Implementation Of Frameworks

In light of the planned omnibus (#CSRD, #CSDDD, #EUTaxonomy) proposal, over 90 organizations published a multi-stakeholder statement yesterday. Key message: "Instead of playing ping-pong with the legal framework, we strongly encourage focusing on smart and easy implementation and consider the current lack of key data relevant for the economic transformation." Some highlights: 1️⃣ On Legal Uncertainty: "Any arbitrary change or cut in the standards would risk confusing the market, and demand more efforts from companies which are already investing in the application of the EU standards." 2️⃣ On the 25% Reporting Reduction Goal: "The 25% reduction target for reporting obligations [...] lacks precise modelling and fails to demonstrate how it aligns with the actual reporting requirements necessary to achieve policy objectives: it is arbitrary." 3️⃣ On Needed Evidence for Policy Interventions: "Following the EC Better Regulation principles, any policy intervention must be informed from evidence." My remark here: Such evidence is difficult to produce right now, as we do not have reliable data (only first implementation experiences). The CSRD is not even fully transposed by all countries and CSDDD transposition is in very early stages. 4️⃣ On Adopting a Long-Term View: "It must be recognised that these challenges [implementation costs] will also decrease after two or three reporting cycles. Similarly, the recurring costs are expected to be significantly lower after the first-time investment." The way forward? (1) The Statement points to the need for better practical guidance and implementation support. (2) It also points out to provide capacity building for States so that they can better assist companies, especially SMEs. (3) It also urges to ensure consistency across EU regulations (e.g., on definitions and methods). #sustainability, #esg, #eugreendeal

🌍 UNESCO’s Pillars Framework for Digital Transformation in Education offers a roadmap for leaders, educators, and tech partners to work together and bridge the digital divide. This framework is about more than just tech—it’s about supporting communities and keeping education a public good. 💡 When implementing EdTech, policymakers should pay special attention to these critical aspects to ensure that technology meaningfully enhances education without introducing unintended issues:  🚸1. Equity and Access Policymakers need to prioritize closing the digital divide by providing affordable internet, reliable devices, and offline options where connectivity is limited. Without equitable access, EdTech can worsen existing educational inequalities.  💻2. Data Privacy and Security Implementing strong data privacy laws and secure platforms is essential to build trust. Policymakers must ensure compliance with data protection standards and implement safeguards against data breaches, especially in systems that involve sensitive information.  🚌3. Pedagogical Alignment and Quality of Content Digital tools and content should be high-quality, curriculum-aligned, and support real learning needs. Policymakers should involve educators in selecting and shaping EdTech tools that align with proven pedagogical practices.  🌍4. Sustainable Funding and Cost Management To avoid financial strain, policymakers should develop sustainable, long-term funding models and evaluate the total cost of ownership, including infrastructure, updates, and training. Balancing costs with impact is key to sustaining EdTech programs.  🦺5. Capacity Building and Professional Development Training is essential for teachers to integrate EdTech into their teaching practices confidently. Policymakers need to provide robust, ongoing professional development and peer-support systems, so educators feel empowered rather than overwhelmed by new tools. 👓 6. Monitoring, Evaluation, and Continuous Improvement Policymakers should establish monitoring and evaluation processes to track progress and understand what works. This includes using data to refine strategies, ensure goals are met, and avoid wasted resources on ineffective solutions. 🧑🚒 7. Cultural and Social Adaptation Cultural sensitivity is crucial, especially in communities less familiar with digital learning. Policymakers should promote a growth mindset and address resistance through community engagement and awareness campaigns that highlight the educational value of EdTech. 🥸 8. Environmental Sustainability Policymakers should integrate green practices, like using energy-efficient devices and recycling programs, to reduce EdTech’s carbon footprint. Sustainable practices can also help keep costs manageable over time. 🔥Download: UNESCO. (2024). Six pillars for the digital transformation of education. UNESCO. https://lnkd.in/eYgr922n  #DigitalTransformation #EducationInnovation #GlobalEducation

