Manus AI: Inside the Multi-Agent Platform That Crashed on Launch Day
Manus AI launched as a multi-agent system combining Claude 3.5 Sonnet and Qwen models. We tested it across three real-world tasks. Here is what we found.
A Launch That Broke the Internet
On March 6, 2025, Manus AI opened its doors — and immediately crashed under demand. Registration servers buckled, and invitation codes appeared on secondary markets as users scrambled for access. The hype was real, but the question remained: does the technology justify it?
Unlike single-model chatbots, Manus AI operates as a multi-agent system — combining Claude 3.5 Sonnet, Alibaba's Qwen models, and autonomous agents to handle complex, multi-step tasks with minimal user guidance. That architectural distinction matters. It is the difference between asking a question and delegating a project.
Real-World Testing
We put Manus AI through three practical tasks to evaluate its capabilities under realistic conditions.
Task 1: Tech Reporter Research
Goal: Compile a comprehensive list of technology reporters with bios and contact information.
Results: The initial search returned only 5 names — far short of useful. After providing feedback and refining the query parameters, the system expanded to 30 results with detailed biographies. However, paywalled content proved problematic: Manus struggled to access or summarize content behind subscription barriers.
Verdict: Promising with iteration, but requires human oversight to course-correct.
Task 2: NYC Apartment Search
Goal: Find and rank apartment listings matching specific criteria in New York City.
Results: The system initially misinterpreted several search criteria, returning results that did not match the brief. After adjustment, it produced structured rankings organized by category — "Best Overall," "Best Value," and "Luxury Option" — with supporting rationale for each recommendation.
Verdict: Useful output, but the initial misinterpretation is a concern for unattended workflows.
Task 3: Young Innovators Discovery
Goal: Identify 50 emerging innovators under 30 in the technology space.
Results: After three hours of processing, the system returned only three candidates. With additional prompting and iteration, it eventually compiled 50 names — but struggled significantly with academic databases and gated publications.
Verdict: The three-hour processing time for a partial result reveals infrastructure limitations that prevent production-grade reliability.
Strengths and Limitations
What works well:
What needs work:
Market Context
Manus AI's launch coincides with a surge in China's AI sector. The Hang Seng Tech Index gained 40% following DeepSeek's January rise. The Chinese government has allocated $140 billion to AI investment, and Alibaba committed $53 billion to cloud services expansion. The competitive landscape for multi-agent AI systems is accelerating rapidly.
Our Assessment
Manus AI demonstrates genuine architectural innovation — the multi-agent approach is the right direction for complex task automation. But the infrastructure is not yet ready for enterprise-grade reliability. Server crashes, multi-hour processing times, and limited access to gated content sources are deal-breakers for organizations that need consistent, predictable results.
The technology is worth watching. For production deployment, it is not there yet.
Learn how The AI Cowboys builds reliable AI systems or contact us to discuss agentic AI for your organization.