GPT-5 and agents

The arrival of GPT-5 marks a new era for generative artificial intelligence, significantly pushing the boundaries of what's possible in terms of reasoning, coding, contextual understanding, and agent autonomy. This model, designed from the ground up to serve as the foundation for agentic applications, brings major advances that are profoundly transforming the uses of AI, both for developers and for organizations looking to automate complex tasks or optimize their workflows.

GPT-5 has been trained to excel at executing long, multi-step tasks, providing responses that are accurate, consistent, and adapted to a wide variety of contexts. Its operation is modular: depending on the chosen configuration, it can act completely autonomously or, on the contrary, be content with targeted interventions, always within a programmatic framework set by the user. This ability to finely balance the agent's level of initiative is at the heart of the model's effectiveness, particularly for business use scenarios, where control and security are paramount.

One of the major areas of progress brought by GPT-5 concerns the management of agenticity, that is, the capacity of the AI ​​agent to control tools, explore problems proactively or explicitly wait for new instructions. To optimize the agent's behavior, it is now possible to adjust the reasoning_effort parameter, thus adjusting the model's exploration depth. When efficiency is paramount—for example, for repetitive tasks or rapid contextual searches—a "low" or "medium" mode reduces latency and focuses the AI ​​on the essentials. Conversely, for complex tasks or those requiring numerous iterations, increasing the reasoning_effort level allows for a more exhaustive and autonomous approach, with the model going so far as to minimize the number of questions sent back to the user and persevering until the request is completely resolved.

The ability to frame the agent's behavior also involves defining explicit stopping criteria, tool call budgets, or introducing escape clauses that allow the model to continue under uncertainty, thus accelerating decision-making when the context warrants it. In the same spirit, the management of tool preambles plays a key role in transparency and alignment between AI and its users. GPT-5 is capable of providing detailed action plans, reformulating objectives, clarifying choices, and issuing progress reports, thus improving readability and confidence in agent-driven processes.

In terms of technical performance, GPT-5 outperforms all previous models, particularly in the areas of coding and software development. It stands out for its ability to handle large code bases, fix bugs, implement cross-functional features, and design applications from scratch, both on the frontend and backend. To maximize the quality of the generated code, it is recommended to structure the prompts in such a way as to force the model to define its own rubrics of excellence, to plan, to reflect deeply, and then to self-evaluate at each stage. This approach, centered on the rigor, modularity, and reusability of the code, is accentuated by the use of modern frameworks such as Next.js, React, Tailwind CSS, as well as project organization standards inspired by industry best practices.

The successful integration of GPT-5 into advanced development environments—such as the Cursor code editor, which collaborated closely with the model training teams—is a perfect illustration of the maturity achieved. Cursor has refined its prompts to balance response verbosity, limit unnecessary interruptions in long-running tasks, and customize the agent's behavior according to user preferences. This shows that, to exploit the full potential of GPT-5, it is no longer just a matter of sending raw instructions, but rather of building ecosystems of instructions, rules, and parameters adapted to each context, allowing the agent to integrate seamlessly into existing production chains.

In terms of controllability, GPT-5 introduces increased sensitivity to instructions, both in terms of form (verbosity, style, level of detail) and content (respect for priorities, management of contradictions). The clarity and structure of prompts become essential levers to guarantee the quality of results: instructions that are too vague or contradictory can cause the model to waste time in attempts to resolve internal conflicts, while an explicit prioritization of instructions favors the speed and relevance of the response. The model also stands out for its sophisticated management of Markdown responses, adjustable via the API or by periodic reminders in the prompts, thus offering rendering flexibility adapted to each application environment.

For production use, GPT-5 fully leverages the “Responses API,” launched by OpenAI in March 2025, which radically transforms the management of agentic conversations. By enabling the transmission of reasoning history from one turn to the next, this API significantly reduces costs and token consumption, while accelerating response times and ensuring optimal logical continuity in task sequencing. Evaluations conducted show significant gains in industry benchmarks, particularly in retail, simply by switching to this new conversational architecture.

Prompt optimization, the heart of success with GPT-5, also involves the practice of “meta-prompting,” which involves asking AI to improve instructions itself, detect ambiguities, or suggest variants adapted to a given objective. This self-improvement loop is proving extremely effective for refining workflows and accelerating the integration of the model into real business use cases.

Finally, the GPT-5 philosophy is part of a logic of advanced agenticity, where AI is no longer just a passive assistance tool, but a true collaborator, capable of planning, adapting, verifying and persevering in the execution of complex tasks. This evolution paves the way for intelligent automation of processes previously reserved for humans, while guaranteeing an unparalleled level of control, personalization and security. For trainers, developers and organizations, this transformation invites them to rethink the architecture of human-machine interactions, invest in the quality of prompts and adopt continuous optimization practices to leverage the power of the new generation of generative AI.

To go further: how design an agent that is predictable and usable by everyone, how mastering the security of AI agents and towards agile and responsible governance of autonomous agents.