mcp vs agent to agent protocols

MLOps.community · Intermediate ·🤖 AI Agents & Automation ·1y ago

Key Takeaways

MCP vs Agent to Agent protocols for AI agent control and delegation, highlighting the trade-offs between specific control and blackbox handoffs approach.

Full Transcript

Are you considering agent to agent a competitor or the same as MC? I think it's going to eat the cake. You really? Yes. Why? Okay. So, think about it from a um perspective of the business side of things, not the technical side of things. Um, I want specific control over how step-by-step actions get performed inside of my inside of my agent product or right my product to our agent. I will use MCP but the promise of A2A is that you delegate. Yeah, it's a handoffs approach instead of and so it is also as per the docs says that it's blackbox. So which means that I'll tell you how to do it and then you because you are you know all wise with your tool know how to solve it. So now the question is is that better or worse, right? I think it's about the eventually the dynamics of like who's going to do the job better. But at least with MCP without the other protocols, MCP was going to do everything for you. Your agent was going to like each and every task, right? But now you have a delegation mechanism wherein suddenly the value prop gets split between different people and A2A comes in like supports it. So that's my point about eating the cake. That's fair.
Watch on YouTube ↗ (saves to browser)
Sign in to unlock AI tutor explanation · ⚡30

Playlist

Uploads from MLOps.community · MLOps.community · 0 of 60

← Previous Next →
1 Our 1st MLOps Meetup // Luke Marsden // MLOps Meetup #1
Our 1st MLOps Meetup // Luke Marsden // MLOps Meetup #1
MLOps.community
 2 Remote Collaboration as a Data Scientist
Remote Collaboration as a Data Scientist
MLOps.community
 3 MLOps Manifesto with Luke Marsden from Dotscience
MLOps Manifesto with Luke Marsden from Dotscience
MLOps.community
 4 MLOps lifecycle description
MLOps lifecycle description
MLOps.community
 5 What Does Best in Class AI/ML Governance Look Like in Fin Services? // Charles Radclyffe // MLOps #2
What Does Best in Class AI/ML Governance Look Like in Fin Services? // Charles Radclyffe // MLOps #2
MLOps.community
 6 Life purpose and too many spreadsheets
Life purpose and too many spreadsheets
MLOps.community
 7 Explainability, Black boxes and EU white paper on reproducibility
Explainability, Black boxes and EU white paper on reproducibility
MLOps.community
 8 Hierarchy of Machine Learning Needs // Phil Winder // MLOps Meetup #3
Hierarchy of Machine Learning Needs // Phil Winder // MLOps Meetup #3
MLOps.community
 9 Automatically Retrain Machine Learning Models? Are best practices worth it?
Automatically Retrain Machine Learning Models? Are best practices worth it?
MLOps.community
 10 Building an MLOps Team? Key ideas to keep in mind
Building an MLOps Team? Key ideas to keep in mind
MLOps.community
 11 Hierarchy of MLOps Needs
Hierarchy of MLOps Needs
MLOps.community
 12 Bare necessities for getting an ML model into production
Bare necessities for getting an ML model into production
MLOps.community
 13 MLOps and Monitoring
MLOps and Monitoring
MLOps.community
 14 How Phil Winder got into Data Science and Software Engineering
How Phil Winder got into Data Science and Software Engineering
MLOps.community
 15 Provenance and Reproducibility in Machine Learning; what is it and why you need it?
Provenance and Reproducibility in Machine Learning; what is it and why you need it?
MLOps.community
 16 Friction Between Data Scientists and Software Engineers
Friction Between Data Scientists and Software Engineers
MLOps.community
 17 MLOps Problems in different size companies
MLOps Problems in different size companies
MLOps.community
 18 ML tooling in large companies
ML tooling in large companies
MLOps.community
 19 ML Platforms - The build vs buy question
ML Platforms - The build vs buy question
MLOps.community
 20 ML Services Gateway at SurveyMonkey
ML Services Gateway at SurveyMonkey
MLOps.community
 21 Message buses, Async and sync architecture
Message buses, Async and sync architecture
MLOps.community
 22 MLOps #4: Shubhi Jain - Building an ML Platform @SurveyMonkey
MLOps #4: Shubhi Jain - Building an ML Platform @SurveyMonkey
MLOps.community
 23 Hybrid Data Science Teams @SurveyMonkey
Hybrid Data Science Teams @SurveyMonkey
MLOps.community
 24 How do you handle ML version control at SurveyMonkey
How do you handle ML version control at SurveyMonkey
MLOps.community
 25 Doing ML with Personal Information
Doing ML with Personal Information
MLOps.community
 26 Evolution of the ML feature store @SurveyMonkey
Evolution of the ML feature store @SurveyMonkey
MLOps.community
 27 Developing a Machine Learning Feature Store
Developing a Machine Learning Feature Store
MLOps.community
 28 Auto retrain ML models is not the question
Auto retrain ML models is not the question
MLOps.community
 29 3 key parts to Machine Learning monitoring
3 key parts to Machine Learning monitoring
MLOps.community
 30 MLOps Meetup #6: Mid-Scale Production Feature Engineering with Dr. Venkata Pingali
MLOps Meetup #6: Mid-Scale Production Feature Engineering with Dr. Venkata Pingali
MLOps.community
 31 MLOps meetup #5 High Stakes ML: Active Failures, Latent Factors with Flavio Clesio
MLOps meetup #5 High Stakes ML: Active Failures, Latent Factors with Flavio Clesio
MLOps.community
 32 MLOps: Airflow Pros and Cons
MLOps: Airflow Pros and Cons
MLOps.community
 33 Specific challenges in Machine Learning
Specific challenges in Machine Learning
MLOps.community
 34 Current State Of Machine Learning
Current State Of Machine Learning
MLOps.community
 35 Humans in the Loop are a defining factor in Machine Learning
Humans in the Loop are a defining factor in Machine Learning
MLOps.community
 36 Learning from real life Machine Learning failures
Learning from real life Machine Learning failures
MLOps.community
 37 Survivorship Bias in machine learning tutorials
Survivorship Bias in machine learning tutorials
MLOps.community
 38 Swiss Cheese model in Machine Learning
Swiss Cheese model in Machine Learning
MLOps.community
 39 Resume driven development in Machine learning & software engineering
Resume driven development in Machine learning & software engineering
MLOps.community
 40 Who has the highest standards in ML?
Who has the highest standards in ML?
MLOps.community
 41 Venkata Pingali of Scribble Data Thoughts on the Current State of Machine Learning
Venkata Pingali of Scribble Data Thoughts on the Current State of Machine Learning
MLOps.community
 42 Dependable data and being able to Trust in your Data with Venkata Pengali of Scribble Data
Dependable data and being able to Trust in your Data with Venkata Pengali of Scribble Data
MLOps.community
 43 Speed, Trust, Evolution and Scale in MLOps
Speed, Trust, Evolution and Scale in MLOps
MLOps.community
 44 More difficult transition for data scientists to become ML engineers
More difficult transition for data scientists to become ML engineers
MLOps.community
 45 How many models in prod til I need a dedicated ML platform?
How many models in prod til I need a dedicated ML platform?
MLOps.community
 46 Deeper thinking from data scientists around platform blackholes
Deeper thinking from data scientists around platform blackholes
MLOps.community
 47 Checkpointing, metadata, and confidence in your data
Checkpointing, metadata, and confidence in your data
MLOps.community
 48 Adjacent usecases and multistep feature engineering
Adjacent usecases and multistep feature engineering
MLOps.community
 49 Standardization of Machine Learning tools like in Software Engineering with Venkata Pingali
Standardization of Machine Learning tools like in Software Engineering with Venkata Pingali
MLOps.community
 50 Reproducability flaws in end to end Machine Learning debugging
Reproducability flaws in end to end Machine Learning debugging
MLOps.community
 51 3rd wave of data scientists
3rd wave of data scientists
MLOps.community
 52 MLOps meetup #7 Alex Spanos // TrueLayer 's MLOps Pipeline
MLOps meetup #7 Alex Spanos // TrueLayer 's MLOps Pipeline
MLOps.community
 53 MLOps Meetup #8 Optimizing Your ML Workflow with Kubeflow 1.0
MLOps Meetup #8 Optimizing Your ML Workflow with Kubeflow 1.0
MLOps.community
 54 Are Kubeflow and Airflow complementary?
Are Kubeflow and Airflow complementary?
MLOps.community
 55 Why Kubeflow gained so much traction=open community
Why Kubeflow gained so much traction=open community
MLOps.community
 56 Who decides the dirrection of Kubeflow
Who decides the dirrection of Kubeflow
MLOps.community
 57 What do Kubeflow and Arrikto do and how do they work together?
What do Kubeflow and Arrikto do and how do they work together?
MLOps.community
 58 Versioning your ML steps with Kubeflow
Versioning your ML steps with Kubeflow
MLOps.community
 59 Machine Learning Lifecycles//Perception vs Reality
Machine Learning Lifecycles//Perception vs Reality
MLOps.community
 60 Kubeflow vs SageMaker in Machine Learning
Kubeflow vs SageMaker in Machine Learning
MLOps.community

