AI Agent
What AI agents are, how they autonomously plan and execute tasks, and the architectural patterns that distinguish agents from simple …
Orchestration
Apache Airflow vs AWS Step Functions for ML Pipelines
Comparing Airflow and Step Functions for orchestrating ML training, data processing, and deployment pipelines.
Apache Airflow vs Dagster for ML Pipeline Orchestration
Comparing Airflow and Dagster for orchestrating data and ML pipelines, covering architecture, developer experience, testing, and ML-specific …
AutoGen vs CrewAI - Multi-Agent Systems Compared
Comparing Microsoft AutoGen and CrewAI for building multi-agent AI systems, covering conversation patterns, role design, and orchestration.
Azure Data Factory - Cloud Data Integration and ETL
Azure Data Factory is a managed cloud ETL service for building data integration pipelines that move and transform data at scale across cloud …
Azure Logic Apps - Low-Code Workflow Orchestration
Azure Logic Apps is a cloud-based platform for creating and running automated workflows that integrate apps, data, services, and systems …
Cloud Composer - Managed Apache Airflow Service
Google Cloud Composer is a fully managed workflow orchestration service built on Apache Airflow for authoring, scheduling, and monitoring …
Cloud Workflows - Serverless Orchestration Service
Google Cloud Workflows is a serverless orchestration service that sequences HTTP-based API calls, Cloud Functions, and GCP services into …
Compound AI System
An AI architecture that combines multiple models, retrievers, tools, and programmatic logic to solve tasks that exceed the capabilities of …
Fan-Out/Fan-In Pattern for AI Workloads
Parallel processing pattern for AI tasks: split work across multiple model calls, process concurrently, and aggregate results for faster …
Multi-Agent Orchestration
Multi-agent orchestration is the pattern of coordinating multiple specialized AI agents to collaborate on complex tasks, with roots in …
Multi-Model Routing Patterns
Strategies for routing requests to different AI models based on task complexity, cost constraints, and latency requirements. Router design, …
Orchestrator-Worker Pattern
An orchestrator LLM decomposes complex tasks and delegates subtasks to specialized worker models or agents, coordinating results into a …
Plan-and-Execute Pattern - Separating Planning from Execution in AI Agents
A two-phase agent pattern where a capable planner model creates a step-by-step plan, then delegates each step to cheaper, faster executor …
Saga Pattern
What the saga pattern is, how it manages distributed transactions across microservices, and implementation approaches.
Workflow Engine
Software that automates the execution of business processes by coordinating tasks, decisions, and integrations according to a defined …
Amazon EventBridge - Event-Driven AI Orchestration
Using Amazon EventBridge to connect AWS AI services, trigger pipelines from S3 events, and build loosely coupled multi-step workflows.
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 applications.
Agentic Workflow Patterns - From Simple Chains to Complex Orchestration
Chain, router, parallel, hierarchical, and loop patterns for AI agents. When to use each, error handling, and fallback strategies.
AWS Step Functions - Workflow Orchestration for AI Pipelines
How Step Functions orchestrates multi-step AI workflows, handles retries and errors, and integrates with other AWS services - with practical …
AWS Step Functions vs Lambda Chains for AI Orchestration
When to use state machines vs direct invocation for AI workflows. Error handling, retry patterns, cost comparison, and visibility …
CrewAI - Multi-Agent Orchestration Framework
What CrewAI is, how it models multi-agent systems as crews with roles and tasks, integration with LLM backends, and when to use it versus …
Data Pipeline Patterns for AI/ML Workloads
Practical patterns for building reliable data pipelines that feed AI and ML systems - ingestion, transformation, feature engineering, and …
LangGraph - Stateful AI Agent Graphs
How LangGraph models AI agent workflows as stateful graphs, enabling cyclic execution, human-in-the-loop, and complex multi-step agent …
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 frameworks are production-ready.
Multi-Agent Systems
Definition, architecture patterns, and frameworks for multi-agent AI systems - and the signals that indicate a single-agent approach is no …