Most chatbots, such as ChatGPT and Claude, are becoming more powerful every day. They are incorporating more tools, characters and features, such as Canvas or Artifacts, to improve usability. However, especially if you are a heavy user of AI (especially as a non-coder), the limitations are the same: the more data and the more complex the tasks, the less AI becomes usable.
It becomes lazy and takes shortcuts.
It hallucinates. It forgets things. The quality degrades massively, and worst of all, you still pay for it.
Most of these issues are known limitations that happen because of one of the most limiting factors of AI: the context window. Think of it as the AI's limited working memory: the more data it contains, the more overwhelmed the AI becomes while still trying to please you. The result is a pure waste of time and money.
The Solution That Changes Everything
There have been a lot of advancements in this area trying to overcome these technical limitations, such as plugging in memories, but one incredibly powerful solution is multi-agency.
The AI breaks down tasks it has never seen before using its reasoning capabilities and sends them to other AIs (so-called subagents) to complete. Then it aggregates the results and answers the user's request.
In this approach, the so-called sub-agent starts with a fresh memory. It doesn't need to know the entire context; it just needs to know the subtask at hand. It executes the task, delivers the results and disappears. Any further subtasks start with a new LLM. This core difference to having one large LLM trying to do everything by itself changes the entire game.
Handling much more complex tasks becomes possible. You get much less hallucination and much higher quality. Think of those subagents focusing on one smaller task; they can perform much better than trying to handle a huge task all at once. And if you have parallelisation, the end-to-end experience can be much faster than single processing, though this also depends on the tooling of the multi-agent solution.
The Tools You Can Use Right Now
If you follow the news, you might have heard about Claude Cowork. Built on top of a framework developed by Anthropic a few months ago, called Agent SDK, Claude Cowork can process highly complex tasks end-to-end using a high-reasoning, multi-agent approach.
It develops a well-thought-out plan for accomplishing a given complex task from start to finish. It spawns multiple agents ad hoc (think of it as a scalable team on demand). It extends code in a sandbox environment, giving users the full power of coding without requiring any prior knowledge (e.g. reading and editing files, calling APIs, and much more).
This tool is incredibly powerful, but expensive, though worth the investment if you consider the ROI.
If you are reluctant to pay a monthly subscription fee of $100 to $200, you can also use the framework with code, or you can use Cherry Studio, an open-source chatbot that integrates this framework.
A Real World Example: Deep Analysis of Microsoft's 2025 Annual Report
This technology can be used to solve a variety of complex tasks, including those that require the use of tools. Imagine presenting a dense financial report to different experts (financial gurus, strategists, etc.) to obtain a comprehensive view of the results.
The coordinating AI (the one you are talking to in the chat) decides ad hoc how many agents to use, how to prompt them, and so on. You don't need any prior configuration. That's the real beauty of this amazing technology.
The process works like this: First, the system reads the contents of the report, then sends subtasks to multiple expert subagents. Each of these subtasks is a subagent with its own memory and tools. After a minute or so, you have a detailed analysis of the final report compiled from five different angles.
Cost Considerations
You might be wondering how much this will cost you. For a dense report with millions of tokens processed, you're looking at roughly $2.50 to $3.00 USD using Haiku 4.5, especially when cached tokens reduce the total cost significantly. If there's a lot at stake for you, it's more than worth every penny.
Getting Started in Three Steps
Try it yourself with Cherry Studio. Install Cherry Studio from the official repository, add the API key for Anthropic, and click 'Add Agent' on the right. Then select the model and create a scratch area. That's it.
Now you can start chatting with the agent and let it free you from those painful, boring tasks.
Read the full deep dive on airabbit.blog: https://airabbit.blog/agents-can-now-clone-themselves-and-do-crazy-things-part-i-deep-stock-analysis/
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