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Understanding the Current Landscape
In the realm of AI, it's crucial to grasp its current state. Over the past year, we've witnessed a transition from lofty expectations to tangible advancements. What once seemed like magic has now materialized into three distinct capabilities: generative AI, reasoning, and human-like interaction.
Generative AI, in particular, has emerged as a game-changer, enabling the creation of diverse content—from images to text and even video. This capability, coupled with the ability to reason, marks a significant shift in how software interacts with the world, resembling human-like cognition.
Navigating the Path Forward
When we look back on the journey of AI, it's clear that we're on the cusp of a major turning point —one defined by intelligence augmentation and collaborative problem-solving.
Just as previous waves of technology transformed society, AI promises to revolutionize productivity and redefine human-machine interactions. Looking ahead, we anticipate a convergence of AI with other transformative technologies, paving the way for groundbreaking applications across various sectors. From healthcare to education, AI holds the key to addressing complex challenges and unlocking new possibilities.
As we explore AI further, the concept of AI agents or autonomous agents in AI becomes increasingly significant. These agents represent the next evolution in AI, where machines not only generate content but also perform tasks autonomously. But what exactly are AI agents?
What is an Agent in the Context of GenAI?
An AI agent is a computer program that performs tasks autonomously by observing its past experiences, environment, making decisions, and executing actions. It is designed to achieve specific goals or tasks without human intervention.
The high-level concept of an AI agent involves recursive task completion with parallelized processing, justified and prioritized.
An autonomous AI agent possesses five key characteristics:
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Autonomous Function: Operates without ongoing human guidance.
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Environment Interaction: Observes and responds to its surroundings.
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Decision-Making: Processes information to make informed choices.
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Goal-Oriented: Programmed to achieve specific objectives.
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Action Execution: Carries out tasks to fulfill its designated role.
This is an incredible overview of LLM-powered autonomous agent systems.
The power of AI Agents: Transformative workflows
In the context of AI, our interactions often follow a non-agentic workflow, akin to typing a problem and receiving a generated answer. But what if we shifted to an agentic approach, mirroring human collaboration and iteration? Imagine an AI-powered essay writing process: instead of demanding a flawless draft in one go, we prompt an AI to outline, research, draft, revise, and iterate. This iterative workflow, involving multiple AI agents with diverse roles and expertise, forms the backbone of agentic reasoning.
While some may argue that AI agents are merely large language models (LLMs), the crux lies in their collaborative potential. The two major common areas currently where an everyday user can benefit from an AI agent are essay generation and code snippet generation.
Picture a team of agents—writers, reviewers, spell checkers, fact checkers—each contributing their specialized skills to refine an essay through iterative cycles. This will lead to enhanced productivity and remarkably better results than traditional non-agentic methods.
Take, for instance, coding tasks. While zero-shot prompting may yield decent results, the true power lies in wrapping an agentic workflow around LLMs. By leveraging reflection—prompting an AI to evaluate and improve its own output—alongside tool use and multi-agent collaboration, we witness exponential performance gains.
Andrew Ng, renowned AI expert, highlights that GPT-3.5, when integrated into an agentic workflow, can outperform GPT-4 in specific scenarios, demonstrating the powerful impact of collaborative reasoning.
“I truly believe the future of artificial intelligence is going to be agentic”
- Dr Andrew Ng
Key Design Patterns of AI Agent
Let's delve deeper into the key design patterns underpinning agentic workflows:
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Reflection: By prompting AI to assess and refine its output iteratively, we unlock superior performance and accuracy, akin to human self-correction and improvement.
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Tool Use: Equipping AI with pre-existing tools and libraries empowers it to execute tasks efficiently and reliably, leveraging existing resources to streamline workflows.
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Planning: Providing AI with the ability to strategize and plan steps enables it to tackle complex tasks methodically, mimicking human problem-solving approaches.
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Multi-Agent Collaboration: Harnessing the collective intelligence of multiple AI agents, each specialized in distinct roles, fosters synergy and innovation, driving performance to new heights.
These design patterns not only enhance productivity but also pave the way for groundbreaking advancements in AI capabilities. As we embrace agentic reasoning, we anticipate a paradigm shift in how we approach AI applications. By leveraging the collaborative potential of AI agents, we embark on a journey towards unlocking the full spectrum of AI's potential, one iterative step at a time.
Why is AI Agent an exciting opportunity?
With AI agents handling most tasks, a single person can achieve the same level of output and progress as a large team. Sam Altman, CEO of OpenAI, was proposing the concept of the “1-person billion-dollar company.” Even though some LLMs today can accept very long input contexts (for instance, Gemini 1.5 Pro accepts 1 million tokens), their ability to truly understand long, complex inputs are mixed. An agentic workflow in which the LLM is prompted to focus on one thing at a time can give better performance. By telling it when it should play software engineer, we can also specify what is important in that subtask.
“[Intelligent] autonomous agents are the natural endpoint of automation in general. In principle, an agent could be used to automate any other process. Once these agents become highly sophisticated and reliable, it is easy to imagine an exponential growth in automation across fields and industries.”
- Bojan Tunguz, Machine Learning at NVIDIA
Companies need to start preparing today for agents’ arrival to the mainstream in three to five years with a robust transformation roadmap.
Conclusion: Embracing the AI Future
AI's impact goes far beyond the things it can already do. While we're seeing exciting applications now, the future holds even greater possibilities for how AI can transform our world, signaling a productivity revolution comparable to historical milestones. This revolution unfolds in stages, evolving from simple tools to sophisticated machine assistants, and ultimately to intricate machine networks. The economic impact will be profound, reducing costs and enhancing efficiency across various industries.
Our research motivates a strong belief that existing and upcoming models will be significantly "smarter," particularly in terms of planning capabilities. This capability is key for executing complex tasks and will significantly increase the number of tasks that AI can perform.
Let's embrace this journey and explore the boundless opportunities AI has to offer.
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Sources:
https://youtube.com/watch?v=ZYf9V2fSFwU&si=nE02ea-LlxJ37ZHL, https://youtube.com/watch?v=9ZhbA0FHZYc&si=4qkd6HRGTywIEF1F, https://youtube.com/watch?v=TDPqt7ONUCY&si=kPHjQNdXPSLmi5aJ, https://blog.google/technology/ai/google-gemini-next-generation-model-february-2024/, https://lilianweng.github.io/posts/2023-06-23-agent/, https://www.deeplearning.ai/short-courses/ai-agentic-design-patterns-with-autogen/, https://www.bcg.com/publications/2023/gpt-was-only-the-beginning-autonomous-agents-are-coming, https://www.mattprd.com/p/the-complete-beginners-guide-to-autonomous-agents
