#Agent2agent Protocol

New short course 📢 A2A: The Agent2Agent Protocol Agents built with different frameworks don’t usually work together without custom glue code. A2A changes that by standardizing how agents discover and communicate with each other. Built in collaboration with @GoogleCloud and @IBM Research, this hands-on course shows how to expose agents as A2A servers, create A2A clients, and orchestrate multi-agent workflows across frameworks like ADK, LangGraph, and BeeAI. Taught by Holt Skinner, Developer Advocate at Google, Ivan Nardini, AI/ML DevRel at Google, and Sandi Besen, Ecosystem Lead at IBM Research. 👉 Enroll at the link in bio or comment "Agent2Agent" to receive the link in your inbox.

AI agents have been promised for the past 2 years, but whenever I’ve tried them, they’ve mostly been very mediocre. The main reasons are: 1) Hallucination 2) Smaller context window 3) Integrations When it comes to integrations, there are two parts, (other two will get better over time). First, using other tools and APIs. Second, taking help from other AI agents. As you know, the first part was solved by MCP a couple of months ago. However, it still remained unsolved to have a similar protocol for the communication of AI agents. With the Google’s A2A protocol, agents will now be able to communicate with each other at a level that was previously impossible.  NOW your Ai agent does not have to be best at trip planning or legal. It can just take help of the best ones in the market. If you want always wanted to build some AI agents, this is the perfect time! [google’s A2A protocol, AI agent, AI startup, Software Product]

What makes a codebase agent-ready? Eno Reyes, Factory AI Co-founder & CTO, explains why AI agents succeed in some orgs and fall apart in others. 📉 It is not adoption. 🧾 It is not token volume. ⭐ It is not having a few power users. It is whether the codebase is clean enough to support autonomy. If it is not agent-ready, it is simple: slop in, slop out. Watch the clip for the most practical advice on AI agents. Link in bio! #AIAgents #AgenticCoding #AutonomousAgents #DevTools #SoftwareEngineering

How to build AI agents: A great cheat sheet (bookmark for later). Here's how to use it: 1️⃣ System Prompt: Define your agent’s role, capabilities, and boundaries. This gives your agent the necessary context. 2️⃣ LLM (Large Language Model): Choose the engine. GPT-5, Claude, Mistral, or an open-source model — pick based on reasoning needs, latency, and cost. 3️⃣ Tools - Equip your agent with tools: API access, code interpreters, database queries, web search, etc. More tools = more utility. Max 20. 4️⃣ Orchestration: Use frameworks (like LangChain, AutoGen, CrewAI) to manage reasoning, task decomposition, and multi-agent collaboration. 5️⃣ Memory: Implement both short-term (context window) and long-term memory (Vector DBs like Pinecone, Weaviate, Chroma). 6️⃣ UI (User Interface): Design an intuitive chat UI or business automation workflow interface that enables smooth interaction with your agent (and automated actions). 7️⃣ AI Evals: Test your agent's performance with real-world tasks. Use tools like TruLens, Rebuff, or custom evals to measure effectiveness, reliability, and safety. #workfromhome #jobsearch #freelancelife #digitalnomad #remotework

A2A or Agent to Agent is how AI systems move from a single chatbot to a team of intelligent agents. In this video, I break down A2A in simple terms using real world examples like investing and customer support. You will see how multiple AI agents collaborate, share context, challenge each other, and arrive at better decisions. If you are building or leading with agentic AI, this concept is foundational.

AI Agents are the future of Generative AI. Instead of just generating text, agents can use tools, execute functions, call APIs, and complete multi-step tasks autonomously. In this short, you’ll learn: • What an AI agent really is • How tool calling works • Why agents are more powerful than simple prompts • A real Python example If you want to build serious Gen AI applications, agents are essential. Follow CodeVisium for practical Gen AI breakdowns — one short at a time. #GenerativeAI #GenAI #AIAgents #LLM #CodeVisium

AI Agents don’t just answer questions — they take actions. With OpenAI function calling, you can build agents that use tools, call APIs, and automate workflows. This is how modern AI systems are built. #AIAgent #OpenAI #GenAI #MachineLearning #Automation

🚀 Just turned my editor into a full AI agent lab. Installed Microsoft’s AI Toolkit inside Visual Studio Code — and now I can: ✅ Pick cloud or local models (like OpenAI) ✅ Test prompts in Playground ✅ Build agents visually ✅ Debug agents step-by-step with Agent Inspector ✅ Run evaluations across multiple models Best part? The toolkit itself is free. You only pay if you use paid cloud APIs — local models run completely offline. This isn’t just AI chat anymore. This is real agent development — inside your editor. If you’re building AI in 2026, this changes everything. #AI #agenticai #vscode #AIDevelopment #Developers #llm #MultiAgent #CodingLife #TechReels #buildinpublic

Here are 5 AI agent frameworks that are worth building with in 2026. whether you need: • Control + stateful flows (loops/branches) • Multi-agent “teams” with roles • Chat-based collaboration workflows • RAG + integrations to ship faster • A personal agent that can actually do stuff for you #aiagents #aidevelopment #llm #langchain #langgraph #crewai #autogen #rag

Agentic AI is changing software development. 🤖⚡ It’s not a chatbot upgrade; it’s AI that can plan, execute, and self-correct. The developer role is shifting from writing code to designing workflows, constraints, and behavior. If you understand systems thinking + feedback loops, AI agents won’t replace you; they’ll extend you. 🧠📈 #AgenticAI #AIAgents #SoftwareDevelopment #SystemDesign #AICareer

Here the two commonly recommended sandbox patterns for AI Agents #artificialintelligence #learnai #coding #chatgpt #claude

What does it actually take to deploy an AI agent inside a real organization — not just demo one? In this walkthrough, Gio shows the full process of building and deploying a production-ready AI agent — from defining purpose and permissions to connecting private knowledge and validating outputs before launch. This isn’t a chatbot tutorial. It’s how engineering and operational teams turn internal documentation, standards, and institutional knowledge into controlled AI systems that deliver measurable value. You’ll see: ✅ How organizations evaluate whether AI is worth deploying ✅ How agents are configured around specific workflows ✅ How private knowledge is structured using RAG (without exposing IP) ✅ How responses stay grounded in verified materials — not internet guesses ✅ How performance is measured through usage, time saved, and cost impact The demo includes a real engineering use case where an agent retrieves standards and code references buried inside large document libraries — reducing research time while maintaining accuracy. Because in regulated environments, speed matters — but correctness matters more. Unlike generic AI tools, these agents operate from controlled information sources defined by your organization. No random internet data. No uncontrolled outputs. No hype. 👉 Try the Free AI Audit Assistant to identify workflows where operational AI can create real impact. Follow for practical, real-world AI deployment insights — built for organizations, not experiments. Real AI. Real operations. No hype. #PrivionAI #IndustrialAI #OperationalAI #EngineeringAI #ManufacturingAI