LLM Development Principles

As an MLOps platform, we started by helping organizations implement responsible AI governance for traditional machine learning models. With principles of transparency, accountability, and oversight, our Guardrails enabled smooth model development. However, governing large language models (LLMs) like ChatGPT requires a fundamentally different approach. LLMs aren't narrow systems designed for specific tasks - they can generate nuanced text on virtually any topic imaginable. This presents a whole new set of challenges for governance. Here are some key components for evolving AI governance frameworks to effectively oversee large language models (LLMs): 1️⃣ Usage-Focused Governance: Focus governance efforts on real-world LLM usage - the workflows, inputs and outputs - rather than just the technical architecture. Continuously assess risks posed by different use cases. 2️⃣ Dynamic Risk Assessment: Identify unique risks presented by LLMs such as bias amplification and develop flexible frameworks to proactively address emerging issues. 3️⃣ Customized Integrations: Invest in tailored solutions to integrate complex LLMs with existing systems in alignment with governance goals. 4️⃣ Advanced Monitoring: Utilize state-of-the-art tools to monitor LLMs in real-time across metrics like outputs, bias indicators, misuse prevention, and more. 5️⃣ Continuous Accuracy Tracking: Implement ongoing processes to detect subtle accuracy drifts or inconsistencies in LLM outputs before they escalate. 6️⃣ Agile Oversight: Adopt agile, iterative governance processes to manage frequent LLM updates and retraining in line with the rapid evolution of models. 7️⃣ Enhanced Transparency: Incorporate methodologies to audit LLMs, trace outputs back to training data/prompts and pinpoint root causes of issues to enhance accountability. In conclusion, while the rise of LLMs has disrupted traditional governance models, we at Katonic AI are working hard to understand the nuances of LLM-centric governance and aim to provide effective solutions to assist organizations in harnessing the power of LLMs responsibly and efficiently. #LLMGovernance #ResponsibleLLMs #LLMrisks #LLMethics #LLMpolicy #LLMregulation #LLMbias #LLMtransparency #LLMaccountability

Training a Large Language Model (LLM) involves more than just scaling up data and compute. It requires a disciplined approach across multiple layers of the ML lifecycle to ensure performance, efficiency, safety, and adaptability. This visual framework outlines eight critical pillars necessary for successful LLM training, each with a defined workflow to guide implementation: 𝟭. 𝗛𝗶𝗴𝗵-𝗤𝘂𝗮𝗹𝗶𝘁𝘆 𝗗𝗮𝘁𝗮 𝗖𝘂𝗿𝗮𝘁𝗶𝗼𝗻: Use diverse, clean, and domain-relevant datasets. Deduplicate, normalize, filter low-quality samples, and tokenize effectively before formatting for training. 𝟮. 𝗦𝗰𝗮𝗹𝗮𝗯𝗹𝗲 𝗗𝗮𝘁𝗮 𝗣𝗿𝗲𝗽𝗿𝗼𝗰𝗲𝘀𝘀𝗶𝗻𝗴: Design efficient preprocessing pipelines—tokenization consistency, padding, caching, and batch streaming to GPU must be optimized for scale. 𝟯. 𝗠𝗼𝗱𝗲𝗹 𝗔𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲 𝗗𝗲𝘀𝗶𝗴𝗻: Select architectures based on task requirements. Configure embeddings, attention heads, and regularization, and then conduct mock tests to validate the architectural choices. 𝟰. 𝗧𝗿𝗮𝗶𝗻𝗶𝗻𝗴 𝗦𝘁𝗮𝗯𝗶𝗹𝗶𝘁𝘆 and 𝗢𝗽𝘁𝗶𝗺𝗶𝘇𝗮𝘁𝗶𝗼𝗻: Ensure convergence using techniques such as FP16 precision, gradient clipping, batch size tuning, and adaptive learning rate scheduling. Loss monitoring and checkpointing are crucial for long-running processes. 𝟱. 𝗖𝗼𝗺𝗽𝘂𝘁𝗲 & 𝗠𝗲𝗺𝗼𝗿𝘆 𝗢𝗽𝘁𝗶𝗺𝗶𝘇𝗮𝘁𝗶𝗼𝗻: Leverage distributed training, efficient attention mechanisms, and pipeline parallelism. Profile usage, compress checkpoints, and enable auto-resume for robustness. 𝟲. 𝗘𝘃𝗮𝗹𝘂𝗮𝘁𝗶𝗼𝗻 & 𝗩𝗮𝗹𝗶𝗱𝗮𝘁𝗶𝗼𝗻: Regularly evaluate using defined metrics and baseline comparisons. Test with few-shot prompts, review model outputs, and track performance metrics to prevent drift and overfitting. 𝟳. 𝗘𝘁𝗵𝗶𝗰𝗮𝗹 𝗮𝗻𝗱 𝗦𝗮𝗳𝗲𝘁𝘆 𝗖𝗵𝗲𝗰𝗸𝘀: Mitigate model risks by applying adversarial testing, output filtering, decoding constraints, and incorporating user feedback. Audit results to ensure responsible outputs. 🔸 𝟴. 𝗙𝗶𝗻𝗲-𝗧𝘂𝗻𝗶𝗻𝗴 & 𝗗𝗼𝗺𝗮𝗶𝗻 𝗔𝗱𝗮𝗽𝘁𝗮𝘁𝗶𝗼𝗻: Adapt models for specific domains using techniques like LoRA/PEFT and controlled learning rates. Monitor overfitting, evaluate continuously, and deploy with confidence. These principles form a unified blueprint for building robust, efficient, and production-ready LLMs—whether training from scratch or adapting pre-trained models.

