R.A.I.D. (Realtime AI for Distribution) outperforms industry benchmarks for accuracy on 3.7 million live FMCG transactions, redefining decision making in volatile global marketsLondon, UK, March 23, 2026 (GLOBE NEWSWIRE) -- RedCloud Holdings plc (NASDAQ: RCT) ("RedCloud" or the "Company"), the technology company building AI infrastructure for global trade today declared that the era of traditional ERP systems faces a steep decline in relevance, alongside validating R.A.I.D. (Realtime AI for Distribution), i ...

A layered overview of key Agentic AI concepts.Let’s understand it layer by layer.1) LLMs (foundation layer)At the core, you have LLMs like GPT, DeepSeek, etc.Core ideas here:- Tokenization & inference parameters: how text is broken into tokens and processed by the model.- Prompt engineering: designing inputs to get better outputs.- LLM APIs: programmatic interfaces to interact with the model.This is the engine that powers everything else.2) AI Agents (built on LLMs)Agents wrap around LLMs to give them the a ...

Core Insights - The article does not provide specific insights or analysis regarding any companies or industries, focusing instead on disclaimers and disclosures [1][2] Group 1 - There is no stock, option, or similar derivative position in any of the companies mentioned [1] - The article expresses personal opinions and is not receiving compensation beyond Seeking Alpha [1] - The authors are not licensed or certified by any institute or regulatory body [2]

Core Insights - The article does not provide specific insights or analysis regarding any companies or industries, focusing instead on disclaimers and disclosures [1][2] Group 1 - There is no stock, option, or similar derivative position in any of the companies mentioned [1] - The article expresses personal opinions and does not involve compensation beyond Seeking Alpha [1] - No business relationship exists with any company whose stock is mentioned [1] Group 2 - Past performance is not indicative of future results [2] - No investment recommendations or advice are provided for suitability to particular investors [2] - The views expressed may not reflect those of Seeking Alpha as a whole [2]

Core Viewpoint - The stock of Amazon (AMZN) is labeled as a strong buy due to a long-term transition from generative AI hosting to gaining an advantage in Agentic Infrastructure, particularly through the integration of Amazon's proprietary silicon, Trainium3 [1] Group 1 - Amazon is expected to benefit from a strategic shift in technology focus, enhancing its competitive edge in the market [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]