LLM Frameworks for Multi-Model AI Solutions

What is the LLM Mesh AI architecture and why your enterprise may need it? Key highlights include: • Introducing the LLM Mesh, a new architecture for building modular, scalable agentic applications • Standardizing interactions across diverse AI services like LLMs, retrieval, embeddings, tools, and agents • Abstracting complex dependencies to streamline switching between OpenAI, Gemini, HuggingFace, or self-hosted models • Managing over seven AI-native object types including prompts, agents, tools, retrieval services, and LLMs • Supporting both code-first and visual low-code agent development while preserving enterprise control • Embedding safety with human-in-the-loop oversight, reranking, and model introspection • Enabling performance and cost optimization with model selection, quantization, MoE architectures, and vector search Insightful: Who should take note • AI architects designing multi-agent workflows with LLMs • Product teams building RAG pipelines and internal copilots • MLOps and infrastructure leads managing model diversity and orchestration • CISOs and platform teams standardizing AI usage across departments Strategic: Noteworthy aspects • Elevates LLM usage from monolithic prototypes to composable, governed enterprise agents • Separates logic, inference, and orchestration layers for plug-and-play tooling across functions • Encourages role-based object design where LLMs, prompts, and tools are reusable, interchangeable, and secure by design • Works seamlessly across both open-weight and commercial models, making it adaptable to regulatory and infrastructure constraints Actionable: What to do next Start building your enterprise LLM Mesh to scale agentic applications without hitting your complexity threshold. Define your abstraction layer early and treat LLMs, tools, and prompts as reusable, modular objects. Invest in standardizing the interfaces between them. This unlocks faster iteration, smarter experimentation, and long-term architectural resilience. Consideration: Why this matters As with microservices in the cloud era, the LLM Mesh introduces a new operating model for AI: one that embraces modularity, safety, and scale. Security, governance, and performance aren’t bolted on and they’re embedded from the ground up. The organizations that get this right won’t just deploy AI faster they’ll actually deploy it responsibly, and at scale.

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.

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

Have you observed lately that many agentic AI applications fail because they rely directly on raw LLM calls without a gateway to handle context routing, model orchestration, caching, rate-limiting, and fallback strategies? You must need an LLM gateway or a layer of such kind that acts as a middleware layer that sits between your application and multiple LLM providers. Hence, an LLM gateway is essential for building scalable, safe, and cost-effective agentic AI applications in the enterprise. An LLM gateway essentially functions as a central control panel to orchestrate workloads across models, agents, and MCP servers (the emerging protocol connecting AI agents to external services). Core functions and concepts of an LLM gateway include: ➤ Unified Entry Point: It provides a single, consistent interface (API) for applications to interact with multiple foundational model providers. ➤ Abstraction Layer: It hides the complexity and provider-specific quirks of working directly with individual LLM APIs. This means developers can use the same code structure regardless of which model they call. ➤ Traffic Controller: It intelligently routes requests to the most suitable LLM based on specific criteria like performance, cost, or policy. ➤ Orchestration Platform: It improves the deployment and management of LLMs in production environments by handling security, authentication, and model updates from a single platform. LLM gateways are becoming essential, particularly for enterprises building production-ready and scalable agentic AI applications, because they address multidimensional challenges related to vendor lock-in, complexity, costs, security, and reliability. Know more about LLM gateways through below resources: https://lnkd.in/gimgJ4hD https://lnkd.in/gawvkzGw https://lnkd.in/g-377ESP

