How to use ChatGPT Agents for practical AI tasks

TL;DR

ChatGPT agents can think, research, and take actions on tasks, but practical use is limited and often slower than manual methods. They may automate browsing, data gathering, and some online actions, yet execution delays, safety warnings, and partial outputs reduce overall usefulness in everyday tasks.

Transcript

Chat GPT introduced AI agents about a week ago and AI agents are basically intended to do things for you on the internet. So they could take over a virtual computer. They could think, they could reason, and they could do deep research and then combine that with the power of the agent to actually click things, fill out boxes. All of that sounded goo... Read More

Key Insights

• AI agents can perform deep research and browser based actions by launching a virtual browser and executing tasks across websites.
• Agent mode merges deep research with operator features to automate tasks, but real results may require user takeover and manual input.
• Examples show agents researching LLC formation, creating formatted outputs, and extracting data from sources like LegalZoom, highlighting inconsistent formatting.
• Agents can create spreadsheets and PowerPoint outputs, yet outputs may require further refinement for presentable results.
• Booking tasks like hotels involve multiple website navigations, with long time lags and fragile links that hinder smooth completion.
• Security warnings appear when signing into websites via agents, indicating potential data exposure and the need for caution with sensitive information.
• Usage scenarios include auditing Google Calendar and summarizing time spent, but accessing calendars or emails requires necessary permissions and can fail.
• Overall, the video presents a critical view where manual methods often beat agent automation in speed and reliability.

Questions & Answers

Q: A real search query question about the video (e.g. 'How to...', 'What is...', 'Why does...', 'When should...'). Question 1

What exactly can ChatGPT agents do and in which cases do they provide real value? The video explains that agents can think, research deeply, and take actions on websites, including booking or data extraction, but real value is limited by delays, the need for user takeover, and occasional inconsistent outputs. It emphasizes that for many tasks a traditional prompt or manual workflow is faster and more reliable.

Q: Question 2

How does agent mode differ from earlier tools like deep research or operator in ChatGPT? The demonstration shows that agent mode combines both deep research and the ability to perform actions, including launching a virtual browser to perform tasks. This integration allows end to end task execution but also introduces new complexities, such as monitoring progress and managing security concerns.

Q: Question 3

What are the practical examples shown for agent use, and what issues do they reveal? The video covers LLC research and instances like creating formatted outputs or booking a hotel. Issues include long processing times, format mismatches, and links that may not work, illustrating that automation may not always beat straightforward manual methods.

Q: Question 4

What risks are highlighted when using agents for tasks requiring login or payments? The presenter points out warnings about data exposure when signing into websites through a virtual browser, emphasizing caution with sensitive information and the need to avoid entering payment details through agents unless necessary.

Q: Question 5

How does the video compare agent based booking to manual booking? It shows that an agent took significantly longer to find a hotel and generate a link than performing the same search with manual filters, suggesting limited advantage for routine bookings and potential frustration.

Q: Question 6

Can agents handle personal data like calendars reliably? The video demonstrates an attempt to audit Google Calendar, noting that permissions and navigation are required, and that the agent may encounter snags, requiring user intervention, which reduces seamless automation.

Q: Question 7

What does the video imply about the future of ChatGPT agents? It presents a cautious view, recognizing interesting features while highlighting practical shortcomings, indicating that the current iteration may not live up to expectations for broad, reliable automation in everyday use.

Q: Question 8

What is the reviewer’s overall stance on ChatGPT agents after testing multiple scenarios? The reviewer remains skeptical, acknowledging some useful ideas but largely finds agent driven automation slower, less reliable, and not clearly superior to simpler or manual approaches for many common tasks.

Summary & Key Takeaways

• ChatGPT agents combine deep research and operator capabilities into an agent mode that can think, research, and take actions, but real-world usefulness varies by task

• The video argues that agent mode often underperforms compared with simple prompts, showing long waits, partial formatting, and difficult to control, which limits practical adoption

• Some demonstrated tasks include calendar audits, creating spreadsheets from research, and booking online services, but benefits are often outweighed by delays and data risk concerns