Tools For Managing Enterprise AI Deployments

After extensive research and hands-on experience, I've created this comprehensive visualization of the AI Agents ecosystem. Whether you're building, deploying, or scaling AI agents, this stack covers all essential components. Key Components: 1. Vertical Agents - Industry leaders like Anthropic, Decagon, and Perplexity showing what's possible - Specialized solutions from MultiOn, Harvey, and others 2. Observability & Memory - Tools like LangSmith and Arize for monitoring - Memory solutions: MemGPT, LangChain for context retention - Braintrust and AgentOps.ai for performance tracking 3. Framework & Hosting - Robust frameworks: Letta, LangGraph, AutogenAI - Reliable hosting: Letta, LangGraph, LiveKit - Integration tools from Semantic Kernel and Phidata 4. Model Serving & Storage - Enterprise solutions: OpenAI, Anthropic, Together.ai - Vector stores: Chroma, Pinecone, Supabase - Efficient serving with vLLM and SGL You can start with one tool from each category based on your specific use case. The ecosystem is evolving rapidly, but these foundations will remain relevant. Perfect reference for: - AI Engineers - MLOps Teams - Product Managers - Tech Architects Feel free to save and share! Let me know if you have questions about implementing any part of this stack.

AI field note: introducing Toolshed from PwC, a novel approach to scaling tool use with AI agents (and winner of best paper/poster at ICAART). LLMs are limited in the number of external tools agents can use at once., usually to about 128 which sounds like a lot, but in a real-world enterprise quickly becomes a limitation. This creates a major bottleneck for real-world applications like database operations or collaborative AI systems that need access to hundreds or thousands of specialized functions. Enter Toolshed, a novel approach from PwC that reimagines tool retrieval and usage that enables AI systems to effectively utilize thousands of tools without fine-tuning or retraining. Toolshed introduces two primary technical components that work together to enable scalable tool use beyond the typical 128-tool limit: 📚 Toolshed Knowledge Bases: Vector databases optimized for tool retrieval that store enhanced representations of each tool, including: tool name and description, argument schema with parameter details, synthetically generated hypothetical questions, key topics and intents the tool addresses Tool-specific metadata for execution. 🧲 Advanced RAG-Tool Fusion: A comprehensive three-phase approach that creatively applies retrieval-augmented generation techniques to the tool selection problem, enhancing tool documents with rich metadata and contextual information accuracy, decomposing queries into independent sub-tasks, and reranking to ensure optimal tool selection. The paper demonstrates significant quantitative improvements over existing methods through rigorous benchmarking and systematic testing: ⚡️ 46-56% improvement in retrieval accuracy (on ToolE and Seal-Tools benchmarks vs. standard methods like BM25). ✨ Optimized top-k selection threshold to systematically balance retrieval accuracy with agent performance and token costs. 💫 Scalability testing: Proven effective when scaling to 4,000 tools. 🎁 Zero fine-tuning required: Works with out-of-the-box embeddings and LLMs. Not too shabby. Toolshed addresses challenges in enterprise AI deployment, offering practical solutions for complex production environments such as cross-domain versatility (we successfully tested across finance, healthcare, and database domains), secure database interactions, multi-agent orchestration, and cost optimization. Congratulations to Elias Lumer, Vamse Kumar Subbiah, and team for winning the best poster award at the International Conference on Agents and AI! For any organization building production AI systems, Toolshed offers a practical path to more capable, reliable tool usage at scale. Really impressive and encouraging work. Link in description.

