Manus AI is a recently launched artificial intelligence system developed by a Chinese startup called Monica (a subsidiary of Butterfly Effect), a Chinese startup based in Wuhan. Unveiled on March 6, 2025, it’s being promoted as the "world’s first fully autonomous AI agent," designed to go beyond traditional chatbots by not just generating responses but independently planning and executing complex, real-world tasks. As mentioned on their website, the name "Manus" comes from the Latin word for "hand," reflecting its focus on turning thoughts into actions.

Watch this first, then read how this was created.

Source: Manus AI

Key Features

Manus operates as a multi-agent system, meaning it combines multiple AI models—like Anthropic’s Claude 3.5 Sonnet and fine-tuned versions of Alibaba’s Qwen—to tackle tasks autonomously. Manus uses Anthropic’s Claude 3.5 Sonnet as its primary natural language processing (NLP) and reasoning engine. Manus employs a multi-agent system where a central "executor" agent coordinates with specialized sub-agents. This architecture allows it to break down high-level objectives into actionable steps, delegating tasks like planning, knowledge retrieval, or tool execution to separate components.

Manus integrates approximately 29 external tools to extend its functionality beyond text generation. These tools enable real-world interaction, such as:

Browser Automation: Via open-source software like Browser Use, Manus can navigate websites, perform searches, and scrape data. This is critical for tasks requiring web-based research or interaction.
Shell Execution and File Manipulation: Tools like shell_exec and file_read allow Manus to interact with system processes and manage files, supporting coding and deployment tasks.
API Interaction and Script Execution: These capabilities enable Manus to interface with external systems and execute generated code, such as deploying a website from scratch.

The seamless integration of these tools with Claude 3.5 Sonnet’s reasoning allows Manus to bridge the gap between planning and execution, a key differentiator from traditional AI assistants.

Manus’s technology isn’t about creating a brand-new AI model from scratch. Instead, it smartly combines existing tools—like Claude 3.5 Sonnet for reasoning and language, fine-tuned versions of Qwen, open-source AI tools, and a multi-agent system that lets different AIs work together.

The real innovation is how these pieces are coordinated. Claude 3.5 Sonnet acts as the "brain," providing intelligence and decision-making, while the multi-agent system and tools handle specific tasks like coding, analyzing stocks, or sorting résumés—often with little to no human involvement.

This setup makes Manus highly efficient and, according to its creators, even better than some leading AI research models, like OpenAI’s Deep Research, in certain performance tests.

Manus AI’s Unique Proposition

Unlike typical AI assistants that require ongoing human prompts, Manus can take a single instruction and run with it, working asynchronously in the cloud even if the user disconnects. It’s built to handle a wide range of activities, such as:

Research and Analysis: Conducting in-depth stock market analysis or comparing insurance policies, delivering structured reports or dashboards.
Content Creation: Building websites from scratch, generating educational materials, or crafting travel itineraries with custom handbooks.
Task Automation: Screening resumes, ranking candidates, and producing spreadsheets—or even managing multiple social media accounts simultaneously.

It also features an interface where users can watch it work in real-time, intervene if needed, and see its step-by-step process (e.g., browsing websites, writing code, or pulling data). The system learns from interactions, adapting to user preferences over time.

Performance Claims

Manus has garnered attention for claiming to outperform OpenAI’s Deep Research on the GAIA benchmark, a test for general AI assistants solving real-world problems. While exact scores aren’t widely public, its developers assert it sets a new state-of-the-art standard across all difficulty levels. Early testers have praised its speed and ability to handle multi-step workflows, though some report glitches like system crashes or inconsistent outputs.

Availability and Buzz

Currently, Manus is in an invitation-only private beta, with limited server capacity cited as the reason for restricted access. Its launch demo video, hosted by Ji Yichao (a Chinese entrepreneur with a background in tech startups like Peak Labs), went viral, racking up over 200,000 views on X shortly after release. The AI community is abuzz, with some calling it China’s “second DeepSeek moment,” referencing another impactful Chinese AI launch earlier in 2025. However, skepticism exists—critics point to potential overhype, early performance issues, and concerns about data privacy given China’s National Intelligence Law, which could mandate data sharing with state agencies.

Implications

Manus represents a shift toward more autonomous AI agents, potentially challenging Western models like ChatGPT or Google’s Gemini, which rely more on human guidance. Its ability to execute tasks end-to-end could disrupt industries like business automation, education, and research, though it also raises ethical questions about job displacement and accountability for autonomous decisions.