Relying on one LLM provider like OpenAI is risky and often leads to unnecessary high costs and latency. But there's another critical challenge: ensuring LLM outputs align with specific guidelines and safety standards. What if you could address both issues with a single solution? This is the core promise behind Portkey's open-source AI Gateway. AI Gateway is an open-source package that seamlessly integrates with 200+ LLMs, including OpenAI, Google Gemini, Ollama, Mistral, and more. It not only solves the provider dependency problem but now also tackles the crucial need for effective guardrails by partnering with providers such as Patronus AI and Aporia. Key features: (1) Effortless load balancing across models and providers (2) Integrated guardrails for precise control over LLM behavior (3) Resilient fallbacks and automatic retries to guarantee your application recovers from failed LLM API requests (4) Adds minimal latency as a middleware (~10ms) (5) Supported SDKs include Python, Node.JS, Rust, and more One of the main hurdles to enterprise AI adoption is ensuring LLM inputs and outputs are safe and adhere to your company’s policies. This is why projects like Portkey are so useful. Integrating guardrails into an AI gateway creates a powerful combination that orchestrates LLM requests based on predefined guardrails, providing precise control over LLM outputs. Switching to more affordable yet performant models is a useful technique to reduce cost and latency for your app. I covered this and eleven more techniques in my last AI Tidbits Deep Dive https://lnkd.in/gucUZzYn GitHub repo https://lnkd.in/g8pjgT9R

Which #AIarchitecture fits your specific use case - LLM, SLM, FLM, or MoE? Modern #AIdevelopment requires strategic thinking about architecture selection from day one. Each of these four approaches represents a fundamentally different trade-off between computational resources, specialized performance, and deployment flexibility. The stakes are higher than most people realize, choosing the wrong architecture doesn't just impact performance metrics, it can derail entire projects, waste months of development cycles, and consume budgets that could have delivered significantly better results with the right initial architectural decision. 🔹#LLMs are strong at complex reasoning tasks : Their extensive pretraining on various datasets produces flexible models that handle intricate, multi-domain problems. These problems require a broad understanding and deep contextual insight. 🔹#SLMs focus on efficiency instead of breadth : They are designed with smaller datasets and optimized tokenization, making them suitable for mobile applications, edge computing, and real-time systems where speed and resource limits matter. 🔹#FLMs deliver domain expertise through specialization : By fine-tuning base models with domain-specific data and task-specific prompts, they consistently outperform general models in specialized fields like medical diagnosis, legal analysis, and technical support. 🔹#MoE architectures allow for smarter scaling : Their gating logic activates only the relevant expert layers based on the context. This feature makes them a great choice for multi-domain platforms and enterprise applications needing efficient scaling while keeping performance high. The essential factor is aligning architecture capabilities with your actual needs: performance requirements, latency limits, deployment environment, and cost factors. Success comes from picking the right tool for the task.

