gensai.aiOpenAI now offers a coherent toolkit for building capable agents:
1. Suitable reasoning models
The agent's brain rests on reasoning models able to break a complex task into several steps (chain-of-thought reasoning).
OpenAI offered four main models as of April 2025:
| Model | Task complexity | Latency | Input price ($/1M tokens) | Output price ($/1M tokens) |
|---|---|---|---|---|
| o1 | Medium | Medium | 15 | 60 |
| o1-mini | Simple | Low | 1.1 | 4.4 |
| o1-pro | Advanced | High | 150 | 600 |
| o3-mini | Simple | Low | 1.1 | 4.4 |
Gensai's advice:
- For simple tasks, favour o3-mini.
- For complex projects, use o1-pro — while keeping costs under control.
2. Voice models: text-to-speech and speech-to-text
OpenAI extends its agents with the ability to understand and produce speech:
- gpt-4o-transcribe: accurate audio transcription (successor to Whisper).
- gpt-4o-mini-transcribe: a lighter version for environments requiring very low latency.
- gpt-4o-mini-tts: voice generation with customisable delivery.
Example applications: voice agents for call centres, enterprise voice assistants, natural language user interfaces.
3. Responses: an API designed for agentic AI
The Responses API makes it possible to combine models and tools:
- Web Search: retrieving information online in real time.
- File Search: querying internal knowledge bases.
- Computer Use: automating browser actions (clicks, filling in forms and so on).
Worth knowing: the Responses API will replace the older Assistants API in 2026. It is designed for multi-turn conversations and accepts multimodal inputs (text, files, images).
4. Agent SDK: orchestrating your agents simply
OpenAI provides a Python Agent SDK to:
- create agents easily, with custom instructions,
- orchestrate several agents through handoffs (intelligent transfer between agents),
- secure the flows with guardrails (access control and action validation).
Example of a simple agent definition:
from agents import Agent
math_tutor_agent = Agent(
name="Math Tutor",
instructions="You provide help with math problems. Explain your reasoning at each step."
)
Handoff example: a generalist agent automatically redirects the request to a specialist (mathematics, history and so on).
Guardrails: automatic filters that prevent sensitive actions (for example avoiding forbidden topics).
Why build AI agents for your organisation?
With OpenAI's tools, you can:
- automate complex processes (customer service, document processing, web research and so on);
- create voice assistants that are fast and personalised;
- optimise costs with models matched to each need (o3-mini, o1, o1-pro);
- gain agility through a simplified, modular development environment.
Gensai, a studio specialising in digital solutions that integrate AI, supports you in designing and deploying your custom AI agents.
FAQ: OpenAI AI agents
Is an AI agent different from a chatbot?
Yes. An AI agent can plan, act, reason and evolve, whereas a chatbot is often limited to answering.
Which models should you start with?
For simple use cases (FAQs, forms), o3-mini is enough. For advanced cases (complex planning, strategy), go for o1 or o1-pro.
Can you use OpenAI's tools without being a developer?
A grounding in Python is recommended, but the ecosystem is designed to be picked up quickly.
Is the Agent SDK secure?
Yes. Guardrails filter actions and limit the risks.