𝐅𝐫𝐨𝐦 𝐏𝐫𝐨𝐭𝐨𝐭𝐲𝐩𝐞 𝐭𝐨 𝐏𝐫𝐨𝐝𝐮𝐜𝐭𝐢𝐨𝐧: 𝐀𝐫𝐜𝐡𝐢𝐭𝐞𝐜𝐭𝐢𝐧𝐠 𝐋𝐋𝐌-𝐏𝐨𝐰𝐞𝐫𝐞𝐝 𝐀𝐩𝐩𝐥𝐢𝐜𝐚𝐭𝐢𝐨𝐧𝐬 It’s one thing to build a cool LLM demo. It’s another to make it scalable, safe, and production-grade. Whether you’re building a chatbot, assistant, or workflow engine, the architecture around the model is what determines usability, reliability, and impact. 4 𝐂𝐨𝐦𝐦𝐨𝐧 𝐏𝐚𝐭𝐭𝐞𝐫𝐧𝐬 𝐢𝐧 𝐋𝐋𝐌 𝐀𝐩𝐩𝐥𝐢𝐜𝐚𝐭𝐢𝐨𝐧𝐬: 𝐏𝐫𝐨𝐦𝐩𝐭-𝐁𝐚𝐬𝐞𝐝 𝐀𝐩𝐩𝐬 𝘋𝘪𝘳𝘦𝘤𝘵 𝘱𝘳𝘰𝘮𝘱𝘵 → 𝘳𝘦𝘴𝘱𝘰𝘯𝘴𝘦 ✅ Fast to build ❌ Hard to scale or govern 𝐑𝐀𝐆 (𝐑𝐞𝐭𝐫𝐢𝐞𝐯𝐚𝐥-𝐀𝐮𝐠𝐦𝐞𝐧𝐭𝐞𝐝 𝐆𝐞𝐧𝐞𝐫𝐚𝐭𝐢𝐨𝐧) 𝘍𝘦𝘵𝘤𝘩𝘦𝘴 𝘳𝘦𝘭𝘦𝘷𝘢𝘯𝘵 𝘬𝘯𝘰𝘸𝘭𝘦𝘥𝘨𝘦 𝘪𝘯 𝘳𝘦𝘢𝘭 𝘵𝘪𝘮𝘦 ✅ Boosts factual accuracy ❌ Needs good retrieval, chunking, and indexing logic 𝐀𝐠𝐞𝐧𝐭-𝐁𝐚𝐬𝐞𝐝 𝐖𝐨𝐫𝐤𝐟𝐥𝐨𝐰𝐬 𝘈𝘨𝘦𝘯𝘵𝘴 𝘳𝘦𝘢𝘴𝘰𝘯, 𝘱𝘭𝘢𝘯, 𝘢𝘯𝘥 𝘢𝘤𝘵 ✅ Great for dynamic, tool-using tasks ❌ Requires orchestration and safe execution strategies 𝐋𝐋𝐌 𝐏𝐢𝐩𝐞𝐥𝐢𝐧𝐞𝐬 𝘊𝘩𝘢𝘪𝘯𝘴 𝘮𝘶𝘭𝘵𝘪𝘱𝘭𝘦 𝘓𝘓𝘔 𝘤𝘢𝘭𝘭𝘴 (𝘦.𝘨., 𝘦𝘹𝘵𝘳𝘢𝘤𝘵 → 𝘢𝘯𝘢𝘭𝘺𝘻𝘦 → 𝘴𝘶𝘮𝘮𝘢𝘳𝘪𝘻𝘦) ✅ Modular and testable ❌ Adds latency and system complexity 𝐊𝐞𝐲 𝐀𝐫𝐜𝐡𝐢𝐭𝐞𝐜𝐭𝐮𝐫𝐚𝐥 𝐂𝐨𝐧𝐬𝐢𝐝𝐞𝐫𝐚𝐭𝐢𝐨𝐧𝐬: 𝐇𝐨𝐬𝐭𝐞𝐝 𝐯𝐬. 𝐎𝐩𝐞𝐧-𝐒𝐨𝐮𝐫𝐜𝐞 𝐌𝐨𝐝𝐞𝐥𝐬 (e.g., GPT vs. Mistral) 𝐅𝐫𝐚𝐦𝐞𝐰𝐨𝐫𝐤𝐬: LangChain, LlamaIndex, Semantic Kernel 𝐌𝐞𝐦𝐨𝐫𝐲 & 𝐒𝐭𝐚𝐭𝐞: Chat history, user profile, external context 𝐎𝐛𝐬𝐞𝐫𝐯𝐚𝐛𝐢𝐥𝐢𝐭𝐲: Logging, feedback loops, versioning 𝐒𝐞𝐜𝐮𝐫𝐢𝐭𝐲 & 𝐒𝐚𝐟𝐞𝐭𝐲: Guardrails, validation, fallback paths 𝐋𝐨𝐨𝐤𝐢𝐧𝐠 𝐀𝐡𝐞𝐚𝐝 New standards like 𝐀𝐧𝐭𝐡𝐫𝐨𝐩𝐢𝐜’𝐬 𝐌𝐨𝐝𝐞𝐥 𝐂𝐨𝐧𝐭𝐞𝐱𝐭 𝐏𝐫𝐨𝐭𝐨𝐜𝐨𝐥 (𝐌𝐂𝐏) and 𝐆𝐨𝐨𝐠𝐥𝐞'𝐬 𝐀𝐠𝐞𝐧𝐭-𝐭𝐨-𝐀𝐠𝐞𝐧𝐭 (𝐀2𝐀) 𝐩𝐫𝐨𝐭𝐨𝐜𝐨𝐥 are early steps toward more interoperable, modular AI ecosystems. If adopted widely, they could enable agents and models to share context and collaborate more effectively — powering next-gen enterprise workflows. 𝐔𝐩 𝐧𝐞𝐱𝐭: How to design guardrails and safety layers to ensure your LLM applications are reliable, responsible, and ready for production. Which of these patterns are you exploring in your stack? #engineeringtidbits #LLMs #RAG #AIArchitecture #Agents #MCP #A2A #LangChain #EnterpriseAI #NLP

