Why AI Agent is the next frontier for the AI field
GenAI was just the beginning. The future of AI will be all about AI Agents!
So why read this now?
This article looks at AI agents and their growing role in our future.
The Future of Work is changing now.
We'll discuss what this means and what top AI experts are saying about AI agents.
Whether you're experienced in AI or new to it, understanding AI agents is key to staying updated and involved, especially for product development.
AI is evolving from simple models to powerful agents that enhance human intelligence in many areas. This shift will change how we work, live, and interact with technology.
This article is a must-read for organization and product leaders who want to:
Let’s dive in…
1- So what’s next for GenAI?
I spent hours on AI research papers written by Andrew Ng, Andrej Karpathy and many other AI experts. And there is a trend from LLM to RAG to AI Agent.
Here’s the trend pattern in the image below.
Find out what top AI experts are saying about AI Agents...
The future is Agentic! Here are the four workflows that I believe will drive significant progress: Reflection, Tool use, Planning and Multi-agent collaboration.?
Andrew Ng DeepLearning AI
AI agents will become the primary way we interact with computers in the future. They will be able to understand our needs and preferences, and proactively help us with tasks and decision-making.
Satya Nadella, CEO of Microsoft
By end of 2024, AI will power 60% of personal device interactions, with Gen Z adopting AI agents as their preferred method of interaction."
Sundar Pichai, CEO of Google
2- Why do we need AI Agents when we have LLMs & RAGs?
From a product manager angle, it's crucial to understand how things are getting built and why AI agents are becoming essential, even with the advancements of LLMs (ie Large Language Models) and RAGs (ie Retrieval-Augmented Generation).
Below are the main characteristics:
LLMs and RAG models generate text based on patterns in training data, but they can't set or pursue specific goals. AI agents, however, can be designed with explicit goals, planning, and actions to achieve them. This goal-oriented behavior is vital for product managers looking to build intelligent, purposeful softwares.
Most language models process each input independently without persistent memory. In contrast, AI agents can maintain an internal state, accumulating knowledge over time to inform future decisions and actions. This capability allows for more intelligent and adaptive product development.
LLMs operate in the text domain without direct interaction with the physical world. AI agents can perceive and act upon their environment, be it digital or physical. For product managers, this means creating products that can interact with and respond to real-world scenarios.
LLMs excel at tasks similar to their training data but struggle with entirely new domains. AI agents have the potential to learn, reason, and plan, allowing for better transfer and generalization to novel situations. This adaptability is key for product managers aiming to innovate across diverse use cases.
Once trained, most language models are static. AI agents, on the other hand, can continuously learn and adapt as they interact with new environments and situations (like humans). This ongoing learning process is crucial for developing products that evolve with user needs.
LLMs are typically specialized for specific language tasks. AI agents can be general, multi-task systems, combining skills like language, reasoning, perception, and control. This versatility is beneficial for product managers dealing with complex, multi-faceted problems.
See below the illustration on the future of work.
Why AI Agents Matter?
While LLMs and RAGs have pushed the boundaries of language generation, AI agents represent a step towards more intelligent, autonomous, and multi-capable systems. They offer the ability to truly understand, learn, and solve real-world problems, making them indispensable for the future of product management.
Embrace the shift towards AI agents to enhance your products and stay ahead in the rapidly evolving tech landscape.
3- How AI Agents Will Change the World
Why AI Agents Matter
Product leaders need to recognize that AI agents are not just another technological advancement—they are a game-changer. Embracing AI agents can revolutionize the way we develop products, enhancing user experiences and driving efficiency. Stay ahead of the curve by understanding and integrating AI agents into your product strategy.
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LLM: Can go deep into places to visit or give travel tips.
RAG: Can find blogs and articles about destinations for your specific situation today
AI Agent: Can do all that plus:
4- Understanding LLM, RAG, vs AI Agents
Let’s quickly contrast those frameworks so you can remember them.
1. Task Orientation vs. General Knowledge
LLMs: Great for broad language understanding and generation, like vast libraries of info.
RAG: Enhances LLMs by finding relevant info, still focusing on knowledge and text.
???AI Agents: Built with specific goals, they bridge understanding language and taking real-world or digital actions.
2. Multi-Step Reasoning
LLMs & RAG: Work on single inputs and provide responses.
???AI Agents: Chain together multiple steps:
3. Proactivity
LLMs & RAG: Respond to direct prompts.
???AI Agents: Can be proactive:
4. Integration with Existing Systems
LLMs & RAG: Operate within their own environment.
???AI Agents: Interface with various systems and APIs:
5- What does the Architecture of an AI Agent Entail?
An AI agent’s architecture includes key components that enable it to think, plan, and act in its environment.
This design typically involves:
1. Reasoning Engine
The Core: Uses a powerful Large Language Model (LLM) to understand natural language, access knowledge, and reason through complex problems.
2. Knowledge Base
Memory Store: Houses factual information, past experiences, and preferences relevant to its tasks.
3. Tool Integration
Interaction Capability: Connects with various software applications and services through APIs, allowing the agent to manipulate and control its environment.
4. Sensory Input
Perception: Gathers data from text, images, or various sensors to perceive its surroundings.
5. User Interface (Maybe)
Communication Bridge: Enables seamless interaction and collaboration with human users. (While not standardized yet, a user-friendly UX is essential and may become standard soon.)
That’s it for now, until next time…
Key Takeaways
Top 5 Key Takeaways on AI Agents
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Jean-Michel VAN is an avid (slow) runner, a foodie (only dessert), and a tech product leader, helping product teams and product managers accelerate their impact and leadership with tactical frameworks and candid tips - and ultimately helping them build better products.
He shares his journey at Another PM Day, and is a coach at Product School and Product-Led Alliance.
Connect with JM on LinkedIn
MIT Alum | Co-founder & CEO of Opsin | Helping enterprises securely connect their LLMs to data sources
3 周Great post on AI Agent Jean-Michel VAN!
Unlocking innovation through validating ideas, finding problem-solution fit and product-market fit, instilling learning, experimentation and reflection culture and government funding
2 个月AI agents are definitely the way forward as they can be tailored to specific tasks. Some of the AI software solutions for SME processes or challenges can be too broad and not get the job done as well as an agent.
- securing the art of the possible
2 个月And let’s add Reuven Cohen to the mix… proper hands-on engineering
Great post Jean-Michel VAN Come by r/aiagents or the weekly live event in ai hackerspace here on linkedin.