This Startup Just Raised $13 Million To Make AI Agents Easy For Any Teamhttps://t.co/F5rTzO5oGB https://t.co/d7s5T4whLs ...

Deep sea, actually, despite the sort of volatility of April, which deep sea cause in markets, it moves slower than some of the other Chinese names working on models. But the whole point is that this is a genetic. They want something that goes beyond the chapel.Yeah, that's right. And I think we've all been a little bit surprised at how little we've heard from Deep Sea in the last eight months since it upended the markets. We've seen a glut of products from Chinese and US rivals.And there's a lot of speculat ...

🎙️ From Identity to AI Agents: Exploring Matchain’s Road to Mass Adoption📅 Sep 3, 2025 | 🕒 12:00 PM UTC🎙️ Host:@LBank_Exchange🤝 Co-Host:@xmuha0🎤 Guests:@matchain_io & @petrixbarbosa📰 Media Observers:@CoinGapeMedia@cryptodotnews👉 Set a reminder:https://t.co/kunOisolgs#LBankSpaces #Matchain #AI #Crypto ...

AI & Crypto Market Overview - The industry believes in the future of AI and AI agent tokens [1] - The industry highlights VIRTUAL and Wayfinder as examples in the AI space [1] Investment Opportunity - The industry suggests readers explore the AI sector early [1]

AI Agent Evolution - AI agents have evolved from simple LLMs to sophisticated systems with reasoning, memory, and tool use [1] - Early transformer-based chatbots processed small chunks of input, exemplified by ChatGPT's initial 4k token context window [1] - LLMs expanded to handle thousands of tokens, enabling parsing of larger documents and longer conversations [1] - Retrieval-Augmented Generation (RAG) provided access to fresh and external data, enhancing LLM outputs with tools like search APIs and calculators [1] - Multimodal LLMs process text, images, and audio, incorporating memory for persistence across interactions [1] Key Components of Advanced AI Agents - Current AI agents are equipped with short-term, long-term, and episodic memory [1] - Tool calling capabilities, including search, APIs, and actions, are integral to advanced AI agents [1] - Reasoning and ReAct-based decision-making are crucial components of modern AI agents [1]

If you found it insightful, reshare it with your network.Find me → @_avichawlaEvery day, I share tutorials and insights on DS, ML, LLMs, and RAGs.Avi Chawla (@_avichawla):5 levels of evolution of AI Agents.Over the last few years, we’ve gone from simple LLMs → to fully-fledged Agentic systems with reasoning, memory, and tool use.Here’s a step-by-step breakdown.1) Small context window LLMs- Input: Text → LLM → Output: Text- Early https://t.co/DvNTsnXpYT ...

AI Agent Evolution - The industry has progressed from simple LLMs to sophisticated Agentic systems with reasoning, memory, and tool use [1] - Early transformer-based chatbots were limited by small context windows, exemplified by ChatGPT's initial 4k token limit [1] - The industry has seen upgrades to handle thousands of tokens, enabling parsing of larger documents and longer conversations [1] - Retrieval-Augmented Generation (RAG) provided access to fresh and external data, enhancing LLM outputs [1] - Multimodal LLMs can process multiple data types (text, images, audio), with memory introducing persistence across interactions [1] Key Components of Advanced AI Agents - Advanced AI Agents are equipped with short-term, long-term, and episodic memory [1] - Tool calling (search, APIs, actions) is a crucial feature of modern AI Agents [1] - Reasoning and ReAct-based decision-making are integral to the current AI Agent era [1]

Core Concepts - LLMs like GPT and DeepSeek serve as the foundational engine powering Agentic AI [1] - AI Agents wrap around LLMs, granting them autonomous action capabilities and making them useful in real-world workflows [2] - Agentic systems emerge from combining multiple agents, enabling collaboration and coordination [3] Agentic Infrastructure - Agentic Infrastructure encompasses tokenization & inference parameters, prompt engineering, and LLM APIs [2] - Tool usage & function calling, agent reasoning (e g, ReAct), task planning & decomposition, and memory management are crucial components [3] - Inter-Agent communication, routing & scheduling, state coordination, and Multi-Agent RAG facilitate collaboration [4] - Agent roles & specialization and orchestration frameworks (e g, CrewAI) enhance workflow construction [4] Trust, Safety, and Scalability - Observability & logging (e g, using DeepEval), error handling & retries, and security & access control are essential for trust and safety [6] - Rate limiting & cost management, workflow automation, and human-in-the-loop controls ensure scalability and governance [6] - Agentic AI features a stacked architecture, with outer layers adding reliability, coordination, and governance [5]

Core Concepts - LLMs like GPT and DeepSeek power Agentic AI [1] - AI Agents wrap around LLMs, enabling autonomous action [2] - Agentic systems combine multiple agents for collaboration [2] Agentic Infrastructure - Observability & logging track performance using frameworks like DeepEval [2] - Tokenization & inference parameters define text processing [3] - Prompt engineering improves output quality [3] - Tool usage & function calling connect LLMs to external APIs [4] - Agent reasoning methods include ReAct and Chain-of-Thought [4] - Task planning & decomposition break down large tasks [4] - Memory management tracks history and context [4] Multi-Agent Systems - Inter-Agent communication uses protocols like ACP, A2A [5] - Routing & scheduling determines agent task allocation [5] - State coordination ensures consistency in collaboration [5] - Multi-Agent RAG uses retrieval-augmented generation [5] - Orchestration frameworks like CrewAI build workflows [5] Enterprise Considerations - Error handling & retries provide resilience [7] - Security & access control prevent overreach [7] - Rate limiting & cost management control resource usage [7] - Human-in-the-loop controls allow oversight [7]

Technology & Infrastructure - Rollup tokens are being acquired for "zk exposure" (零知识证明)[1] - Cysic is shipping hardware capable of 1.31 million proofs per second, contradicting claims of impossibility [1] - Infrastructure layer is generating significant revenue [1] Challenges & Opportunities - High proof costs are impacting margins [1] - The industry is moving beyond application layer focus [1] - There's a shift from chasing AI agents to focusing on hardware solutions [1]