With the Responses API and Agents SDK, OpenAI Aims to Turn Flashy Demos into Functional Tools for Businesses
- OpenAI launches the Responses API and Agents SDK, enabling businesses to build customizable AI agents that automate tasks like web searches, data retrieval, and computer workflows.
- Despite high accuracy claims, challenges remain, including AI hallucinations, unreliable citations, and the need for greater autonomy in agentic systems.
- 2025 could be the “year of the AI agent” as OpenAI shifts focus from demos to enterprise-ready tools, betting on agents as the future of workplace AI.
OpenAI is doubling down on its vision for AI agents—autonomous systems that complete tasks without constant human oversight. On Tuesday, the company unveiled its Responses API and Agents SDK, tools designed to empower developers and businesses to build custom AI agents using OpenAI’s latest models, including GPT-4o search and the Computer-Using Agent (CUA). These releases mark a strategic pivot from experimental demos to practical, scalable solutions, even as the broader tech industry grapples with defining what AI agents should do—let alone delivering on their promises.
From Assistants to Agents: What’s New?
The Responses API replaces OpenAI’s older Assistants API (phasing out in 2026) and introduces capabilities inspired by its consumer-facing tools like Operator (web navigation) and deep research (report generation). Businesses can now develop agents that:
- Search the web with cited sources using GPT-4o search models, which boast 90% accuracy on fact-based benchmarks.
- Scan internal files securely, with OpenAI pledging not to train on proprietary data.
- Automate computer workflows via the CUA model, which simulates mouse and keyboard actions for tasks like data entry.
Notably, enterprises can run the CUA model locally for enhanced security, a critical feature for industries like finance or healthcare. But OpenAI is upfront about limitations: the CUA is still in “research preview,” prone to errors, and currently limited to web-based tasks in its consumer version.
The Hype vs. Reality Challenge
AI agents have long been a buzzword, but real-world applications often fall short. Just this week, Chinese startup Butterfly Effect faced backlash when its Manus agent platform failed to meet user expectations. OpenAI’s Olivier Godement acknowledges the gap: “It’s pretty easy to demo your agent. To scale an agent is pretty hard, and to get people to use it often is very hard.”
While GPT-4o search outperforms traditional models (scoring 90% vs. GPT-4.5’s 63% on factual accuracy), it still gets 10% of answers wrong. Citations in ChatGPT’s outputs have also been criticized as unreliable, and navigational queries (e.g., “Lakers score today”) remain a pain point.
Tools for Trust and Transparency
To address skepticism, OpenAI is releasing the Agents SDK, an open-source toolkit for integrating safeguards, monitoring agent activity, and debugging. This builds on Swarm, its 2023 framework for multi-agent collaboration. The goal? To help developers balance automation with accountability.
Godement believes 2024 could bridge the “demo-to-product” divide, while CEO Sam Altman has earmarked 2025 as the year AI agents enter mainstream workflows. For businesses, the promise is clear: agents could automate repetitive tasks, streamline research, and reduce human error—if OpenAI’s tools evolve to match the hype.
Agents Are Coming—Ready or Not
OpenAI’s latest releases signal a maturation of agentic AI, but the road ahead is fraught with technical and trust hurdles. As businesses experiment with the Responses API and Agents SDK, the true test will be whether these tools can move beyond viral demos to become indispensable workplace allies. One thing is certain: the race to build reliable AI agents is no longer a theoretical debate—it’s a reality shaping the future of work.