8 𝐓𝐲𝐩𝐞𝐬 𝐨𝐟 𝐋𝐋𝐌𝐬 𝐏𝐨𝐰𝐞𝐫𝐢𝐧𝐠 𝐭𝐡𝐞 𝐍𝐞𝐱𝐭 𝐆𝐞𝐧𝐞𝐫𝐚𝐭𝐢𝐨𝐧 𝐨𝐟 𝐀𝐈 𝐀𝐠𝐞𝐧𝐭𝐬 Most discussions around AI still talk about “LLMs” as if they’re one thing. But in reality, not all LLMs are built the same — and understanding their architectures can help you design smarter, more capable AI systems. This visual breaks down 8 distinct types of LLMs that are now being used in AI agents — each engineered for a very specific cognitive function. 1. 𝐆𝐏𝐓 (𝐆𝐞𝐧𝐞𝐫𝐚𝐥 𝐏𝐫𝐞𝐭𝐫𝐚𝐢𝐧𝐞𝐝 𝐓𝐫𝐚𝐧𝐬𝐟𝐨𝐫𝐦𝐞𝐫) The foundation most people know — trained on vast corpora to generate contextual text. Ideal for general-purpose reasoning, summarization, and generation. 𝟐. 𝐌𝐨𝐄 (𝐌𝐢𝐱𝐭𝐮𝐫𝐞 𝐨𝐟 𝐄𝐱𝐩𝐞𝐫𝐭𝐬) A modular architecture where multiple “experts” handle different parts of a problem. The model dynamically selects which experts to use — improving efficiency and specialization. 𝟑. 𝐋𝐑𝐌 (𝐋𝐚𝐫𝐠𝐞 𝐑𝐞𝐚𝐬𝐨𝐧𝐢𝐧𝐠 𝐌𝐨𝐝𝐞𝐥) Designed for structured reasoning and problem decomposition. These models excel in multi-step logic, chain-of-thought reasoning, and verification loops. 𝟒. 𝐕𝐋𝐌 (𝐕𝐢𝐬𝐢𝐨𝐧-𝐋𝐚𝐧𝐠𝐮𝐚𝐠𝐞 𝐌𝐨𝐝𝐞𝐥) Combines image and text understanding. It’s what enables systems like GPT-4V and Gemini to interpret images, describe scenes, and reason across modalities. 𝟓. 𝐒𝐋𝐌 (𝐒𝐦𝐚𝐥𝐥 𝐋𝐚𝐧𝐠𝐮𝐚𝐠𝐞 𝐌𝐨𝐝𝐞𝐥) Lightweight yet capable models — efficient enough for edge or private deployment. They handle narrow, domain-specific tasks with low latency and higher privacy. 𝟔. 𝐋𝐀𝐌 (𝐋𝐚𝐫𝐠𝐞 𝐀𝐜𝐭𝐢𝐨𝐧 𝐌𝐨𝐝𝐞𝐥) Built for tool use and task execution. These models can plan, sequence, and perform multi-step actions autonomously, forming the backbone of agentic workflows. 𝟕. 𝐇𝐑𝐌 (𝐇𝐢𝐞𝐫𝐚𝐫𝐜𝐡𝐢𝐜𝐚𝐥 𝐑𝐞𝐚𝐬𝐨𝐧𝐢𝐧𝐠 𝐌𝐨𝐝𝐞𝐥) Adds a “planner-executor” hierarchy. These models break problems into sub-goals, reason at multiple levels, and combine high-level logic with fast low-level computation. 𝟖. 𝐋𝐂𝐌 (𝐋𝐚𝐫𝐠𝐞 𝐂𝐨𝐧𝐜𝐞𝐩𝐭 𝐌𝐨𝐝𝐞𝐥) Trained for conceptual understanding and abstraction. They map relationships between ideas, entities, and patterns — crucial for knowledge discovery and creativity. As LLM architectures evolve, we’re moving from monolithic intelligence to compositional cognition — models that reason, perceive, plan, and act in coordination. The future of AI won’t belong to a single type of LLM, but to ecosystems of specialized models that think together — much like teams of human experts collaborating on complex goals. Understanding these distinctions is key for anyone building or integrating AI agents today. #ArtificialIntelligence #LLM #AIAgents #MachineLearning #DeepLearning #AIEngineering #GenerativeAI #Innovation