ChatGPT Agent: A Holistic Agent or Just a Better Demo?

Today, OpenAI announced ChatGPT Agent [2], a major step towards a more capable and autonomous AI agent. For a deeper dive into the fundamentals of agentic AI, see my Introduction to Agentic AI post. While I’m still waiting for the ‘Agent mode’ to appear on my paid account, the announcement and the subsequent community discussions provide more than enough material for an initial analysis.

My take: this is one of the most holistic AI agents released to date. It represents a powerful merger of what were previously separate domains: coding agents, terminal agents, browsing agents, and research agents. Its true strength appears to lie not in a new revolutionary architecture, but in the practical and powerful integration of a diverse toolset.

The Power of Real Tools

OpenAI makes it clear that ChatGPT Agent is equipped for action:

We’ve equipped ChatGPT agent with a suite of tools: a visual browser that interacts with the web through a graphical-user interface, a text-based browser for simpler reasoning-based web queries (e.g., Web Search tool), a terminal (e.g., Bash tool), and direct API access.

This is a significant move away from more abstract, structured data protocols like MCP and towards giving the agent direct access to the same tools a human would use. This is a concept I’ve explored before. In my post, Agentic Tools, I argued that direct tool use, especially with CLIs, is often more efficient and context-friendly.

Giving an agent access to a terminal is a game-changer. It allows the agent to operate in a way that is far more familiar and powerful for developers, moving beyond the constraints of predefined API schemas. This aligns with the idea that the best way to automate a task is to build a script for it—and ChatGPT Agent is essentially being empowered to write and run its own scripts on the fly.

A Unified System

What makes this release compelling is the integration of previously separate research projects into a single, cohesive system.

It brings together three strengths of earlier breakthroughs: [Operator’s⁠] ability to interact with websites, [deep research’s⁠] skill in synthesizing information, and ChatGPT’s intelligence and conversational fluency.

This unified approach allows the agent to ‘fluidly shift between reasoning and action to handle complex workflows from start to finish.’ An agent that can browse a site, download a file, process it in the terminal, and then synthesize a report is leagues ahead of one that can only perform one of those functions. [4][5]

The 98% Problem and Community Skepticism

While OpenAI’s announcement paints a rosy picture, the developer community on Hacker News was quick to ground the hype with real-world skepticism. [3] The ‘almost right’ nature of LLMs remains a huge hurdle.

One commenter captured this perfectly in response to the demo video:

The ‘spreadsheet’ example video is kind of funny… It feels like either finding that 2% that’s off (or dealing with 2% error) will be the time consuming part in a lot of cases… Especially when the 2% error is subtle and buried in step 3 of 46 of some complex agentic flow.

This is the core challenge. An agent that is 98% correct can create more work than it saves, as verifying the output requires a full review, defeating the purpose of the automation. This leads to a useful mental model for working with today’s agents, as another user suggested:

The proper use of these systems is to treat them like an intern or new grad hire. You can give them the work that none of the mid-tier or senior people want to do… But you will have to review their work thoroughly because there is a good chance they have no idea what they are actually doing.

This sentiment is echoed by those building agents professionally, who often refer to the ‘last mile problem’:

I’m not so optimistic as someone that works on agents for businesses and creating tools for it. The leap from low 90s to 99% is classic last mile problem for LLM agents.

Sound familiar? This is a classic transportation and supply chain issue. [1]

In supply chain management and transportation planning, the last mile or last kilometer is the last leg of a journey comprises the movement of passengers and goods from a transportation hub to a final destination.

Open Questions: Long-Running Tasks and Security

While the agent’s capabilities are broad, I’m particularly curious about its performance on long-running tasks. The ‘deep research’ feature it incorporates can take a significant amount of time. How does the agent maintain state, context, and focus over tasks that might take an hour or more? If it gets stuck or goes down a rabbit hole, how gracefully can it be redirected without losing all its progress? This is a critical aspect of true autonomy that demos rarely showcase. For more on how agents can be designed to anticipate and act proactively, explore my post on Proactive Agents.

Furthermore, the security implications of giving an AI direct access to your data, credentials, and a terminal cannot be overstated. OpenAI acknowledges the risks of prompt injection, but the community rightly remains concerned:

The security risks with this sound scary. Let’s say you give it access to your email and calendar. Now it knows all of your deepest secrets… A malicious website could trick the agent into divulging your deepest secrets!

Conclusion

ChatGPT Agent is undeniably an exciting development. Its strength lies in its holistic design and its embrace of practical, powerful tools like a terminal and direct browser control—an approach I believe is the right path forward.

However, the leap to a truly reliable agent has not yet been made. The ‘98% correct’ problem is a massive barrier to trust and utility. For now, the ‘treat it like an intern’ model is a wise one. While OpenAI’s benchmarks are impressive, real-world value will be determined by how well the agent closes that final 1-2% gap in accuracy and reliability. I’m eager to get my hands on it and see for myself.

Most, if not all, of Fiverr and Upwork tasks can now be replaced by ChatGPT Agent for an astonishing $0.50 per execution, signaling a profound shift that could effectively replace the gig economy.

References

1. Last mile (transportation)
2. Introducing ChatGPT Agent
3. Hacker News: ChatGPT Agent Discussion
4. Introducing Operator
5. Introducing Deep Research