisions to achieve specific goals with minimal human intervention. They leverage large language models (LLMs), memory, and tools to handle complex, multi-step tasks and adapt to dynamic environments
Agents operate with high independence.
You tell them what to achieve.
They figure out the rest.
The Agent Framework: 3 Core Components
Every functioning AI agent includes three essential parts:
- Model (the Brain)
The language model responsible for decision-making. - Tools (the Hands)
APIs, plug-ins, code functions, web search, or external actions. - Instructions (the Playbook)
The logic, rules, and constraints that define behavior.
Each piece is critical. Let’s go step-by-step.
1. Choose the Right Model
For your first agent, choose a proven, high-performing model:
- GPT-4o for general intelligence
- Claude 3 Sonnet for reasoning and language-heavy tasks
Later, you can add logic for dynamically switching models based on task type or system load.
✅ Start strong. Simplify early decisions by standardizing around one solid model.
2. Integrate Tools
Most agents will need tools to:
- Access or retrieve data (e.g., Web search, vector DBs)
- Perform actions (e.g., send messages, run code, trigger APIs)
- Coordinate operations (e.g., function calling, memory, routing)
OpenAI’s SDK supports tool usage via Function Calling and Multi-Call Programs (MCPs).
A common early setup:
- Tool 1: Web search plugin
- Tool 2: Function-calling endpoint (e.g.,
/calculate,/lookup) - Tool 3: Memory or long-term storage interface
Design tools with well-defined inputs and outputs.
3. Write Clear Instructions
Instructions guide the model’s behavior and decision logic.
The best-performing agents:
- Follow explicit behavioral constraints
- Have clear task boundaries
- Account for edge cases and user intent
- Include fallback or retry logic
You can even use an LLM like ChatGPT to help write or test your instruction set. Prompt it to “review this agent policy for gaps or ambiguity.”
Think of this like agent onboarding: give it a job description, not just a goal.
Single-Agent vs. Multi-Agent Systems
Most people jump to multi-agent architectures too quickly.
OpenAI recommends starting with single agents—even for complex workflows.
Why? In their tests, well-equipped single agents with 15+ tools outperformed multi-agent networks in speed and reliability.
But when multi-agent systems make sense:
Two common patterns:
- Manager Pattern: A coordinator agent delegates to role-specific sub-agents
- Decentralized Pattern: Each agent can interact freely based on capability
Keep in mind: each handoff adds latency and complexity.
Case Example: A Multi-Agent Homework Tutor
Let’s say we’re building a homework assistant.
Start with two single agents:
- Agent A: Solves math problems
- Agent B: Handles history-related queries
Once both are working independently, we build a router agent to direct incoming queries to the correct specialist.
Last step: add a guardrail layer to detect inappropriate or off-topic inputs. This improves safety and keeps the agent focused.
Key Tips for New Agent Builders
- Start with one use case
- Build one agent, not a fleet
- Give it tight rules and permissions
- Expand only when a simple system breaks
- Add human intervention for critical workflows (OpenAI’s #1 tip)
Final Thoughts
AI agents aren’t science fiction. They’re available now—and increasingly practical.
Whether you’re building internal automations, client-facing tools, or new digital products, the agent pattern is a powerful way to deliver real autonomy in your AI systems.
We’re actively using this framework in our internal tools and client projects.
If you’re exploring how to build intelligent, goal-driven systems using language models, we’d love to help.