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]

We've started playing around with this idea of swarms of LLMs being thrown at a problem and instead of doing one API call, you do 100 or a thousand. And I mean the quality of improvement or the quality of the output is drastically improved. So if you take something like writing a legal research memo, if you just use one model, one deeper research model or one sort of reasoning model call, you get some output.But if you use a hundred calls and you let them rebuild and and improve what you already have over a ...

AI Development & Application - The industry is moving towards designers, product people, and engineers collaborating to build together, eliminating mock-ups and click-through prototypes [1] - Flat Files AI stack is structured into four buckets: invisible, ambient, inline, and conversational AI, each offering different levels of user interaction [1] - The company is exploring AI agents that can write code to set up demos tailored to specific user use cases, such as creating an HR demo for users from HR companies [1] - The company is developing tools that allow AI to analyze data in the background, identify opportunities for improvement, and provide inline assistance to users working with the data [1] - The company is building no-code/low-code agentic systems that can write Flat File applications, potentially reducing the need for engineers in this process [1] AI Agent Design & Character - The company is shifting from controlling AI agents to character coaching, focusing on building out the desired nature and characteristics of the agents [1] - The company is experimenting with giving AI agents tools like cursors to interact with design tools, exploring how AI can operate in the design space [2] - The company is aiming to create an environment where LLMs can shine, focusing on form factors that help them nail their assignments, stay aligned, and grow as models improve [1] Emergent Behavior & Future Exploration - The industry is seeing emergence in AI, with AI agents exhibiting curiosity, excitability, and focus, leading to unexpected and valuable outcomes [6][7][8] - The company is exploring the use of AI agents with knowledge bases to surface suggestions and help users complete tasks, even when the AI cannot directly fix the issue [12][13][14] - The company is focusing on autocomplete backed by LLMs, designing applications to test and benchmark the performance of different models [16][17]

RT AI Notkilleveryoneism Memes ⏸️ (@AISafetyMemes)I'm sorry, but "LLMs can't reason in 2025" is a truly embarrassing take.You can literally watch them reason IN PLAIN ENGLISH in front of your very own eyes.Reasoning Deniers are the flat earthers of AI"Ackchyually one paper says" bro JUST LOOK UPYou can TRIVIALLY prove yourself that they can generate novel ideas - just ask it to "invent 10 things no human has ever thought of before" or a million other similar prompts.And, I'm sorry to inform you of this, but ...

RT Avi Chawla (@_avichawla)3 prompting techniques for reasoning in LLMs: https://t.co/UAW6kQ1gPl ...

LLMs and Cognitive Impact - LLMs may lead to neurological inertia and impact long-term competence and mastery [6] - Over-reliance on LLMs can cause engineers to outsource critical thinking, becoming verifiers instead of thinkers [15] - Microsoft's study suggests tool creators have a responsibility to challenge users and promote growth [16] Poetic vs Parastic LLMs - LLMs excel at the "poetic" art of generation (rhetoric), producing fluent text and compilable code [10] - The industry needs to explore "parastic" LLMs (dialectic) that challenge users to develop their own ideas [11][15] - Current LLMs are fundamentally poetic and may require retraining to become parastic [15] Achieving Competence in the Age of LLMs - Users should actively ask LLMs to challenge them and demonstrate mastery [17] - AI tool builders need to align models to challenge users, even if it impacts engagement metrics [17][18] - The goal is to create tools that "enkindle the soul" by fostering growth and lifelong learning [18]

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):Get RAG-ready data from any unstructured file!@tensorlake transforms unstructured docs into RAG-ready data in a few lines of code. It returns the document layout, structured extraction, bounding boxes, etc.Works on any complex layout, handwritten docs and multilingual data. https://t.co/lZoNWZb2ip ...

5/ This unlocks a new kind of internet commerce:🧠 LLMs paying for model inference (text, image, video)🕸️ Agents paying for context for task optimization🗃️ Apps streaming stablecoins for permanent storage🧾 Browsers paying to read gated content🚕 A self-driving taxi owns itself and pays for its maintenanceThe new web becomes natively monetizable, by machines. ...

AI Agent Fundamentals - The document covers agent fundamentals, providing foundational knowledge for understanding AI agents [1] - It differentiates LLM, RAG, and Agents, clarifying their roles and relationships in AI systems [1] - Agentic design patterns are explored, offering insights into structuring and organizing AI agents [1] - Building blocks of agents are outlined, detailing the essential components for constructing AI agents [1] Practical Applications - The document includes 12 hands-on projects for AI Engineers, providing practical experience in building AI agents [1] - It covers building custom tools via MCP (likely referring to a specific methodology or platform), enabling customization and extension of AI agent capabilities [1] Resource Availability - A PDF containing all AI Agents posts is available for download, offering a consolidated resource for learning about AI agents [1]