In short, Manus AI is an ambitious, still-evolving tool that’s generating excitement and debate. Its real-world impact will depend on how it scales, refines its capabilities, and addresses early limitations.

Comparing Manus to other Browser-Based Agents like Google Mariner and ChatGPT Operator

Manus AI, Google’s Project Mariner (built on Gemini), and OpenAI’s Operator (integrated with ChatGPT) represent cutting-edge advancements in agentic AI—systems designed to autonomously perform complex tasks by interacting with digital environments like browsers or computer interfaces. While detailed technical specifics for all three remain partially obscured due to their developmental stages or limited releases, a comparison based on available information highlights their capabilities, approaches, and current standings as of March 12, 2025.

Manus AI

Stands out for its ability to autonomously handle a broad range of tasks, from deep web research to code execution.

Core Technology: Manus integrates Anthropic’s Claude 3.5 Sonnet as its primary reasoning and language model, supplemented by fine-tuned versions of Alibaba’s Qwen models for cost efficiency and task-specific optimization. It leverages around 29 external tools, including the open-source Browser Use agent, to interact with websites and execute commands.
Capabilities: Manus can browse the web, generate detailed plans, operate a computer, and run code in isolated sessions. It excels in task decomposition and execution, such as deploying websites or analyzing data, by orchestrating a multi-agent system led by a central executor agent. On the GAIA benchmark (testing reasoning, multi-modality, and tool use), Manus scores 86.5% on Level 1 tasks, outperforming OpenAI’s Deep Research (74.3%) and approaching human performance (92%).
Strengths: Its strength lies in its integration of multiple tools and models into a cohesive, autonomous system. The multi-agent architecture allows it to break down complex objectives and delegate effectively, while its reliance on open-source components ensures adaptability.
Limitations: As a closed beta product, its full potential is speculative, and some hype may be exaggerated. It depends on third-party models like Claude, which could limit scalability due to cost or availability constraints.

Google’s Project Mariner (Gemini-Based)

Project Mariner is Google’s agentic AI initiative, built atop the Gemini 2.0 model family. It remains a conceptual or pre-release project as of now, with limited public details beyond demonstrations and benchmarks.

Core Technology: Mariner uses Gemini 2.0 (likely the Flash or Experimental variants), Google’s latest multimodal LLM, optimized for speed and reasoning. It’s designed as a web-browsing agent, leveraging Google’s vast ecosystem (e.g., Search, Knowledge Graph) and screenshot-based screen interpretation for navigation.
Capabilities: Mariner focuses on browser-based task execution, scoring 83.5% on the WebVoyager benchmark, which tests web interaction proficiency. It can interpret graphical interfaces, take actions, and iterate based on screen feedback, aiming to automate tasks like form filling or research within Chrome. Mariner can interact with the browser and users, functioning as an assistant or autonomously completing tasks. It learns by observing and engaging with users, enabling it to automate repetitive processes efficiently.
Strengths: Its integration with Google’s infrastructure provides unparalleled access to real-time web data and search capabilities. Gemini 2.0’s multimodal design suggests potential for future expansion into image or voice-driven tasks, and its speed (e.g., Flash variant) could make it highly responsive.
Limitations: Mariner’s capabilities are less proven, as it lags behind Operator and Manus in public rollout. Its browser-only focus narrows its scope compared to Manus’s broader toolset, and details about its agent architecture or tool integration remain under wraps, fueling speculation about its full potential.

OpenAI’s Operator (ChatGPT-Integrated)

Operator, launched in January 2025 as a research preview for ChatGPT Pro users in the US, is OpenAI’s foray into agentic AI, built on the Computer-using Agent (CUA) model. It’s poised for eventual integration with ChatGPT’s broader ecosystem.

Core Technology: Operator uses CUA, a specialized model trained to interact with graphical user interfaces via screenshots, distinct from ChatGPT’s GPT-4o or o1 models. It’s designed to mimic human computer use, scanning screens and taking iterative actions without relying solely on APIs.
Capabilities: Operator can perform browser-based tasks (e.g., booking flights, data entry) and scores 87% on WebVoyager, edging out Mariner (83.5%) and significantly surpassing Anthropic’s Computer Use (56%). On OSWorld, which tests desktop tasks like file manipulation, CUA scores 38.1% (vs. humans at 72.4%), indicating broader potential beyond browsers, though this isn’t yet fully accessible. It’s currently limited to browser interactions in its preview phase.
Strengths: Operator benefits from OpenAI’s expertise in reasoning and NLP, offering high accuracy in task execution. Its screenshot-based approach broadens its applicability to websites lacking APIs, and its planned API release suggests future extensibility for developers.
Limitations: Its functionality appears narrower than Manus’s in the preview phase, focusing on browser tasks without the multi-agent complexity or extensive toolset. Access is restricted to Pro users ($200/month), and its desktop capabilities remain underdeveloped compared to its benchmarks.