IBM 💙 Open Source Our AI platform, watsonx, is powered by a rich stack of open source technologies, enhancing AI workflows with transparency, responsibility, and enterprise readiness. Here's the list of key projects: 𝗧𝗿𝗮𝗶𝗻𝗶𝗻𝗴 & 𝗩𝗮𝗹𝗶𝗱𝗮𝘁𝗶𝗼𝗻: - CodeFlare: Simplifies the scaling and management of distributed AI workloads by providing an easy-to-use interface for resource allocation, job submission, and workload management. - Ray / KubeRay: A framework for scaling distributed Python workloads. KubeRay integrates Ray with Kubernetes, enabling distributed AI tasks to run efficiently across clusters. - PyTorch: An open-source framework for deep learning model development, supporting both small and large distributed training, ideal for building AI models with over 10 billion parameters. - Kubeflow Training Operator: Orchestrates distributed training jobs across Kubernetes, supporting popular ML frameworks like PyTorch and TensorFlow for scalable AI model training. - Job Scheduler (Kueue/MCAD): Manages job scheduling and resource quotas, ensuring that distributed AI workloads are only started when sufficient resources are available. 𝗧𝘂𝗻𝗶𝗻𝗴 & 𝗜𝗻𝗳𝗲𝗿𝗲𝗻𝗰𝗲: - KServe: A Kubernetes-based platform for serving machine learning models at scale, providing production-level model inference for frameworks. - fms-hf-tuning: A collection of recipes for fine-tuning Hugging Face models using PyTorch’s distributed APIs, optimized for performance and scalability. - vLLM: A fast and flexible library designed for serving LLMs in both batch and real-time scenarios. - TGIS (Text Generation Inference Server): IBM’s fork of Hugging Face’s TGI, optimized for serving LLMs with high performance. - PyTorch: Used for both training and inference, this is a core framework in watsonx. - Hugging Face libraries: Offers a rich collection of pre-trained models and datasets, to provide cutting-edge AI capabilities. - Kubernetes DRA/InstaSlice: DRA allows for dynamic resource allocation in Kubernetes clusters, while InstaSlice facilitates resource sharing, particularly for GPU-intensive AI tasks. 𝗔𝗜 𝗗𝗲𝘃𝗲𝗹𝗼𝗽𝗺𝗲𝗻𝘁 𝗟𝗶𝗳𝗲𝗰𝘆𝗰𝗹𝗲: - Kubeflow & Pipelines: Provides end-to-end orchestration for AI workflows, automating everything from data preprocessing to model deployment and monitoring. - Open Data Hub: A comprehensive platform of tools for the entire AI lifecycle, from model development to deployment. - InstructLab: A project for shaping LLMs, allowing developers to enhance model capabilities by contributing skills and knowledge. - Granite models: IBM’s open source LLMs, spanning various modalities and trained on high-quality data. We're committed to the future of Open Source and its impact on the AI community.

Generative AI is a complete set of technologies that work together to provide intelligence at scale. This stack includes the foundation models that create text, images, audio, or code. It also features production monitoring and observability tools that ensure systems are reliable in real-world applications. Here’s how the stack comes together: 1. 🔹Foundation Models At the base, we have models trained on large datasets, covering text (GPT, Mistral, Anthropic), audio (ElevenLabs, Speechify, Resemble AI), 3D (NVIDIA, Luma AI, Open Source), image (Stability AI, Midjourney, Runway, ClipDrop), and code (Codium, Warp, Sourcegraph). These are the core engines of generation. 2. 🔹Compute Interface To power these models, organizations rely on GPU supply chains (NVIDIA, CoreWeave, Lambda) and PaaS providers (Replicate, Modal, Baseten) that provide scalable infrastructure. Without this computing support, modern GenAI wouldn’t be possible. 3. 🔹Data Layer Models are only as good as their data. This layer includes synthetic data platforms (Synthesia, Bifrost, Datagen) and data pipelines for collection, preprocessing, and enrichment. 4. 🔹Search & Retrieval A key component is vector databases (Pinecone, Weaviate, Milvus, Chroma) that allow for efficient context retrieval. They power RAG (Retrieval-Augmented Generation) systems and keep AI responses grounded. 5. 🔹ML Platforms & Model Tuning Here we find training and fine-tuning platforms (Weights & Biases, Hugging Face, SageMaker) alongside data labeling solutions (Scale AI, Surge AI, Snorkel). This layer helps models adjust to specific domains, industries, or company knowledge. 6. 🔹Developer Tools & Infrastructure Developers use application frameworks (LangChain, LlamaIndex, MindOS) and orchestration tools that make it easier to build AI-driven apps. These tools connect raw models and usable solutions. 7. 🔹Production Monitoring & Observability Once deployed, AI systems need supervision. Tools like Arize, Fiddler, Datadog and user analytics platforms (Aquarium, Arthur) track performance, identify drift, enforce firewalls, and ensure compliance. This is where LLMOps comes in, making large-scale deployments reliable, safe, and clear. The Generative AI Stack turns raw model power into practical AI applications. It combines compute, data, tools, monitoring, and governance into one seamless ecosystem. #GenAI