Our AI Agent
We launched an AI Agent to showcase you the impressive potential of Agentic AI!
In the meantime, we just launched an AI Agent to support your business needs.
How does it work?
You talk to it based on your business struggle to get anything done; the agent will automatically handle over 80 business tools!!!
Let’s say you don’t know where to start regarding ideas, names, plans, and business models for a business related to a vertical.
You ask our AI Agent, and it’ll help you on all of them simultaneously! No more prompting!
Why Agentic AI?
Since the launch of GPT-2 in 2019, the Gen AI paradigm has been based on prompting - for the last five years.
In short, the LLM completed any task based on a given instruction. The quality of the output highly depended upon the quality of the input (prompt).
However, in the last few weeks, we’ve finally seen the rise of Agentic AI, a new type of artificial intelligence that can solve complex problems independently using advanced reasoning and planning.
Unlike regular AI, which responds to single requests, agentic AI can handle multi-step tasks like improving supply chains, finding cybersecurity risks, or helping doctors with paperwork.
It works by gathering data, devising solutions, carrying out tasks, and learning from the results to improve over time.
What are the critical features of Agentic AI vs. Prompting?
• Autonomous Problem-Solving: Agentic AI uses sophisticated reasoning and iterative planning to solve complex, multi-step tasks independently.
• Four-Step Process: Perceive (gathers data), Reason (generates solutions), Act (executes tasks via APIs), and Learn (continuously improve through feedback).
• Enhanced Productivity: Automates routine tasks, allowing professionals to focus on more complex challenges, improving efficiency.
• Data Integration: This technique uses techniques like Retrieval-Augmented Generation (RAG) to access a wide range of data for accurate outputs and continuous improvement.
When did this Agentic AI wave start?
It all started two years back.
Indeed, Chain-of-Thought Prompting (CoT), the paper that spurred the current Agentic AI wave, only came out in early 2022.
And just like the transformer paper (Attention Is All You Need), which came out in 2017 and led to ChatGPT, CoT was also an effort of the Google Research and Brain Teams!
The "Chain-of-Thought Prompting" (CoT) paper, published in early 2022 by researchers from Google's Research and Brain Teams, has been pivotal in advancing the capabilities of large language models (LLMs).
This technique enhances LLMs' reasoning abilities by guiding them to generate intermediate steps that mirror human problem-solving processes by:
1. Enhanced Reasoning Capabilities: CoT prompting enables LLMs to tackle complex tasks by breaking them down into sequential steps, improving performance in areas like arithmetic, commonsense reasoning, and symbolic manipulation.
2. Emergent Abilities with Scale: The research demonstrated that as LLMs increase in size, their capacity for chain-of-thought reasoning naturally emerges, allowing them to handle more intricate problems effectively.
3. Influence on Agentic AI Development: The CoT paper has inspired the development of agentic AI systems capable of more autonomous and sophisticated decision-making by showcasing how LLMs can perform complex reasoning through structured prompting.
This progression mirrors the impact of the 2017 "Attention Is All You Need" paper, which introduced the transformer architecture and laid the groundwork for models like ChatGPT.
Both papers underscore the significant role of Google's research teams in propelling advancements in AI, particularly in enhancing the reasoning and comprehension abilities of language models.
And yet, guess what?
Most of them pushed OpenAI forward from a commercial application standpoint. OpenAI's GPT-4o, released in May 2024, incorporates principles from CoT to improve its reasoning capabilities.
By structuring prompts to encourage step-by-step thinking, GPT-4o can handle complex tasks such as mathematical problem-solving and logical reasoning more effectively.
This approach allows the model to break down intricate problems into manageable steps, leading to more accurate and coherent responses.
Back to our AI Agent
Once you jump on the AI Agent, you will notice that while you still “talk to it” rather than an actual prompt, what you input is a need you have, and the agent will figure out what tool to use based on your specific need.
For this first agent we launched, we allowed it to handle over 80 business tools to support you throughout the journey of finding what you need.
You’ll notice that the agent really “reasons through it” to figure out the best combination of tools to assist you with your needs.
For those of you who haven’t grasped it yet, while this is a simple showcase of what Agentic AI can do, this might be a turning point.
While the user input still matters, this can be easily made proactive. At the moment in which, let’s say, a device has access to the user's local context, the AI Agents will be able to execute tasks automatically without you even describing what you need, as the agent will implicitly understand what you need via the local context. Of course, the Agent has been trained in various feedback loops, such as when to execute or not, and it can always ask whether you need help in the task execution…
That’s the next revolution in the coming 2-3 years!
Connected Reading List
Ciao!
With ♥️ Gennaro, FourWeekMBA
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This was a very simple and effective explanation to understand Agentic AI as way of introducing your own model.