The AI research Paper Behind Manus Agent: Why Code-Centric Agents Are Outperforming Everything Else  ... 👉 Why Manus' Approach Defies Conventional Agent Design Last week's viral discourse about Manus' capabilities traces back to a fundamental insight: LLMs achieve peak performance when operating in their native "language" - code. The founder’s revelation about avoiding MCP (Manual Control Protocols) in favor of code-driven actions explains why Manus:  - Solves tasks in 30% fewer steps than JSON/text-based agents   - Handles edge cases through runtime code iteration (not pre-defined workflows)   - Reduces hallucination errors by 22% via Python's structured feedback  Traditional agent architectures hit a complexity wall because they force LLMs to "describe" actions rather than "execute" them.  👉 What Makes Code-Centric Agents Different?  Manus' foundation builds on three pivotal insights from Xingyao Wang’s research: 1. "Code as cognitive scaffolding": Programming constructs (loops, conditionals) provide built-in reasoning frameworks   2. "Training alignment": LLMs process code 18% faster than natural language due to code-heavy pretraining   3. "Error-driven evolution": 63% of successful task completions required mid-execution code corrections  This approach transforms LLMs from passive planners to active executors. Where competitors chain 5-7 API calls for basic workflows, Manus agents write consolidated code blocks:  ```python   # Single operation handling data fetch, analysis, and formatting   stock_data = yfinance.download(tickers, period=lookback)   cleaned = stock_data.fillna(method='ffill').dropna()   analysis = cleaned.ta.strategy(MyCustomStrategy())   save_to_database(analysis, format='parquet')   ```` 👉 How Code-Centric Design Unlocks Real-World Impact Manus' technical whitepaper reveals their implementation recipe: 1. "Dynamic code generation": Agents write minimal code for immediate next-step execution 2. "Jupyter-like sandboxing": Isolated environments with package auto-install capabilities 3. "Semantic error recovery": Using traceback messages to rewrite code vs restarting In stress tests, this handled 89% of failed API calls autonomously by: - Modifying input parameters (38% of cases) - Switching alternative libraries (27%) - Implementing custom fallback logic (24%) 👉 The Bottom Line  Manus' traction stems from recognizing that LLMs aren't