Use A2A to connect agents across different frameworks and teams
Skills:
Agent Foundations80%
Learn more: https://bit.ly/4anVtk6
Announcing a new short course, A2A: The Agent2Agent Protocol, built in collaboration with Google Cloud and IBM Research.
As agentic systems grow more complex, connecting agents built with different frameworks often means writing custom integrations. A2A is an open protocol that standardizes how agents discover each other and communicate, making cross-framework collaboration far easier. Originally introduced by Google Cloud and now stewarded by the Linux Foundation, A2A is quickly becoming a shared standard for agent collaboration.
In this hands-on course, you’ll build a healthcare multi-agent system using agents created with different frameworks. You’ll expose agents as A2A servers, create A2A clients, and orchestrate agents into sequential and hierarchical workflows. You’ll also see how A2A complements MCP: while MCP connects agents to external data systems, A2A enables agents to work with each other.
You’ll learn how to:
- Implement A2A’s client-server architecture, including agent cards and lifecycle concepts
- Expose agents built with ADK, LangGraph, or BeeAI as A2A-compliant servers
- Create A2A clients to connect and coordinate agents across frameworks
- Orchestrate multi-agent workflows dynamically and deploy them using open-source infrastructure
The course is taught by Holt Skinner, DevRel at Google, Ivan Nardini, AI/ML DevRel at Google, and Sandi Besen, Ecosystem Lead at IBM Research.
Enroll now: https://bit.ly/4anVtk6
Watch on YouTube ↗
(saves to browser)
Sign in to unlock AI tutor explanation · ⚡30
Playlist
Uploads from DeepLearningAI · DeepLearningAI · 0 of 60
← Previous
Next →
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
Forward and Backward Propagation (C1W4L06)
DeepLearningAI
deeplearning.ai's Heroes of Deep Learning: Yuanqing Lin
DeepLearningAI
deeplearning.ai's Heroes of Deep Learning: Ruslan Salakhutdinov
DeepLearningAI
deeplearning.ai's Heroes of Deep Learning: Yoshua Bengio
DeepLearningAI
deeplearning.ai's Heroes of Deep Learning: Pieter Abbeel
DeepLearningAI
deeplearning.ai's Heroes of Deep Learning: Ian Goodfellow
DeepLearningAI
deeplearning.ai's Heroes of Deep Learning: Andrej Karpathy
DeepLearningAI
Using an Appropriate Scale (C2W3L02)
DeepLearningAI
Gradient Checking (C2W1L13)
DeepLearningAI
Gradient Checking Implementation Notes (C2W1L14)
DeepLearningAI
Learning Rate Decay (C2W2L09)
DeepLearningAI
Understanding Mini-Batch Gradient Dexcent (C2W2L02)
DeepLearningAI
Mini Batch Gradient Descent (C2W2L01)
DeepLearningAI
The Problem of Local Optima (C2W3L10)
DeepLearningAI
Exponentially Weighted Averages (C2W2L03)
DeepLearningAI
Tuning Process (C2W3L01)
DeepLearningAI
Understanding Exponentially Weighted Averages (C2W2L04)
DeepLearningAI
Bias Correction of Exponentially Weighted Averages (C2W2L05)
DeepLearningAI
Gradient Descent With Momentum (C2W2L06)
DeepLearningAI
Normalizing Activations in a Network (C2W3L04)
DeepLearningAI
Hyperparameter Tuning in Practice (C2W3L03)
DeepLearningAI
Adam Optimization Algorithm (C2W2L08)
DeepLearningAI
RMSProp (C2W2L07)
DeepLearningAI
Fitting Batch Norm Into Neural Networks (C2W3L05)
DeepLearningAI
Why Does Batch Norm Work? (C2W3L06)
DeepLearningAI
Batch Norm At Test Time (C2W3L07)
DeepLearningAI
Softmax Regression (C2W3L08)
DeepLearningAI
Deep Learning Frameworks (C2W3L10)
DeepLearningAI
Neural Network Overview (C1W3L01)
DeepLearningAI
Training Softmax Classifier (C2W3L09)
DeepLearningAI
Why Deep Representations? (C1W4L04)
DeepLearningAI
Gradient Descent For Neural Networks (C1W3L09)
DeepLearningAI
Neural Network Representations (C1W3L02)
DeepLearningAI
TensorFlow (C2W3L11)
DeepLearningAI
Activation Functions (C1W3L06)
DeepLearningAI
Explanation For Vectorized Implementation (C1W3L05)
DeepLearningAI
Getting Matrix Dimensions Right (C1W4L03)
DeepLearningAI
Understanding Dropout (C2W1L07)
DeepLearningAI
Building Blocks of a Deep Neural Network (C1W4L05)
DeepLearningAI
Why Non-linear Activation Functions (C1W3L07)
DeepLearningAI
Computing Neural Network Output (C1W3L03)
DeepLearningAI
Backpropagation Intuition (C1W3L10)
DeepLearningAI
Train/Dev/Test Sets (C2W1L01)
DeepLearningAI
Deep L-Layer Neural Network (C1W4L01)
DeepLearningAI
Random Initialization (C1W3L11)
DeepLearningAI
Other Regularization Methods (C2W1L08)
DeepLearningAI
Normalizing Inputs (C2W1L09)
DeepLearningAI
Derivatives Of Activation Functions (C1W3L08)
DeepLearningAI
Parameters vs Hyperparameters (C1W4L07)
DeepLearningAI
Vectorizing Across Multiple Examples (C1W3L04)
DeepLearningAI
What does this have to do with the brain? (C1W4L08)
DeepLearningAI
Dropout Regularization (C2W1L06)
DeepLearningAI
Vanishing/Exploding Gradients (C2W1L10)
DeepLearningAI
Basic Recipe for Machine Learning (C2W1L03)
DeepLearningAI
Bias/Variance (C2W1L02)
DeepLearningAI
Forward Propagation in a Deep Network (C1W4L02)
DeepLearningAI
Weight Initialization in a Deep Network (C2W1L11)
DeepLearningAI
Numerical Approximations of Gradients (C2W1L12)
DeepLearningAI
Regularization (C2W1L04)
DeepLearningAI
Why Regularization Reduces Overfitting (C2W1L05)
DeepLearningAI
More on: Agent Foundations
View skill →
Related AI Lessons
⚡
⚡
⚡
⚡
The AI Bridge Problem: Why Enterprise AI Integration Is an Architecture Challenge, Not an AI Challenge
Dev.to AI
BizNode's self-healing watchdog auto-restarts crashed services. Zero downtime, zero babysitting needed
Dev.to AI
Restrict access to sensitive documents in your Amazon Quick knowledge bases for Amazon S3
AWS Machine Learning
The Context Layer: Why Enterprise AI Agents Fail Without It — and What It Actually Takes to Fix That
Dev.to · Swapnil Chougule
🎓
Tutor Explanation
DeepCamp AI