AutoGPT: Can It Solve 5 Different Tasks From Easy to Complex?

TL;DR

AutoGPT can complete multi-step, file-driven workflows end-to-end, producing saved outputs like images and structured text when goals are explicit.

Briefing Cornell Notes

Briefing

AutoGPT is tested across five tasks—ranging from simple file-based instructions to web scraping and multi-step research—and it repeatedly produces usable outputs, even when it stumbles on execution details. The clearest takeaway is that the system can chain actions (read → plan → browse → write → save) with enough autonomy to complete end-to-end workflows, but its success depends heavily on how precisely goals are specified and how well intermediate steps are validated.

The first challenge starts with a straightforward directive: read mission.txt, follow its instructions, generate an image, write two paragraphs connecting AGI to AI alignment, and add a reflective conclusion. AutoGPT successfully reads the file, generates a “baby … cyborg” image, writes the requested alignment-focused paragraphs, and appends a conclusion to the output file. The results are short but coherent, and the workflow demonstrates the system’s strength at turning text instructions into structured artifacts (image + saved text) without manual stitching.

Next comes a more concrete coding task: create working Python for tic-tac-toe, save it as Tic Tac dot Pi, and produce a report on how the code was created. AutoGPT plans by searching for resources, drafts code using a 2D list board representation and looping logic, and tests the program. A file-handling hiccup occurs when an existing filename conflicts, but human feedback resolves it by changing the output name to Tic Tac tool.pi. The game runs, though the AI opponent is weak—winning quickly via a simple line—highlighting that “working code” doesn’t automatically mean “good performance.”

The third challenge shifts to prompt engineering. AutoGPT browses midjourney documentation to learn what a Mid-Journey prompt should look like, then uses two agents to generate two prompts aimed at producing a “textbook portrait” of a historical figure. The prompts are saved along with a report explaining word choices and intended emotional tone. When the prompts are run, the outputs land in the right general aesthetic but miss the mark on the intended subject for at least one prompt, underscoring a recurring theme: autonomy can generate plausible artifacts, but prompt quality still hinges on tighter instruction and better grounding.

Challenge four tests web automation and analysis. AutoGPT scrapes job postings for “prompt engineer” using Beautiful Soup, analyzes titles/descriptions/salaries, and extracts the most in-demand skills. It then writes a step-by-step upskilling guide and even pulls online course suggestions for those skills. The workflow is notably effective: it identifies programming and cloud/devops-related competencies (including Python and cloud tools like AWS and Azure) and quantifies demand by counting mentions across job postings.

The final challenge is the most ambitious: produce a 2,500-word scientific paper about “molok” and AI alignment. AutoGPT performs research, builds an outline, and writes multiple chapters, but it initially loops when switching to the introduction. The system ultimately generates chapter content, then the author compiles it using GPT-4 into a structured paper with an abstract, sections, conclusion, and a PDF-ready format. Overall, the experiment shows AutoGPT’s practical power for multi-step production—while also revealing where human oversight and clearer goal constraints remain essential.

Cornell Notes

AutoGPT completes five escalating tasks by chaining actions like reading instructions, browsing documentation, generating code, scraping job postings, and drafting research writing. It handles file-based workflows well—turning mission.txt into an image plus saved alignment-focused paragraphs and a conclusion. For tic-tac-toe, it produces runnable Python and a creation report, though the AI opponent’s strategy is weak, showing that “runs” is not the same as “performs.” In prompt engineering, it uses Mid-Journey documentation and generates two prompts with explanatory reports, but the resulting images can miss the intended subject. In web scraping, it extracts in-demand prompt-engineering skills from job postings and produces an actionable learning guide, then the molok paper is assembled into a structured scientific draft using GPT-4.

How does AutoGPT perform on a simple “read a file and execute” mission?

It reads mission.txt from the workspace, then follows the embedded instructions: it generates an image (a “baby … cyborg” prompt), writes two paragraphs connecting AGI to AI alignment, and appends a reflective conclusion to the output file. The output is saved in a structured way and the workflow completes end-to-end without manual intervention.

What does the tic-tac-toe test reveal about AutoGPT’s coding ability?

AutoGPT plans by browsing for resources, then writes Python code using a 2D list board and loop-based printing. It tests the code by running the script and interacting with the game. A practical failure occurs around file naming—an existing filename causes an update conflict—resolved by using a different filename (Tic Tac tool.pi). The game works, but the opponent is easy to beat, indicating limited strategic quality.

Why did the Mid-Journey prompt results miss the mark even after documentation was consulted?

AutoGPT browses Mid-Journey documentation and produces two prompts via two agents, along with a report describing intended tone and word choices. However, the prompts appear to be generated largely from instructions rather than incorporating the retrieved documentation content into the prompt-writing step. When run, one prompt produces an image that is aesthetically plausible but not clearly aligned with the intended historical figure.

What makes the job-posting scraping challenge stand out?

AutoGPT uses Beautiful Soup to scrape prompt-engineering job postings, then analyzes job titles/descriptions/salaries to identify the most in-demand skills. It quantifies demand by counting mentions of skills across postings and writes a step-by-step guide to acquire them. It also delegates to find online courses, producing concrete learning resources (e.g., “Python for Everybody” and “Java programming”).

How was the molok-and-AI-alignment scientific paper ultimately produced?

AutoGPT performs research, generates an outline, and writes multiple chapters, but it loops when switching back to write the introduction. The chapter outputs are then compiled using GPT-4 into a coherent paper structure with an abstract, sections, conclusion, and a PDF-ready document, including an image and a table of contents.

Review Questions

Where did AutoGPT succeed purely through instruction following, and where did it require human correction (e.g., filename changes)?
Which task most clearly demonstrates quantitative analysis, and what metric was used to rank skills?
What specific failure mode appears in the molok paper workflow, and how was it mitigated using GPT-4?

Key Points

1
AutoGPT can complete multi-step, file-driven workflows end-to-end, producing saved outputs like images and structured text when goals are explicit.
2
Working code generation is achievable, but performance quality (e.g., tic-tac-toe opponent strength) can remain poor without additional evaluation and iteration.
3
Prompt engineering benefits from documentation access, yet prompt-grounding can fail if retrieved information isn’t actually incorporated into the generated prompts.
4
Web scraping plus aggregation can produce actionable, quantified results—such as counting skill mentions across job postings to identify in-demand competencies.
5
Delegation inside AutoGPT helps extend scope, such as finding courses after extracting skill requirements.
6
Large research writing may require external consolidation: AutoGPT can draft chapters, but GPT-4 can be used to compile them into a coherent scientific paper format.

Highlights

AutoGPT successfully turns a mission.txt instruction set into an image plus AGI/AI-alignment paragraphs and a saved conclusion.

The tic-tac-toe script runs after a filename conflict is resolved, but the AI opponent is weak—easy wins expose limits beyond “it works.”

Job-posting scraping yields a ranked skills list by counting mentions across postings and produces a step-by-step learning plan.

Mid-Journey prompts can look polished while still missing the intended historical subject, showing how fragile prompt specificity can be.

The molok paper draft is assembled into a polished multi-section document using GPT-4 after AutoGPT’s chapter-writing loop.

AutoGPT: Can It Solve 5 Different Tasks From Easy to Complex? - WOW!