AI Pioneer Shows The Power of AI AGENTS - "The Future Is Agentic"

Dr. Andrew Ning's Insights on Agentic Workflows

Introduction to Dr. Andrew Ning

• Dr. Andrew Ning recently spoke at Sequoia, expressing strong optimism about agents in AI.

• He is a prominent computer scientist, co-founder of Google Brain, and former Chief Scientist at Baidu.

• Co-founded Coursera, providing free access to education in various fields including computer science.

Sequoia Capital's Influence

• Sequoia is a legendary venture capital firm with significant influence in Silicon Valley.

• Their portfolio accounts for over 25% of the total market capitalization of NASDAQ-listed companies.

• Notable investments include Reddit, Instacart, DoorDash, Airbnb, Apple, and Zoom.

Understanding Agentic Workflows

• Traditional use of LLMs involves non-agentic workflows where prompts are given without iteration.

• An agentic workflow allows for iterative processes: writing outlines, conducting research, drafting revisions.

• The power lies in multiple agents collaborating with distinct roles (e.g., writer, reviewer).

Benefits of Iterative Processes

• Human-like planning and iteration lead to superior outcomes compared to single-pass approaches.

• Case studies show that agent workflows yield significantly better results than zero-shot prompting methods.

Performance Metrics

• Zero-shot prompting yields 48% accuracy with GPT 3.5; GPT 4 improves this to 67%.

• When wrapped in an agentic workflow, GPT 3.5 outperforms even GPT 4’s standalone performance.

Understanding Agents in AI Development

Overview of Agent Design Patterns

• The speaker discusses the chaotic landscape of AI agents, emphasizing the need for concrete design patterns amidst extensive research and open-source projects.

• Reflection is highlighted as a widely used tool that enhances the performance of large language models (LLMs), making them more robust.

Key Concepts in Agent Functionality

• Reflection: This involves prompting LLMs to evaluate their previous outputs and suggest improvements, leading to better results.

• Tool Use: Refers to equipping LLMs with various tools (e.g., web scraping, SEC lookup) that they can utilize based on their descriptions, significantly expanding their capabilities.

Planning and Collaboration Among Agents

• Planning: Encourages LLMs to think through steps methodically by explaining reasoning step-by-step, which often yields superior outcomes.

• Multi-Agent Collaboration: Emerging technology where multiple agents work together. It allows different models to provide diverse perspectives during collaboration.

Practical Applications of Reflection

• An example illustrates how self-reflection can improve code quality. By prompting an LLM to review its own generated code for errors, it can produce a refined version.

• The process includes giving feedback on generated code and allowing the model to correct itself based on identified issues.

Automation in Coding with Agents

• The speaker describes automating coding tasks using agents that can run unit tests and troubleshoot failures autonomously.

• Recommendations for further reading are provided at the end of each section, encouraging deeper exploration into these technologies.

Evolving Multi-Agent Systems

• A natural evolution involves having two distinct agents—one for coding and another for reviewing—prompted differently but potentially utilizing the same base model.

General Purpose Technology in Workflows

Enhancing Performance with Language Models

• The implementation of general-purpose technology can significantly boost the performance of Learning Management Systems (LMS).

• Current language models (LMs) can perform web searches and generate code, showcasing their versatility across various workflows.

• Early advancements in LMs were heavily influenced by the computer vision community due to limitations in handling images before models like GPT-4.

Tool Use and Its Importance

• Tool use is crucial as it allows LMs to execute hardcoded functions consistently, ensuring reliable outputs compared to other LMs that may vary results.

• Existing tools such as external libraries and API calls can be integrated into LMs without needing to rewrite them from scratch, enhancing productivity.

The Role of Planning Algorithms

AI Agents and Their Capabilities

• Many users experience a "wow" moment when interacting with planning algorithms, realizing the potential of AI agents for autonomous decision-making.

• An example from Hugging GPT illustrates how AI agents can autonomously determine tasks based on user prompts, demonstrating their evolving capabilities.

Iterative Improvement in Agents

• Despite being finicky at times, AI agents can recover from failures through iterative processes, making them increasingly powerful over time.

• As agentic models improve alongside better tooling frameworks like Crew AI and Autogen, the reliability of these agents is expected to increase significantly.

Multi-Agent Collaboration

Leveraging Multiple Agents for Enhanced Performance

• Multi-agent systems like Chat Dev allow different roles (e.g., CEO, designer, product manager) to collaborate effectively on projects.

• These systems demonstrate surprising complexity in generating functional programs through collaborative efforts among specialized agents.

Benefits of Diverse Agent Models

Agentic Workflows and AI Inference Speed

The Importance of Agentic Reasoning Design Patterns

• The speaker emphasizes the potential for a significant increase in tasks that AI can perform due to agentic workflows, suggesting a transformative year ahead.

• Reflecting on past experiences at Google, the speaker notes that users expect instant feedback from AI systems, which contrasts with the patience required for agent-based workflows.

Challenges of Patience in AI Interactions

• Users often struggle with waiting for responses from AI agents, similar to how novice managers check in too soon after delegating tasks.

• The discussion introduces "grock," an architecture capable of processing 500 to 850 tokens per second, highlighting its efficiency compared to traditional models.

Hyper Inference Speed and Its Implications

• With grock's speed, tasks that typically take minutes could be completed almost instantly, shifting focus from inference time to other bottlenecks like web searches or API calls.

• The speaker argues that leveraging fast token generation can enhance agent workflows by allowing more iterations and potentially better results even from lower-quality language models.

Future Models and Their Impact on AGI Progress

• Anticipation builds around upcoming models like Cloud 5 and Gemini 2.0, suggesting they may achieve performance levels previously thought unattainable through agent reasoning.

• The journey towards Artificial General Intelligence (AGI) is framed as ongoing; advancements in agent workflows are seen as incremental steps forward.

Commoditization of Token Costs

• As new models emerge, the costs associated with token usage are expected to stabilize. This will likely make advanced capabilities more accessible.