just tools for programmers - they're becoming programmers themselves. As the founder noted: "Our agents don't use tools - they create them on demand." What legacy processes could your team reinvent by treating code as the atomic unit of AI operation? The paper’s findings (and Manus’ results) suggest we’re just scratching the surface: CodeAct Research. Xingyao Wang, Yangyi Chen, Lifan Yuan, Yizhe Zhang, Yunzhu Li, Hao Peng, Heng Ji Executable Code Actions Elicit Better LLM Agents Link to the paper in the first comment.

𝐋𝐋𝐌 𝐓𝐫𝐚𝐢𝐧𝐢𝐧𝐠 𝐯𝐬 𝐅𝐢𝐧𝐞-𝐓𝐮𝐧𝐢𝐧𝐠 𝐯𝐬 𝐈𝐧𝐟𝐞𝐫𝐞𝐧𝐜𝐞: 𝐓𝐡𝐞 𝟑 𝐏𝐢𝐥𝐥𝐚𝐫𝐬 𝐨𝐟 𝐀𝐈 𝐀𝐠𝐞𝐧𝐭 𝐃𝐞𝐯𝐞𝐥𝐨𝐩𝐦𝐞𝐧𝐭 Large Language Models (LLMs) do not just appear magically ready to answer your questions. Their journey to becoming smart assistants involves three very different stages and each one matters when building AI agents. 𝟏. 𝐓𝐫𝐚𝐢𝐧𝐢𝐧𝐠 This is the childhood and education phase. The model is exposed to massive datasets books, articles, code, and more to learn general language patterns. It’s expensive, time-consuming, and usually done only by big AI labs. 𝟐. 𝐅𝐢𝐧𝐞-𝐓𝐮𝐧𝐢𝐧𝐠 This is the specialisation phase. Here, a pre-trained model is adapted to a specific domain or task. For example: teaching a general model to excel in medical advice, legal reasoning, or customer support scripts. It’s faster and cheaper than full training, but still needs good-quality, domain-specific data. 𝟑. 𝐈𝐧𝐟𝐞𝐫𝐞𝐧𝐜𝐞 This is the job performance phase. It’s when the model is put to work generating answers, solving problems, and interacting with users in real time. Efficiency here depends on how well the model was trained, fine-tuned, and optimised. 𝐖𝐡𝐲 𝐝𝐨𝐞𝐬 𝐭𝐡𝐢𝐬 𝐦𝐚𝐭𝐭𝐞𝐫? 𝐖𝐡𝐞𝐧 𝐲𝐨𝐮 𝐚𝐫𝐞 𝐛𝐮𝐢𝐥𝐝𝐢𝐧𝐠 𝐀𝐈 𝐚𝐠𝐞𝐧𝐭𝐬, 𝐲𝐨𝐮 𝐧𝐞𝐞𝐝 𝐭𝐨 𝐤𝐧𝐨𝐰 𝐰𝐡𝐞𝐫𝐞 𝐭𝐨 𝐢𝐧𝐯𝐞𝐬𝐭 𝐲𝐨𝐮𝐫 𝐞𝐟𝐟𝐨𝐫𝐭: - Full training is overkill unless you are creating something from scratch. - Fine-tuning helps your agent become an expert in your domain. - Optimising inference ensures it is fast, responsive, and cost-effective for users. 𝐈𝐦𝐩𝐚𝐜𝐭 𝐨𝐧 𝐀𝐈 𝐀𝐠𝐞𝐧𝐭 𝐃𝐞𝐯𝐞𝐥𝐨𝐩𝐦𝐞𝐧𝐭: - Poor training → weak foundation - No fine-tuning → generic, unhelpful responses - Slow inference → bad user experience Balancing these stages is what turns an AI agent from “𝐣𝐮𝐬𝐭 𝐚𝐧𝐨𝐭𝐡𝐞𝐫 𝐜𝐡𝐚𝐭𝐛𝐨𝐭” into a specialised, high-performance assistant. 𝐈𝐟 𝐲𝐨𝐮 𝐡𝐚𝐝 𝐭𝐨 𝐜𝐡𝐨𝐨𝐬𝐞 𝐨𝐧𝐥𝐲 𝐨𝐧𝐞 𝐭𝐨 𝐢𝐧𝐯𝐞𝐬𝐭 𝐢𝐧 𝐟𝐨𝐫 𝐲𝐨𝐮𝐫 𝐀𝐈 𝐚𝐠𝐞𝐧𝐭 𝐫𝐢𝐠𝐡𝐭 𝐧𝐨𝐰 𝐛𝐞𝐭𝐭𝐞𝐫 𝐭𝐫𝐚𝐢𝐧𝐢𝐧𝐠 𝐝𝐚𝐭𝐚, 𝐝𝐞𝐞𝐩𝐞𝐫 𝐟𝐢𝐧𝐞-𝐭𝐮𝐧𝐢𝐧𝐠, 𝐨𝐫 𝐟𝐚𝐬𝐭𝐞𝐫 𝐢𝐧𝐟𝐞𝐫𝐞𝐧𝐜𝐞, 𝐰𝐡𝐢𝐜𝐡 𝐰𝐨𝐮𝐥𝐝 𝐲𝐨𝐮 𝐩𝐫𝐢𝐨𝐫𝐢𝐭𝐢𝐬𝐞?

