This week: Meet the AI agent that works independently to complete complex tasks. We explore how autonomous agents are reshaping workflows, demystify what makes them different from assistants, and offer a hands-on way to simulate real-world interactions with AI.

AI in Action

Manus AI - from assistant to autonomous execution [Productivity]

In the rapidly evolving landscape of artificial intelligence, Manus AI is drawing attention as one of the boldest steps toward full autonomy. Developed by Chinese startup Monica, Manus isn’t just a chatbot or productivity tool, it’s a general purpose AI agent capable of working independently across a wide range of tasks. It operates asynchronously in the cloud, meaning it can continue researching, analyzing, or building while you’re offline and deliver results when you return. Its goa is to act less like a reactive assistant and more like an independent executor.

Manus is already being put to work across industries. Financial analysts use it to pull real time stock data, explore correlations across equities, and generate visual dashboards to support investment decisions. E-commerce businesses use it to analyze sales trends and identify high performing products. Operations teams have deployed Manus to source suppliers and evaluate vendor performance. Even content creators have used it to generate web pages or video scripts all with minimal prompting. It’s not just completing tasks, it decides how to do them.

The engine behind Manus is a combination of several large models, including Claude 3.5 and Alibaba’s Qwen. While it has already outperformed many peers in the GAIA benchmark, it is not without flaws. Users have reported occasional crashes, privacy concerns, and vague explanations for failed tasks. Still, what Manus represents is more than just a powerful product. It’s part of a shift from AI as a tool you operate, to systems that operate on your behalf. As agents like Manus become more capable, we’ll need to rethink how work gets done, how responsibilities are shared, and how to prepare people to collaborate with technologies that take initiative.

Bits of Brilliance

AI Agent vs. Autonomous Agent [AI Concepts]

If Manus AI left you wondering what exactly an “autonomous agent” is, you’re not alone.

First, let’s talk about an AI agent. It is any system that takes input, processes it, and responds in a goal-oriented way. Think of a virtual assistant like Siri or Google Assistant. It listens to your command (“Set a timer for 10 minutes”) and acts on it. These agents follow rules or models, but typically operate in a single input/output loop. They’re helpful, but reactive.

An autonomous agent, by contrast, is proactive. It can break down a goal into sub tasks, make decisions about how to approach them, and act without step by step guidance. Instead of waiting for a prompt, it might decide what data to collect, which tools to use, how to adjust its strategy, and when to stop. Autonomous agents like Manus don’t just respond to users, they take initiative.

The shift from AI agents to autonomous agents is like moving from Excel to interns. Excel is powerful, but only as smart as your formulas. Interns, on the other hand, can take a high-level goal, think for themselves, and figure out how to get it done. Sure, they may make mistakes, but that’s the tradeoff for delegation. And understanding this difference is key as AI agents become more deeply embedded in our work.

Try This

Let your chatbot interview and critique you [Everyday AI]

Instead of asking your chatbot for generic interview tips, try something smarter: have it interview you. Then have it switch roles and give feedback on your answers. The more realistic your setup, the more you gain from the rehearsal. And unlike a friend or colleague, it won’t get tired or hold back honest feedback.

Prompt to try

“You’re a hiring manager at a fintech firm. Interview me for a senior product manager role: Ask one question at a time. After each answer, give me feedback, focus on clarity, structure, and depth. Here is the job description: [insert job description]. ”

You can refine it further by 1)asking it to go deeper after each reply (“What would you do differently?”); 2)Simulate behavioral questions, technical screens, or stakeholder concerns; 3)Adjust tone: formal, casual, skeptical, supportive, etc.

You can try other simulations like giving a tough performance review, pitching your product to an investor, or walking through a roadmap with a skeptical exec. The beauty of this approach is that you’re no longer studying for the moment, you’re rehearsing it.

_{Byte-Sized Intelligence is a personal newsletter created for educational and informational purposes only. The content reflects the personal views of the author and does not represent the opinions of any employer or affiliated organization. This publication does not offer financial, investment, legal, or professional advice. Any references to tools, technologies, or companies are for illustrative purposes only and do not constitute endorsements. Readers should independently verify any information before acting on it. All AI-generated content or tool usage should be approached critically. Always apply human judgment and discretion when using or interpreting AI outputs.}