Comparative Analysis (as of 3/12/25)

Autonomy and Scope: Manus leads in autonomy and versatility, thanks to its multi-agent system and 29-tool integration, enabling it to tackle diverse tasks from planning to execution. Mariner and Operator are more specialized, currently excelling in browser-based workflows but lacking Manus’s breadth.
Performance: On benchmarks, Operator slightly outperforms Mariner in WebVoyager (87% vs. 83.5%), while Manus dominates GAIA (86.5% on Level 1), suggesting superior general-task proficiency. Operator’s OSWorld score (38.1%) hints at untapped desktop potential, but it trails Manus in real-world deployment.
Technology and Innovation: Manus innovates by combining existing models (Claude, Qwen) with open-source tools, while Operator introduces a novel CUA model for GUI interaction. Mariner relies on Gemini’s multimodal strengths but lacks transparency about its agentic layer, making its innovation harder to assess.
Availability and Maturity: Operator is the most accessible (albeit limited to Pro users), followed by Manus (closed beta), while Mariner remains pre-release, giving OpenAI and Monica a head start over Google in deployment.
Ecosystem Integration: Mariner benefits from Google’s ecosystem, potentially offering seamless ties to Search and Workspace. Operator leverages OpenAI’s ChatGPT user base, while Manus, as a standalone agent, lacks such a native platform advantage.

Side-By-Side Comparison of Manus, Mariner, and Operator

Manus AI currently appears the most advanced in scope and autonomy, blending multiple models and tools into a robust general-purpose agent, though its closed beta status tempers definitive claims. Mariner, while promising due to Gemini’s power and Google’s resources and demonstrated use cases, remains speculative until further rollout, trailing in transparency and availability. Operator showcases strong browser performance and innovative GUI interaction, positioning it as a focused, practical tool with growth potential.

For now, Manus seems to set the pace for agentic AI’s future, Operator delivers tangible results in a narrower domain, and Mariner holds latent potential yet to be fully realized. All three are evolving rapidly, and their trajectories suggest a competitive race toward more autonomous, capable AI agents.

Posts on X echo this sentiment, noting Manus’s bar-raising capabilities, Mariner’s hidden innovations, and Operator’s more limited but operational debut, though such claims remain inconclusive without broader access and testing.

What This Means For You

Manus AI's emergence signals a significant shift in the AI landscape, with profound implications for executive coaches, business owners, and CEOs. Here's a curated breakdown of what this means:

For Executive Coaches:

Evolving Coaching Methodologies:
- Coaches can leverage AI tools like Manus to conduct comprehensive background research on clients' industries and challenges, enabling more targeted and impactful coaching sessions.
- Coaches will need to help clients navigate the ethical considerations of using advanced AI, particularly regarding decision-making and accountability.
- AI could be used to create personalized coaching programs, track client progress, and provide real-time feedback.
Focus on Human Skills:
- As AI automates more tasks, the demand for uniquely human skills like emotional intelligence, complex problem-solving, and creative thinking will surge. Coaches will need to help executives develop these.
- Coaches can help executives develop the adaptability and resilience needed to thrive in an AI-driven world.
- Coaches will be needed to help executives manage the change management that will be required to integrate AI into their workforces.

For Executives:

Automation and Efficiency:
- Manus AI's ability to automate complex tasks like resume screening, market analysis, and content creation can significantly boost efficiency and reduce operational costs.
- AI-powered research and analysis can provide business owners with deeper insights into market trends, customer behavior, and competitive landscapes, enabling more informed strategic decisions.
Workforce Transformation:
- CEOs must develop a clear vision for how AI will transform their organizations and industries
- Business owners should anticipate the need to retrain their workforce to work alongside AI, and to fill new roles that are created because of AI.
- CEOs must attract and retain top AI talent, and invest in training and development programs to upskill their existing workforce.

For Everyone:

Ethical Considerations and Risk Management:
- Companies must prioritize ethical considerations, ensuring that AI is used responsibly and transparently.
- Executives need to develop robust risk management strategies to mitigate the potential negative impacts of AI, such as job displacement and data breaches.
- Legal teams need to be aware of the changing legal landscape surrounding AI.

This article was created in collaboration with ChatGPT, Grok by xAI, and Google Gemini.

Manus AI: The Dawn of Autonomous Agents and What It Means for Business