Most new privacy professionals with fresh CIPP certifications are unprepared for this conversation "We want to track what customers look at on our website and send them targeted emails about those products. That’s fine since they’re already our customers, right?" You know the legal framework. You understand GDPR. You passed your certification. But now you're facing a room of marketing stakeholders who need answers that help them do their jobs. Knowledge tells you: This involves processing personal data for marketing - need to check lawful basis, likely legitimate interests with balance test, plus consider ePrivacy rules for tracking. Judgment asks: Does this specific use case make sense? → What exactly are they tracking? Page views or detailed behavior? → What does “personalization” mean here, recommendations or aggressive targeting? → What did customers expect when signing up? → Can they easily opt out? → Is this helpful to the customer or just to marketing? The legal answer is the same. The practical approach varies completely. This gap isn’t discussed enough in privacy education. We learn the "what" and "why" in certification programs, but day-to-day privacy work is all about the "when" and "how." → When to push back vs. find creative workarounds → How to get buy-in without a budget or authority → When "perfect" compliance isn’t realistic—and what to do instead → How to speak business language while holding privacy lines Many privacy professionals struggle here because we're: → Waiting for perfect info before acting → Speaking only in compliance terms → Afraid to make the wrong call and get blamed But here’s the reality: Judgment comes from experience and imperfect action beats perfect paralysis. The most effective privacy professionals aren’t those who memorize every regulation. They’re the ones who navigate gray areas and keep the business moving. Real examples of knowledge vs. judgment: → The Marketing Automation Dilemma Knowledge: Needs lawful basis, tracking consent, LI balancing test Judgment: Start with product category suggestions, include opt-out, test customer response before expanding → The Vendor Assessment Crisis Knowledge: DPA + security questionnaire needed Judgment: Vendor handles minimal data, go live now with essentials, full review in parallel → The Data Retention Debate Knowledge: Delete data when no longer needed Judgment: Tier retention by sensitivity/business value with review points, not a one-size policy Certifications teach you to spot problems. Experience teaches you to solve them. What’s the biggest gap you’ve faced between privacy theory and real-world practice? P.S. If you’re feeling this tension, you’re right on track. This isn’t a flaw in your education. It’s the start of real expertise. The most effective privacy professionals I know all went through this same shift.

𝗟𝗟𝗠 -> 𝗥𝗔𝗚 -> 𝗔𝗜 𝗔𝗴𝗲𝗻𝘁 -> 𝗔𝗴𝗲𝗻𝘁𝗶𝗰 𝗔𝗜 The visual guide explains how these four layers relate—not as competing technologies, but as an evolving intelligence architecture. Here’s a deeper look: 1. 𝗟𝗟𝗠 (𝗟𝗮𝗿𝗴𝗲 𝗟𝗮𝗻𝗴𝘂𝗮𝗴𝗲 𝗠𝗼𝗱𝗲𝗹) This is the foundation. Models like GPT, Claude, and Gemini are trained on vast corpora of text to perform a wide array of tasks: – Text generation – Instruction following – Chain-of-thought reasoning – Few-shot/zero-shot learning – Embedding and token generation However, LLMs are inherently limited to the knowledge encoded during training and struggle with grounding, real-time updates, or long-term memory. 2. 𝗥𝗔𝗚 (𝗥𝗲𝘁𝗿𝗶𝗲𝘃𝗮𝗹-𝗔𝘂𝗴𝗺𝗲𝗻𝘁𝗲𝗱 𝗚𝗲𝗻𝗲𝗿𝗮𝘁𝗶𝗼𝗻) RAG bridges the gap between static model knowledge and dynamic external information. By integrating techniques such as: – Vector search – Embedding-based similarity scoring – Document chunking – Hybrid retrieval (dense + sparse) – Source attribution – Context injection …RAG enhances the quality and factuality of responses. It enables models to “recall” information they were never trained on, and grounds answers in external sources—critical for enterprise-grade applications. 3. 𝗔𝗜 𝗔𝗴𝗲𝗻𝘁 RAG is still a passive architecture—it retrieves and generates. AI Agents go a step further: they act. Agents perform tasks, execute code, call APIs, manage state, and iterate via feedback loops. They introduce key capabilities such as: – Planning and task decomposition – Execution pipelines – Long- and short-term memory integration – File access and API interaction – Use of frameworks like ReAct, LangChain Agents, AutoGen, and CrewAI This is where LLMs become active participants in workflows rather than just passive responders. 4. 𝗔𝗴𝗲𝗻𝘁𝗶𝗰 𝗔𝗜 This is the most advanced layer—where we go beyond a single autonomous agent to multi-agent systems with role-specific behavior, memory sharing, and inter-agent communication. Core concepts include: – Multi-agent collaboration and task delegation – Modular role assignment and hierarchy – Goal-directed planning and lifecycle management – Protocols like MCP (Anthropic’s Model Context Protocol) and A2A (Google’s Agent-to-Agent) – Long-term memory synchronization and feedback-based evolution Agentic AI is what enables truly autonomous, adaptive, and collaborative intelligence across distributed systems. Whether you’re building enterprise copilots, AI-powered ETL systems, or autonomous task orchestration tools, knowing what each layer offers—and where it falls short—will determine whether your AI system scales or breaks. If you found this helpful, share it with your team or network. If there’s something important you think I missed, feel free to comment or message me—I’d be happy to include it in the next iteration.

