The dawn of truly agentic AI, where systems reason and act autonomously, is upon us, as articulated by OpenAI's Ilan Bigio during a recent Build Hour. This pivotal shift moves beyond mere conversational AI, empowering models to tackle complex, long-horizon tasks by leveraging external tools and self-directed reasoning.
Ilan Bigio, from OpenAI's Developer Experience team, and Sarah Urbonas, leading Startup Marketing, hosted the "Build Hour: Agentic Tool Calling" session, the first of 2025, to empower founders, VCs, and AI professionals with best practices and expertise. They delved into OpenAI's latest APIs and models, emphasizing how these innovations enable developers to build scalable, intelligent agentic applications. The core message underscored a significant evolution in AI capabilities, moving from models that simply respond to those that actively accomplish objectives.
OpenAI has been busy, releasing a suite of new capabilities that underpin this agentic future. Key among these are the Responses API, a core primitive for agentic applications supporting chat, tool use, and code interpretation, and new multimodal reasoning models like GPT-4.1 (mini & nano), o3, and o4-mini, which significantly enhance performance for complex tasks. Developers also now have access to Codex CLI, an open-source local terminal AI coding agent that executes complex repository tasks, autonomously writing, testing, and reviewing codebases in parallel. "The technology that we used internally for o1, o3, Codex, everything, is actually mostly out in the open for you to build as well," Ilan emphasized, underscoring OpenAI's commitment to democratizing advanced AI development.
The profound implication for AI development is a shift from explicit, step-by-step instructions to defining an end state, allowing the agent to reason and devise its own strategy. Ilan showcased this with a live demo of Codex, where he simply pasted a GitHub issue into the system and instructed it to "fix this." Codex then autonomously downloaded the repository, set up the environment, and began analyzing the code, demonstrating its ability to understand and act on high-level goals.
The essence of this new paradigm lies in the combination of advanced reasoning and tool calling. "Through reinforcement learning, o1 learns to hone its chain of thought and refine the strategies it uses. It learns to recognize and correct its mistakes. It learns to break down tricky steps into simpler ones. It learns to try a different approach when the current one isn't working. This process dramatically improves the model's ability to reason," Ilan explained. This self-improving reasoning, when combined with access to external tools and APIs, unlocks what OpenAI terms "Agentic Tool Calling." Agents are no longer confined to generating text; they can query files, interact with external services, and execute commands, leading to goal-oriented, resourceful, and robust systems capable of long-horizon tasks. They learn from results, not just predefined actions.
Building these sophisticated agentic systems, however, requires a holistic approach that extends beyond just the core AI model. A robust task system encompasses four critical components: the agent itself (defining its goals, tools, and delegation capabilities), the underlying infrastructure (handling parallel execution, state management, and error recovery), the product interface (how users interact and visualize progress), and rigorous evaluation (assessing performance and collecting examples). This comprehensive framework is essential for transforming AI capabilities into reliable, user-facing applications. The focus shifts to the final outcome: "Now with tasks, you actually are more interested in the end result, and maybe less interested in what each of the turns the model took was," Ilan noted, highlighting the evolving metrics of success. Furthermore, the ability to fine-tune a model to act as an "evaluator" or "grader" based on specific examples and rubrics is a powerful new capability. "If you have a few examples, you can actually fine-tune a model to be your grader," he added, illustrating a practical path to customized performance assessment.
This new wave of agentic tooling, exemplified by OpenAI's latest releases, signals a maturation of AI development. It offers a clear pathway for developers to build intelligent systems that can reason, act, and adapt, tackling complex problems that were previously out of reach for automated solutions.