Think of an advanced LLM integration strategy where Gateways give you full control, Routers provide precision in routing requests, and Proxies ensure seamless integration across systems. Gateways handle governance, authentication, and traffic control, Routers direct workloads to the right models or endpoints based on context, and Proxies simplify compatibility between APIs, making everything flow effortlessly. Together, they create a unified architecture that’s efficient, scalable, and easy to manage—tailored for a smarter, faster deployment of LLMs Key Takeaways: Centralized Governance: Gateways enable full control over traffic, security, and user authentication. Context-Aware Precision: Routers optimize performance by intelligently directing requests to the right model or endpoint. Seamless Integration: Proxies bridge compatibility gaps between systems and APIs, ensuring a smooth deployment process. Scalability and Efficiency: The combined architecture allows for streamlined operations, resource optimization, and scalable deployment across diverse use cases. Future-Ready Design: This strategy ensures flexibility for evolving AI workloads and infrastructure needs. https://lnkd.in/eDFp8hN6

Think all LLMs are the same? Not even close! 𝗛𝗲𝗿𝗲'𝘀 𝗵𝗼𝘄 𝘁𝗼 𝗰𝗵𝗼𝗼𝘀𝗲 𝘁𝗵𝗲 𝗿𝗶𝗴𝗵𝘁 𝗟𝗟𝗠 𝗳𝗼𝗿 𝗔𝗜 𝗔𝘂𝘁𝗼𝗺𝗮𝘁𝗶𝗼𝗻 𝗳𝗼𝗿 𝗘𝗻𝘁𝗲𝗿𝗽𝗿𝗶𝘀𝗲𝘀 (Super detailed steps coming)👇🏻 Selecting the right LLM can make or break your automation stack—cost, performance, compliance, and scalability all hinge on it. Follow this Step-by-step Comparative Analysis of LLMs for Enterprise Use: 1️⃣ 𝗗𝗲𝗳𝗶𝗻𝗲 𝗬𝗼𝘂𝗿 𝗣𝗿𝗶𝗺𝗮𝗿𝘆 𝗨𝘀𝗲 𝗖𝗮𝘀𝗲 𝗙𝗶𝗿𝘀𝘁 Are you automating customer support, internal knowledge retrieval, report generation, or code generation? Your use case dictates whether you need: - Fast inference (small models like Claude Haiku or Mistral) - Deep reasoning (GPT-4, Claude Opus) - Multilingual (Gemini, LLaMA) 2️⃣ 𝗦𝗵𝗼𝗿𝘁𝗹𝗶𝘀𝘁 𝗕𝗮𝘀𝗲𝗱 𝗼𝗻 𝗞𝗲𝘆 𝗘𝘃𝗮𝗹𝘂𝗮𝘁𝗶𝗼𝗻 𝗠𝗲𝘁𝗿𝗶𝗰𝘀 Create a scorecard across: - Accuracy & relevance (test on your own data) - Latency (real-time or async?) - Context window (important for long-form use) - Pricing per token or per 1K inputs/outputs - Data privacy/compliance (can it run on-premise or VPC?) 3️⃣ 𝗕𝗲𝗻𝗰𝗵𝗺𝗮𝗿𝗸 𝗼𝗻 𝗬𝗼𝘂𝗿 𝗗𝗼𝗺𝗮𝗶𝗻 𝗗𝗮𝘁𝗮 Public benchmarks (MMLU, HumanEval) ≠ real-world performance. Always test: - With your internal docs, code, or chats - Across multiple prompt variations - With/without RAG integration 4️⃣ 𝗘𝘃𝗮𝗹𝘂𝗮𝘁𝗲 𝗔𝗣𝗜 𝗙𝗹𝗲𝘅𝗶𝗯𝗶𝗹𝗶𝘁𝘆 & 𝗘𝗰𝗼𝘀𝘆𝘀𝘁𝗲𝗺 𝗙𝗶𝘁 Ask: - Does it integrate well with existing stack (LangChain, Vector DBs, CRMs)? - Are usage quotas, rate limits, and throttling acceptable? - SDK/API maturity? 5️⃣ 𝗖𝗼𝗺𝗽𝗮𝗿𝗲 𝗗𝗲𝗽𝗹𝗼𝘆𝗺𝗲𝗻𝘁 𝗢𝗽𝘁𝗶𝗼𝗻𝘀 - Cloud APIs – OpenAI, Anthropic, Gemini - Private Hosted – Mistral, LLaMA 3 via AWS/GCP - On-Prem – Open-source LLMs like LLaMA 2/3, Mistral 7B via Ollama or vLLM 6️⃣ 𝗗𝗼𝗻’𝘁 𝗢𝘃𝗲𝗿𝗹𝗼𝗼𝗸 𝗧𝗼𝘁𝗮𝗹 𝗖𝗼𝘀𝘁 𝗼𝗳 𝗢𝘄𝗻𝗲𝗿𝘀𝗵𝗶𝗽 Factor in: - Prompt engineering & fine-tuning costs - Vector DB infra (if using RAG) - Inference costs at scale - Sometimes a smaller tuned model outperforms GPT-4 at 1/10th the cost. 7️⃣ 𝗚𝗲𝗻 𝗔𝗜 ≠ 𝗖𝗼𝗻𝘃𝗲𝗿𝘀𝗮𝘁𝗶𝗼𝗻𝗮𝗹 𝗔𝗜 Generative LLMs are powerful but often overkill for rule-based workflows. Combine LLMs + conversational AI platforms for structured automation. Think: - GPT-4 + Kore.ai - Claude + Salesforce Einstein - Mistral + Rasa or Botpress Start with pilot tests. Track response quality, token burn, and latency. Then double down where you see ROI. The right LLM isn’t the biggest one. It’s the one that aligns with your goals, infra, and budget. Do you agree?? #AI #LLMs #AILeader #TechLeader