Switch between OpenAI, Anthropic, and Google with a single line of code. any-llm gives you a single, clean interface to work with OpenAI, Anthropic, Google, and every other major LLM provider. Key Features: • Unified interface: one function for all providers, switch models with just a string change • Developer friendly: full type hints and clear error messages • Framework-agnostic: works across different projects and use cases • Uses official provider SDKs when available for maximum compatibility • No proxy or gateway server required The problem it solves: The LLM provider landscape is fragmented. OpenAI became the standard, but every provider has slight variations in their APIs. LiteLLM reimplements everything instead of using official SDKs. AISuite lacks maintenance. Most solutions force you through a proxy server. any-llm takes a different approach - leverage official SDKs where possible, provide a clean abstraction layer, and keep it simple. The best part? It's 100% Open Source. Link to the repo in the comments!

If you’re building anything with LLMs, your system architecture matters more than your prompts. Most people stop at “call the model, get the output.” But LLM-native systems need workflows, blueprints that define how multiple LLM calls interact, how routing, evaluation, memory, tools, or chaining come into play. Here’s a breakdown of 6 core LLM workflows I see in production: 🧠 LLM Augmentation Classic RAG + tools setup. The model augments its own capabilities using: → Retrieval (e.g., from vector DBs) → Tool use (e.g., calculators, APIs) → Memory (short-term or long-term context) 🔗 Prompt Chaining Workflow Sequential reasoning across steps. Each output is validated (pass/fail) → passed to the next model. Great for multi-stage tasks like reasoning, summarizing, translating, and evaluating. 🛣 LLM Routing Workflow Input routed to different models (or prompts) based on the type of task. Example: classification → Q&A → summarization all handled by different call paths. 📊 LLM Parallelization Workflow (Aggregator) Run multiple models/tasks in parallel → aggregate the outputs. Useful for ensembling or sourcing multiple perspectives. 🎼 LLM Parallelization Workflow (Synthesizer) A more orchestrated version with a control layer. Think: multi-agent systems with a conductor + synthesizer to harmonize responses. 🧪 Evaluator–Optimizer Workflow The most underrated architecture. One LLM generates. Another evaluates (pass/fail + feedback). This loop continues until quality thresholds are met. If you’re an AI engineer, don’t just build for single-shot inference. Design workflows that scale, self-correct, and adapt. 📌 Save this visual for your next project architecture review. 〰️〰️〰️ Follow me (Aishwarya Srinivasan) for more AI insight and subscribe to my Substack to find more in-depth blogs and weekly updates in AI: https://lnkd.in/dpBNr6Jg

Building LLM Agent Architectures on AWS - The Future of Scalable AI Workflows What if you could design AI agents that not only think but also collaborate, route tasks, and refine results automatically? That’s exactly what AWS’s LLM Agent Architecture enables. By combining Amazon Bedrock, AWS Lambda, and external APIs, developers can build intelligent, distributed agent systems that mirror human-like reasoning and decision-making. These are not just chatbots - they’re autonomous, orchestrated systems that handle workflows across industries, from customer service to logistics. Here’s a breakdown of the core patterns powering modern LLM agents : Breakdown: Key Patterns for AI Workflows on AWS 1. Prompt Chaining / Saga Pattern Each step’s output becomes the next input — enabling multi-step reasoning and transactional workflows like order handling, payments, and shipping. Think of it as a conversational assembly line. 2. Routing / Dynamic Dispatch Pattern Uses an intent router to direct queries to the right tool, model, or API. Just like a call center routing customers to the right department — but automated. 3. Parallelization / Scatter-Gather Pattern Agents perform tasks in parallel Lambda functions, then aggregate responses for efficiency and faster decisions. Multiple agents think together — one answer, many minds. 4. Saga / Orchestration Pattern Central orchestrator agents manage multiple collaborators, synchronizing tasks across APIs, data sources, and LLMs. Perfect for managing complex, multi-agent projects like report generation or dynamic workflows. 5. Evaluator / Reflect-Refine Loop Pattern Introduces a feedback mechanism where one agent evaluates another’s output for accuracy and consistency. Essential for building trustworthy, self-improving AI systems. AWS enables modular, event-driven, and autonomous AI architectures, where each pattern represents a step toward self-reliant, production-grade intelligence. From prompt chaining to reflective feedback loops, these blueprints are reshaping how enterprises deploy scalable LLM agents. #AIAgents

