Divya van Mahajan

A Free Course on Using Agents Created by ChatGPT Agent

ai-daily-brief-podcast

Overview

This talk is a live review and experiment by the host of The AI Daily Brief (a daily podcast and video series covering AI news and discussions) in which he uses ChatGPT Agent — a newly launched tool from OpenAI — to autonomously create a free, end-to-end course on AI agent management. The experiment also includes a direct comparison with Manus, a competing general-purpose agent platform. The central thesis is that general-purpose AI agents capable of chaining research, reasoning, and production tasks represent a meaningful leap beyond single-purpose AI tools, and that the ability to manage and orchestrate AI agents is the critical skill set the industry needs to be developing right now.

Source video URL: Not available (internal/podcast content)

Prerequisites

  • Basic familiarity with large language models (LLMs) and conversational AI tools such as ChatGPT
  • General awareness of AI agent concepts (autonomous AI systems that take sequences of actions)
  • Some exposure to tools like OpenAI’s Deep Research, Operator, or similar agentic platforms
  • Awareness of no-code/low-code automation platforms (e.g., N8N, Lindy) is helpful but not required
  • Familiarity with the concept of prompt engineering as a baseline skill

Main Points

What ChatGPT Agent Is and Why the Host Chose This Task

  • ChatGPT Agent combines three capabilities in a single interface: Deep Research (web research), Operator (computer use via graphical UI interaction), and native terminal/coding abilities.
  • This combination allows it to go beyond producing a research summary — it can execute multi-step production tasks end-to-end.
  • The host chose course creation as a test case because it requires research, structured reasoning (syllabus design), and document/output production — a natural fit for Agent’s combined capabilities.
  • The host’s motivation was dissatisfaction with upskilling platforms still focused on 2023-era AI skills (e.g., basic prompt engineering), arguing that agent management and orchestration are the skills needed now.

The Prompt and Initial Course Output

  • The host used voice dictation to give a relatively open-ended, rambling prompt requesting: a full course syllabus, a set of activities/to-dos, and a companion workbook — explicitly a self-contained, free, end-to-end course (not a teaser for a paid product).
  • The initial research phase took approximately four minutes; the first course draft was produced quickly.
  • The resulting course, titled Getting Good at Agent Management, included eight modules:
    • Module 1: Foundations of AI Agents
    • Module 2: Prompt Engineering (intentionally constrained)
    • Module 3: Context Engineering (more comprehensive, with practical activities)
    • Module 4: Tools and Frameworks (divided into no-code/low-code vs. code-based)
    • Module 5: Vibe Coding and the MCP Ecosystem
    • Module 6: Orchestration and Multi-Agent Workflows
    • Module 7: Safety, Guardrails, and Evaluation
    • Module 8: Capstone Projects and Further Inspiration
  • Each module included key points, resource links, and at least one activity.
  • Weaknesses noted: the output drifted from a structured syllabus into an overview dossier; some modules (e.g., Module 5) lost focus; activities became generic in later modules; some obvious tools (Lovable, Replit) were missing.

Mid-Session Refinements and the Activity Bank

  • The host demonstrated that Agent accepts mid-task refinements without waiting for completion — he clarified mid-run that the course should default to non-coders but include an advanced section for coders.
  • He also injected specific inspiration sources (Greg Eisenberg, Riley Brown) mid-run.
  • After the initial output, he prompted Agent to add a dedicated hands-on activity bank (Module 9), which included:
    • N8N workflow challenges (beginner to advanced)
    • Lindy agent-building exercises
    • Manus general-purpose tasks
    • ChatGPT Agent assignments
  • Observation: Agent anchored very closely to the specific examples given, rather than using them as illustrative and exploring further — a noted limitation of the model’s instruction-following behavior.

The Companion Workbook Output

  • The host asked Agent to produce a companion PowerPoint workbook with one slide per activity, organized by course section, with expanded step-by-step instructions.
  • The workbook (~20 pages) focused almost entirely on the Module 9 activity bank, failing to extract activities from all earlier modules as requested.
  • A second iteration added a “goal” section to each activity slide and attempted to diversify imagery (with limited success — images varied only when the platform being discussed changed).
  • Assessment of the workbook: approximately D+ to C range — functional but shallow.

