Ai-Agents
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The Juggler
You already understand async systems, fault tolerance, and distributed patterns. You just know them by …Juggling and Technology
The cascade as a distributed system. Props as AI agent types. Dropping a ball as incident response. If you …Testing and Evaluating AI Agent Performance
Frameworks for evaluating AI agents that plan, use tools, and take actions, covering correctness, reliability, …Testing AI Agent Tool Calls
How to test AI agents that use tools: mocking tool responses, testing tool selection logic, error handling, …Sandbox Testing Pattern for AI Agents
Sandboxed execution environments for testing AI agents with real tool access without production side effects: …Multi-Agent Orchestration
Multi-agent orchestration is the pattern of coordinating multiple specialized AI agents to collaborate on …Amazon Bedrock AgentCore
Amazon Bedrock AgentCore is a managed runtime and governance layer for deploying, operating, and securing AI …Strands Agents - AWS-Native Agent SDK
What Strands Agents is, how it differs from CrewAI and LangGraph, and when to use it for AWS-hosted agent …Pydantic AI - Type-Safe Agent Development
Using Pydantic AI to build AI agents with validated inputs and outputs, Bedrock backend support, and Python …Model Context Protocol (MCP) - Universal Tool Interface for AI Agents
What the Model Context Protocol is, how it enables AI agents to use tools through a standard interface, and …Amazon Bedrock AgentCore - Serverless AI Agent Hosting
How Amazon Bedrock AgentCore provides managed infrastructure for running AI agents at scale without managing …RAG Implementation Patterns - Retrieval Augmented Generation in Practice
Practical patterns for building production RAG systems: chunking strategies, retrieval optimization, …Multi-Agent Systems
Definition, architecture patterns, and frameworks for multi-agent AI systems - and the signals that indicate a …Multi-Agent AI Systems - When One Model Is Not Enough
A practical introduction to multi-agent AI architectures: when to use them, how they work, and which …LlamaIndex - RAG and Agent Framework
Using LlamaIndex for retrieval-augmented generation, data connectors, and agent workflows, with Bedrock and …LangGraph - Stateful AI Agent Graphs
How LangGraph models AI agent workflows as stateful graphs, enabling cyclic execution, human-in-the-loop, and …Langfuse - LLM Observability and Tracing
Using Langfuse to trace LLM calls, evaluate outputs, and monitor AI application quality in production.Knowledge Base (AI)
What an AI knowledge base is, how it differs from a traditional knowledge base, vector stores, and RAG …CrewAI vs Strands Agents - Multi-Agent Framework Comparison
Architecture differences, AWS integration, and decision criteria for choosing between CrewAI and Strands …CrewAI - Multi-Agent Orchestration Framework
What CrewAI is, how it models multi-agent systems as crews with roles and tasks, integration with LLM …Building RAG Systems - A Step-by-Step Guide
Document ingestion, chunking strategies, embedding models, vector stores, retrieval tuning, and generation …Building Enterprise AI Chatbots That Actually Help
Practical guidance for building customer-facing AI chatbots that deliver real value - architecture, knowledge …AI Guardrails - Safety and Compliance Controls
What AI guardrails are, the types of controls they enforce, how to implement them in enterprise applications, …AI Agents - Autonomous Task Execution
What AI agents are, how they differ from simple LLM calls, the key design patterns, and what makes agents fail …
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