The rise of AI agents with João Moura of CrewAI

Weights & Biases · Beginner ·🤖 AI Agents & Automation ·1y ago
In this episode of Gradient Dissent, host Lukas Biewald sits down with João Moura, CEO & Founder of CrewAI, one of the leading platforms enabling AI agents for enterprise applications. Joe shares insights into how AI agents are being successfully deployed in over 40% of Fortune 500 companies, what tools these agents rely on, and how software companies are adapting to an agentic world. They also discuss: - What defines a true AI agent versus simple automation - How AI agents are transforming business processes in industries like finance, insurance, and software - The evolving business models for APIs as AI agents become the dominant software users - What the next breakthroughs in agentic AI might look like in 2025 and beyond If you're curious about the cutting edge of AI automation, enterprise AI adoption, and the real impact of multi-agent systems, this episode is packed with essential insights. Timestamps: 00:00 Introduction to Joe Mora and the rise of AI agents 03:17 What AI agents are actually doing in companies today 05:07 Signals of success: What makes AI agent adoption work? 07:14 Defining AI agents: When is it real and when is it hype? 09:06 The role of tool use in AI agent success 10:08 How Salesforce, LinkedIn, and others are rethinking their pricing models for agents 12:01 How Crew AI reached 40% of the Fortune 500 16:08 AI agents for research: A 21-agent team working on market intelligence 18:09 How AI agents can interact with humans to avoid errors 22:50 Open-source vs. closed-source AI models for agents 26:17 AI agent memory: Short-term, long-term, and entity memory 29:37 The impact of R1 and open-source models on AI agents 34:41 Where AI agents still struggle and what’s missing today 42:44 Will AI agent building become completely no-code? 47:57 How Crew AI uses agents internally for marketing, development, and automation 48:09 Joe Mora’s predictions for AI in 2025 🎙 Get our podcasts on these platforms: Apple Podcasts: http://wandb.me/apple-podcas
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0. What is machine learning?
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1. Build Your First Machine Learning Model
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 3 Intro to ML: Course Overview
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 4 2. Multi-Layer Perceptrons
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 9 5. Sentiment Analysis
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 10 6. Recurrent Neural Networks [RNNs]
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 11 7. Text Generation using LSTMs and GRUs
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 12 8. Text Classification Using Convolutional Neural Networks
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 13 9. Hybrid LSTMs [Long Short-Term Memory]
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 14 Toyota Research Institute on Experiment Tracking with Weights & Biases
Toyota Research Institute on Experiment Tracking with Weights & Biases
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 15 Weights and Biases - Developer Tools for Deep Learning
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 17 10. Seq2Seq Models
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 18 11. Transfer Learning for Domain-Specific Image Classification with Small Datasets
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 19 12. One-shot learning for teaching neural networks to classify objects never seen before
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 20 13. Speech Recognition with Convolutional Neural Networks in Keras/TensorFlow
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 21 14. Data Augmentation | Keras
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 22 15. Batch Size and Learning Rate in CNNs
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 23 Applied Deep Learning Fellowship Overview and Project Selection with Josh Tobin (2019)
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 24 Grading Rubric for AI Applications with Sergey Karayev (2019)
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 25 16. Video Frame Prediction using CNNs and LSTMs (2019)
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 26 Image to LaTeX - Applied Deep Learning Fellowship (2019)
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 27 17. Build and Deploy an Emotion Classifier (2019)
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 28 Applied Deep Learning - Data Management with Josh Tobin (2019)
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 29 Snorkel: Programming Training Data with Paroma Varma of Stanford University (2019)
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 30 Applied Deep Learning - Troubleshooting and Debugging with Josh Tobin (2019)
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 31 Troubleshooting and Iterating ML Models with Lee Redden (2019)
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 32 Designing a Machine Learning Project with Neal Khosla (2019)
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 33 Lukas Beiwald on ML Tools and Experiment Management (2019)
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 34 Building Machine Learning Teams with Josh Tobin (2019)
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 35 Pieter Abeel on Potential Deep Learning Research Directions (2019)
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 36 Testing and Deployment of Deep Learning Models with Josh Tobin (2019)
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 37 Five Lessons for Team-Oriented Research with Peter Welder (2019)
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 38 Applied Deep Learning - Rosanne Liu on AI Research (2019)
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 39 Making the Mid-career Leap from Urban Design to Deep Learning/Data Science
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 40 Organizing ML projects — W&B walkthrough (2020)
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 41 Brandon Rohrer — Machine Learning in Production for Robots
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 42 Nicolas Koumchatzky — Machine Learning in Production for Self-Driving Cars
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 43 My experiments with Reinforcement Learning with Jariullah Safi
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 44 Applications of Machine Learning to COVID-19 Research with Isaac Godfried
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 45 Testing Machine Learning Models with Eric Schles
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 46 How Linear Algebra is not like Algebra with Charles Frye
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 47 Predicting Protein Structures using Deep Learning with Jonathan King
Predicting Protein Structures using Deep Learning with Jonathan King
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 48 Rachael Tatman — Conversational AI and Linguistics
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 49 Reformer by Han Lee
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 50 Sequence Models with Pujaa Rajan
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 51 GitHub Actions & Machine Learning Workflows with Hamel Husain
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 52 Look Mom, No Indices! Vector Calculus with the Fréchet Derivative by Charles Frye
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 53 Jack Clark — Building Trustworthy AI Systems
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 54 Surprising Utility of Surprise: Why ML Uses Negative Log Probabilities - Charles Frye
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 55 Track your machine learning experiments locally, with W&B Local - Chris Van Pelt
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 56 Antipatterns in open source research code with Jariullah Safi
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 57 Attention for time series forecasting & COVID predictions - Isaac Godfried
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 58 Made with ML - Goku Mohandas
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 59 Angela & Danielle — Designing ML Models for Millions of Consumer Robots
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Chapters (16)

Introduction to Joe Mora and the rise of AI agents
3:17 What AI agents are actually doing in companies today
5:07 Signals of success: What makes AI agent adoption work?
7:14 Defining AI agents: When is it real and when is it hype?
9:06 The role of tool use in AI agent success
10:08 How Salesforce, LinkedIn, and others are rethinking their pricing models for age
12:01 How Crew AI reached 40% of the Fortune 500
16:08 AI agents for research: A 21-agent team working on market intelligence
18:09 How AI agents can interact with humans to avoid errors
22:50 Open-source vs. closed-source AI models for agents
26:17 AI agent memory: Short-term, long-term, and entity memory
29:37 The impact of R1 and open-source models on AI agents
34:41 Where AI agents still struggle and what’s missing today
42:44 Will AI agent building become completely no-code?
47:57 How Crew AI uses agents internally for marketing, development, and automation
48:09 Joe Mora’s predictions for AI in 2025
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