Overall Assessment of ChatGPT Agent

  • The host gave the overall output a C (generous) rating.
  • Caveats: the rating reflects a largely one-shot, minimally refined session; with two hours of dedicated iteration the output would be substantially better.
  • The efficiency gain is significant: tasks that previously required multiple separate tools, manual wiring, and considerable time were completed in a single conversational interface in minutes.
  • All produced materials were committed to being shared in the show notes.

Manus Comparison

  • The host ran the identical prompt through Manus for direct comparison.
  • Manus took approximately 13 minutes and structured its process into six observable steps, providing more transparent progress reporting.
  • Manus correctly interpreted inspiration sources (Greg Eisenberg, Riley Brown) more thoroughly than Agent did.
  • Notable error: Manus initially interpreted “MCP” as “Master Control Prompt” rather than “Model Context Protocol” — corrected mid-run.
  • The Manus course output was assessed as structurally stronger: each module was divided into two lessons, resources were more prominently featured, and the content felt more coherent and correctly prioritized.
  • On the workbook/slides task, Manus significantly outperformed ChatGPT Agent — it correctly extracted activities from all modules (not just the activity bank) and produced a more complete and polished presentation (~B+ vs. D+/C).
  • Manus also offered to deploy the output as a live website, which ChatGPT Agent did not do natively.
  • Overall ratings: ChatGPT Agent — C; Manus — B− to B+ depending on the specific deliverable.

Broader Takeaway

  • Manus is currently a more polished product with more development iterations in this use case.
  • ChatGPT Agent has an overwhelming distribution advantage and will rapidly accumulate users and feedback.
  • Both tools demonstrate that general-purpose agents can now one-shot complex, multi-stage knowledge-work tasks — with results that are imperfect but impressive given the time and effort invested.
  • The host frames this as an early indicator of where agentic AI is headed, and emphasizes discovering which use cases normalize and become genuinely powerful most quickly.

Key Concepts

  • ChatGPT Agent: OpenAI’s newly launched general-purpose AI agent that combines Deep Research, Operator (computer/GUI use), and coding/terminal capabilities in a single interface.
  • Operator: OpenAI’s computer-use tool that allows an AI to interact with graphical user interfaces by clicking and navigating like a human user.
  • Deep Research: OpenAI’s AI capability for conducting extended, multi-source web research and synthesizing findings.
  • Agent Management: The practice of designing, orchestrating, monitoring, and refining AI agents and multi-agent systems — framed as the emerging core professional skill in AI.
  • Context Engineering: The discipline of deliberately constructing, managing, and feeding the right context (documents, memory, instructions) into AI agents to improve their outputs — positioned as a successor skill to prompt engineering.
  • MCP (Model Context Protocol): A protocol for standardizing how AI models connect to external tools and data sources; mistakenly interpreted by Manus as “Master Control Prompt.”
  • Vibe Coding: A colloquial term for AI-assisted coding where users describe desired functionality in natural language and the AI generates functional code, lowering the barrier for non-coders.
  • N8N: An open-source, no-code/low-code workflow automation platform commonly used to build agent pipelines and integrations.
  • Lindy: A no-code AI agent building platform used for creating personal assistant-style agents.
  • Manus: A Chinese-developed general-purpose AI agent platform that gained viral attention earlier in 2025; positioned as a competitor to ChatGPT Agent.
  • Multi-agent workflow: A system in which multiple specialized AI agents collaborate, hand off tasks, or are orchestrated by a supervisory agent to complete complex goals.
  • One-shotting: Producing a complete output from a single prompt or minimal iteration, used here as a benchmark for evaluating agent capability.

Summary

The host of The AI Daily Brief used the newly launched ChatGPT Agent to autonomously produce a free, self-directed course on AI agent management — a task chosen specifically because it requires chaining research, structured reasoning, and document production in ways that previously demanded multiple separate tools. The experiment revealed that ChatGPT Agent can produce broadly useful, multi-module course content with companion workbooks in a matter of minutes with minimal human refinement, though the outputs are inconsistent, sometimes drift from the original format requested, and anchor too closely to user-provided examples rather than exercising independent judgment. A parallel run with Manus produced structurally stronger course content and a significantly better workbook output, suggesting Manus is currently the more polished product for this type of task — though ChatGPT Agent’s distribution advantage means it will rapidly accumulate real-world use. The host’s overarching argument is that the ability to manage and orchestrate AI agents — not merely to prompt individual AI assistants — is the skill the industry urgently needs, and that tools like ChatGPT Agent and Manus represent early but compelling evidence that general-purpose agentic platforms can take on increasingly complex knowledge-work tasks end-to-end.