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LangChain Deep Agent on Amazon Bedrock
This post outline the architecture and implementation of LangChain's Deep Agents framework, explaining how foundation models are combined with an Agent Harness to handle complex autonomous workflows. Key concepts explored include prompt caching for token optimization, built-in and custom tools for executing actions or querying databases like MovieLens, and the integration of Model Context Protocol (MCP) servers. Additionally, the text details state management utilizing threads and checkpointers to preserve conversation history across sessions, while showcasing time travel capabilities for branching or reviewing past states. Finally, the materials emphasize Human-in-the-Loop (HITL) safety controls, allowing developers to pause execution before sensitive actions take place and review, edit, approve, or reject pending operations.
Kubernetes on Raspberry Pi 5 : Part 3 - AI/ML at Scale
Kubernetes has become the control plane for machine learning: GPUs are scheduled as extended resources, training runs become Jobs, model servers become Deployments, and MLOps platforms such as Kubeflow extend the API with custom resources. Part 3 moves beyond the Raspberry Pi cluster to explain how a production AI/ML stack is layered, from GPU hardware and the operating system through Kubernetes and the GPU Operator, to Kubeflow, Kueue, KServe and NVIDIA Triton. It uses the Canonical and NVIDIA “Run AI at Scale” whitepaper as a guide, updates it to late 2026, and ends with affordable ways to build the same stack in the cloud or on a mini PC with an NVIDIA eGPU.
LangChain Agent on Bedrock
This post provides a comprehensive technical walkthrough for constructing LangChain agents on AWS Bedrock using uv package management and foundation models such as Anthropic Claude, OpenAI, and Google Gemma via the Bedrock Converse API. It compares lightweight ReAct agents ( create_agent ) designed for single-purpose tasks against feature-rich deep agents ( create_deep_agent ). Furthermore, the guide details short-term memory checkpoints and demonstrates optimizing Model Context Protocol (MCP) tool execution via "Code Mode", presenting tools as sandbox-executed code wrappers to preserve data privacy, enable control loops, and decrease token waste by up to 98.7%.
Kubernetes on Raspberry Pi 5 : Part 2 - Object Model
The provided text details the core Object Model and API abstractions of Kubernetes within the context of a 4-node Raspberry Pi 5 cluster. It explains how the control plane continuously reconciles the actual state with the desired state specified in declarative YAML or JSON manifests. Furthermore, the material breaks down essential architectural building blocks such as nodes, namespaces, pods, labels, replica sets, and deployments. Practical operations are also demonstrated, including how to handle rolling updates, configure internal service discovery, and troubleshoot container environments using kubectl.
Claude MCP on Amazon Bedrock
This post details the implementation of the Model Context Protocol (MCP) to connect external capabilities with Claude models running on Amazon Bedrock. It explains how the architecture decouples integration code by leveraging MCP servers and clients that communicate via flexible transport layers like standard input/output or network protocols. Through practical code examples, it demonstrates how to translate tool definitions into Bedrock's required format for dynamic function execution. Furthermore, the texts contrast three foundational primitives: tools driven autonomously by the model, resources fetched proactively by application code, and prompts triggered explicitly by users. Ultimately, these components establish a standardized, secure, and maintainable framework for building sophisticated agentic applications.