Just published: A comparative analysis of ethical reasoning across major LLMs, examining how different model architectures and training approaches influence moral decision-making capabilities. We put six leading models (including GPT-4, Claude, and LLaMA) through rigorous ethical reasoning tests, moving beyond traditional alignment metrics to explore their explicit moral logic frameworks. Using established ethical typologies, we analyzed how these systems articulate their decision-making process in classic moral dilemmas. Technical insight: Despite architectural differences, we found remarkable convergence in ethical reasoning patterns - suggesting that current training methodologies might be creating similar moral scaffolding across models. The variations we observed appear more linked to fine-tuning and post-training processes than base architecture. Critical for ML practitioners: All models demonstrated sophisticated reasoning comparable to graduate-level philosophy, with a strong bias toward consequentialist frameworks. Implications for model development? This convergence raises interesting questions about diversity in ethical reasoning capabilities and potential training modifications. Check out the full paper here: https://lnkd.in/gFamrRVc #LLMs #MachineLearning #AIAlignment #ModelDevelopment

❌ "𝗝𝘂𝘀𝘁 𝘂𝘀𝗲 𝗖𝗵𝗮𝘁𝗚𝗣𝗧" 𝗶𝘀 𝘁𝗲𝗿𝗿𝗶𝗯𝗹𝗲 𝗮𝗱𝘃𝗶𝗰𝗲. Here's what most AI & Automation leaders get wrong about LLMs: They're building their entire AI infrastructure around ONE or TWO models. The reality? There is no single "best LLM." The top models swap positions every few months, and each has unique strengths and costly blindspots. I analyzed the 6 frontier models driving enterprise AI today. Here's what I found: 𝟭. 𝗚𝗲𝗺𝗶𝗻𝗶 (𝟯 𝗣𝗿𝗼/𝗨𝗹𝘁𝗿𝗮) ✓ Superior reasoning and multimodality ✓ Excels at agentic workflows ✗ Not useful for writing tasks 𝟮. 𝗖𝗵𝗮𝘁𝗚𝗣𝗧 (𝗚𝗣𝗧-𝟱) ✓ Most reliable all-around ✓ Mature ecosystem ✗ A lot prompt-dependent 𝟯. 𝗖𝗹𝗮𝘂𝗱𝗲 (𝟰.𝟱 𝗦𝗼𝗻𝗻𝗲𝘁/𝗢𝗽𝘂𝘀) ✓ Industry leader in coding & debugging ✓ Enterprise-grade safety ✗ Opus is very expensive 𝟰. 𝗗𝗲𝗲𝗽𝗦𝗲𝗲𝗸 (𝗩𝟯.𝟮-𝗘𝘅𝗽) ✓ Great cost-efficiency ✓ Top-tier coding and math ✗ Less mature ecosystem 𝟱. 𝗚𝗿𝗼𝗸 (𝟰/𝟰.𝟭) ✓ Real-time data access ✓ High-speed querying ✗ Limited free access 𝟲. 𝗞𝗶𝗺𝗶 𝗔𝗜 (𝗞𝟮 𝗧𝗵𝗶𝗻𝗸𝗶𝗻𝗴) ✓ Massive context windows ✓ Superior long document analysis ✗ Chinese market focus The winning strategy isn't picking one. It's orchestration. Here's the playbook: → Stop hardcoding single-vendor APIs → Route code writing & reviews to Claude → Send agentic & multimodal workflows to Gemini → Use DeepSeek for cost-effective baseline tasks → Build multi-step workflows, not one-shot prompts 𝗧𝗵𝗲 𝗯𝗼𝘁𝘁𝗼𝗺 𝗹𝗶𝗻𝗲? Your competitive advantage isn't choosing the "best" model. It's building orchestration systems that route intelligently across all of them. The future of enterprise automation is agentic systems that manage your LLM landscape for you. What's the LLM strategy that's working for you? ---- 🎯 Follow for Agentic AI, Gen AI & RPA trends: https://lnkd.in/gFwv7QiX Repost if this helped you see the shift ♻️