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You’d think these powerful AI agents would be able to work as a team by now. But it turns out, there’s a massive missing piece that’s stopping AI agents from scaling like the internet once did. A new report just dropped, and it uncovers a problem…
What are on FIRE 🔥
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AI INSIGHTS
AI agents can plan, reason, use tools, and even work together. But there’s one big problem holding them back: they don’t use a shared protocol to communicate. Without it, we can’t scale intelligent multi-agent systems the way the internet scaled with TCP/IP.
Most agents today are siloed, using custom APIs and wrappers. The result? A fractured ecosystem where agents can’t easily talk to or collaborate with each other — like the early internet before TCP/IP.
1. Protocols Are the New Infrastructure
We need standard protocols, just like TCP/IP did for computers, to enable cross-platform, cross-vendor agent ecosystems.
These protocols are the missing layer between powerful AI agents and true large-scale coordination.
Model Context Protocol (MCP) by Anthropic is currently the most mature and widely adopted protocol. It enables secure and reliable agent-to-tool communication while addressing privacy and architectural issues in tool invocation.
2. A2A & ANP Enable Multi-Agent Collaboration
A2A (Agent2Agent): Proposed by Google, designed for internal enterprise use; agents use “Agent Cards” (JSON) to advertise capabilities and delegate tasks.
ANP (Agent Network Protocol): Open-source and designed for cross-enterprise interoperability, ideal for decentralized collaboration.
The paper introduces 7 evaluation criteria, including: Security + Operability + Extensibility + Trustworthiness + Integration capacity.
3. Protocol Types and Examples
Context-Oriented Protocols (Agent ↔ Tools): MCP: Standard for connecting agents to tools/resources + agents.json: Open format that defines structured tool interfaces (like APIs but AI-readable).
Inter-Agent Protocols (Agent ↔ Agent): ANP: Focused on cross-org communication and discovery + A2A: Used within enterprises for scalable collaboration + Agora: User-centric; lets agents negotiate protocols through natural language using “Protocol Documents” (PDs).
Domain-Specific Protocols: CrowdES: Agent-robot interaction in dynamic spaces + PXP / LOKA: Human-computer collaboration protocols + SPPs: For distributed robotic systems coordination.
Why It Matters: AI agents are on the verge of becoming everyday infrastructure. If we don’t solve this protocol fragmentation soon, we’ll have brilliant AI agents that are stuck in silos, just like early operating systems that couldn’t talk to each other. This is a map of the road ahead.
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TODAY IN AI
AI HIGHLIGHTS
💬 A photo showing a man emotionally interacting with ChatGPT on a NYC subway went viral with AI’s comforting message and the man’s tender response. See it here.
💭 An IIT Kharagpur student tested OpenAI’s ChatGPT o3 model on India’s JEE Advanced 2025 exam. It scored 327/360, high enough to rank #4. But observers are focusing on why it lost 33 marks.
💸 OpenAI plans to make 'AI-native universities'. They definitely want to own the digital layer of universities, just like Google owns email and cloud services in schools?
🔊 A new text-to-speech model is drawing attention online for its hyper-realistic voice replication, even from a small audio snippet. The model has crossed the uncanny valley. See its demo.
🎬 Oscar-winning director Danny Boyle mentored the filmmakers behind the world’s first AI anthology series, set to release next month. Check out the teaser trailer here.
📈 A TikTok creator gained 28.6 million views in just one week by posting AI-generated ‘Yeti ASMR’ videos. Full AI video creative storytelling is going to boom real soon.
💰 AI Daily Fundraising: Meta is reportedly considering a massive $10 billion investment in Scale AI, a company valued at $14 billion, known for serving major clients like Microsoft and OpenAI.
AI SOURCES FROM AI FIRE
NEW EMPOWERED AI TOOLS
🎥 VidAU creates high-converting video ads in seconds with AI. No editting.
📚 Zeno turns YouTube into bite-sized learning, and actionable wisdom.
🤖 ChatBetter offers models from OpenAI, Anthropic, Google, all in one.
📃 Skywork Super Agents turns any idea into engaging content in minutes.
🧩 Thiings is a Free growing collection of 1900+ AI-generated 3D icons.
AI QUICK HITS
🌐 ChatGPT's voice now consistently translates conversations in real time.
🕶️ Meta CTO said 2025 is a ‘pivotal year’ for its augmented & virtual reality.
🧩 An Apple study finds AI reasoning collapses as task complexity increases.
🤖 Here’re 10 must-read nightmare things AI & robots did to humans.
⚠️ Palantir CEO warns AI could trigger "deep societal upheavals" soon.
AI CHART
Researchers at TU Dresden, in collaboration with teams from Germany, the U.K., and the U.S., have built an autonomous AI agent that could support clinical decision-making in oncology.
In early tests, it reached 91% accuracy on real-world simulated patient cases — and correctly cited official treatment guidelines 75% of the time.
🧠 Built on GPT-4, But Not Just a Chatbot
Enhanced with tools for medical imaging analysis (MRI, CT).
Can generate radiology reports and predict genetic mutations from pathology slides.
Uses search tools (PubMed, OncoKB, Google) to stay up-to-date with the latest clinical evidence.
📚 Trained with Real Guidelines, Not Just Web Data
Given access to 6,800+ official oncology documents.
Includes treatment protocols, clinical trial data, and global medical references.
Ensures outputs are grounded in real-world medical standards, not hallucinated guesses.
It was evaluated using 20 complex, real-like cancer scenarios. Then, human experts checked results for accuracy, relevance, and citations. Results: 91% clinical accuracy + 75% correct citations
Yes, it’s early-stage. Yes, only 20 test cases were used. But the implications are real. With proper regulation, privacy safeguards, and human oversight, this could evolve into a standard decision-support tool in cancer care, and beyond.
AI JOBS
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