This video discusses the differences between MCP and Agent to Agent protocols for AI agent control, highlighting the benefits and drawbacks of each approach. It emphasizes the importance of considering the business and technical sides of agent development. By understanding the trade-offs between specific control and blackbox handoffs, developers can make informed decisions about which protocol to use.

Key Takeaways
  1. Evaluate the need for specific control over agent actions
  2. Consider the benefits of a blackbox handoffs approach
  3. Assess the value prop of delegation mechanisms
  4. Choose between MCP and A2A protocols based on project requirements
  5. Implement the chosen protocol in the agent product
💡 The choice between MCP and A2A protocols depends on the dynamics of who will do the job better, and the value prop gets split between different people with A2A's delegation mechanism.

Related Reads

📰
Your AI Agent Has a Job Description. It Doesn’t Have Rules of Conduct.
Learn how to create a job description for your AI agent and understand the importance of rules of conduct in AI development
Medium · Programming
 📰
Android AI interoperability: 7 data-access checks before your app works with AI assistants
Ensure Android app compatibility with AI assistants by performing 7 data-access checks to maintain user privacy and security
Dev.to AI
 📰
Beyond Automation: Why Connected AIoT Platforms Matter in Pharmaceutical Manufacturing
Learn how connected AIoT platforms can improve pharmaceutical manufacturing by integrating disparate data sources and enabling operational visibility
Dev.to AI
 📰
Your Website’s New Audience: AI Agents
Optimize your website for AI agents to improve performance and user experience, as they increasingly browse and interact with the web on behalf of humans
Dev.to AI
Up next
Run Local Agentic AI on Mac with MLX (Private & Offline)
Ksk Royal
Watch →