#NCERT is introducing new syllabi and textbooks for Grade 6 in all subjects as recommended in NCF-SE, 2023. The transition of #students to new syllabi and #textbooks requires #teachers to introduce all Grade 6 students to new pedagogical approaches as outlined in NCF-SE, 2023 before they begin formal study of the new textbooks. It is in this context, a month-long bridge programme has been proposed to provide students with an experience of activity-based, fun-filled learning, free from curriculum load and the burden of non-comprehension. National Council of Educational Research and Training ( NCERT ) has developed the brief guidelines for teachers to support the conduct of this bridge-month programme, including detailed activities in subject-specific areas. This phase is very crucial for transitioning to new curriculum based on the philosophy of NEP 2020. The bridge-month programme is vital for successfully ushering our teachers and students into the new phase of education advocated by NEP 2020 and embodied in NCF-SE 2023. Therefore, it is essential to introduce the bridge month programme before providing the new textbooks to teachers and students. Subject-Wise Bridge Programme: Sanskrit: https://lnkd.in/dpTY4bt3 Art Education: https://lnkd.in/d3VgPWcz English: https://lnkd.in/d3U-JSAG Science: https://lnkd.in/dDdJU2Kc Urdu: https://lnkd.in/d4juPEqT Hindi: https://lnkd.in/dSfkV638 Mathematics: https://lnkd.in/dZJS7YQ5 Physical Education: https://lnkd.in/dBn9Tayb Vocational Education: https://lnkd.in/dXFNdptF Social Science: https://lnkd.in/d4gg6im9

Exciting New Research: LLM-Based Agents for Question Answering! I just came across a fascinating survey paper on Large Language Model (LLM)-based agents for question answering systems. This comprehensive review from George Mason University explores how LLM agents are revolutionizing QA systems by addressing limitations in traditional approaches. >> What makes LLM agents special? Traditional QA systems relied on fixed pipelines with separate modules for query understanding, information retrieval, and answer generation. LLM-based agents transform this approach by using LLMs as their core reasoning engine, enabling dynamic interaction with external environments. The paper breaks down the LLM agent architecture into three key components: - Memory (M): Stores all information including the question and retrieved data - Planning module (πp): Determines the next action by consulting the LLM with planning prompts - Inner-thinking module (πt): Executes internal reasoning processes >> Technical Implementation Details The agent's planning process is mathematically represented as At = πp(St), where πp is the planning policy function and At is the action at time t. The agent's observation is obtained through Ot = E(At), where E is the environment feedback function. The LLM agent QA process follows a sophisticated algorithm where memory is initialized with the question, then for each time step: 1. The planning module selects an action based on memory 2. If the action interacts with external environments, the agent obtains observations and updates memory 3. If it's an internal thinking action, the agent processes the action internally >> Key Areas of Innovation The survey organizes LLM agent QA systems into several critical components: Planning: Both prompting-based approaches (like ReAct, Think on Graph) and tuning-based methods (FireAct, Learning from Failure) that enable agents to formulate action sequences. Question Understanding: Techniques for identifying slots, query expansion (HyQE, Query2CoT), and query reformulation to enhance comprehension. Information Retrieval: Methods for retrieving, ranking, and compressing relevant information from external sources. Answer Generation: Tool-augmented generation (Program-of-Thought) and prompt-enhanced techniques (Chain-of-Thought) that improve response quality. This research highlights how LLM agents are addressing hallucination issues and knowledge limitations by enabling interaction with external tools, databases, and other models.

While auditing an EU FinTech scale-up, I came across some surprising design choices: • Flat subscription sprawl • No Azure Policy enforcement • No Hub-and-Spoke network model • No Management Group hierarchy Clearly, they had grown fast but without structure. So I led a Landing Zone redesign based on Microsoft’s Cloud Adoption Framework and deployed: 👉🏻A Core Infrastructure Management Group with Policy-as-Code 👉🏻Spoke separation by app and environment 👉🏻Role-based access controls aligned with team structure So The result is 94% policy compliance in just 6 weeks & Clear cost ownership per team & A secure, scalable foundation ready for future growth Without Landing Zones, your Azure setup is just an expensive sandbox. #AzureCAF #EnterpriseLandingZone #ArchitectureReview #InfraGovernance #AzureBestPractices #CloudStrategy