Manus AI Under the Microscope: Can This Autonomous Agent Deliver Real Research? (And Is It Worth the Cost?)

Manus AI is an autonomous general-purpose AI agent developed by Chinese startup Monica. Launched in March 2025, Manus has quickly generated massive attention—boasting over 2 million people on its waitlist and being described as the first truly general AI agent capable of executing complex multi-step tasks.

Unlike traditional chatbots or even structured research assistants, Manus doesn't just write—it builds, acts, and solves. From financial dashboards to 3D visualizations and e-commerce sites, it executes tasks with minimal input using a multi-agent architecture inspired by collaborative human workflows.

For a deeper dive into its architecture, multi-agent design, and competitive positioning, see my previous post: 👉 Manus AI: Revolutionizing the Future of Autonomous AI Agents

This post documents my experience evaluating Manus AI by asking it to generate a comprehensive report on Robot Framework adoption in industry.

Prompt Used

I supplied Manus AI with the following comprehensive instruction to test its research capabilities:

You are a research analyst tasked with generating a comprehensive research report on the adoption of Robot Framework in industry ...

Asking Manus for a Report

Here’s the interface where I submitted my prompt:

After submission, Manus AI began processing the request.

During Report Generation

The platform provided a clear status tracker showing report generation in progress:

It took just a few minutes, approximately 15 minutes, for Manus AI to produce a multi-section report complete with subsections, inline citations, and structured content aligned with the prompt.

Final Report Output

Once the generation finished, the resulting report appeared in a structured format with headings, paragraphs, and source references:

You can then download or export it.

📊 Report Summary: Key Metrics and Insights

Total Length: ~20,000 words
Sections: 7 major sections, 100+ subheadings
References: 17 cited sources
Industries Covered: Telecom, Finance, Healthcare, Automotive, Tech, Public Sector
Use Cases: Web, API, Mobile, Desktop, RPA, CI/CD
Framework Comparisons: Robot Framework vs 6 open-source alternatives
Insights Provided:
- Adoption patterns across regions and sectors
- Enterprise-scale and small team examples
- Roadmap updates (v7.0 & v7.2) with future projections
- Challenges and success patterns
- When to choose (or not choose) Robot Framework

The report delivers executive-ready depth, combining technical clarity with strategic guidance.

Credit Consumption

The generation of this full-length report used up a noticeable portion of the allocated credits:

Credits Consumed: 621
Remaining Credits (in my free plan): 379
Total Time to Generate: Around 15 minutes

Prices for other plans are as follows:

Conclusion

After completing the evaluation, here are my takeaways:

✅ Report Quality

Structured and coherent
Contained multiple references and sources
Suitable for stakeholder review or client presentations

⚡ Time to Output

Approximately 15 minutes end-to-end

💰 Credit Efficiency

The comprehensive report costs ~60% of my free-tier credits

🔁 Alternatives

ChatGPT Deep Research
Perplexity Deep Research
Custom LLM Chains: More customizable but require setup

Manus AI offers strong structure and autonomous task execution, positioning itself at the forefront of the emerging AI agent space. The cost of running Manus AI should be factored in, especially when scaling research efforts. Consider the credit consumption rate and how it aligns with your budget and reporting needs.

Enjoyed this post? Found it insightful? Feel free to leave a comment below to share your thoughts or ask questions. A GitHub account is required to join the discussion.