1 min read
Building Multi-Agent Systems with AutoGen and Azure
Instead of a single LLM handling all tasks, multi-agent systems divide work among specialized agents that communicate and coordinate to achieve goals.
Orchestration
15 articles
1 min read
Semantic Kernel: Orchestrating AI Agents with Plugins and Planners
Semantic Kernel abstracts the complexity of working with multiple AI services while providing extensibility through plugins. This allows developers to…
1 min read
Multi-Agent Systems: Orchestrating Specialized AI Agents
Multi-agent architectures shine for tasks requiring diverse expertise: research reports, code reviews, complex analysis, and creative projects. The key is…
1 min read
Building Multi-Agent Systems with Semantic Kernel
Define clear responsibilities for each agent to avoid confusion and improve reliability. Multi-agent architectures excel at complex workflows where…
1 min read
Tool Orchestration: Managing Complex Tool Interactions
Robust tool orchestration enables complex, reliable AI workflows.
1 min read
AI Orchestration Frameworks: Building Complex AI Workflows
Choose orchestration frameworks based on workflow complexity and team expertise.
1 min read
Multi-Agent Frameworks: Orchestrating AI Agent Collaboration
Multi-agent systems unlock sophisticated automation capabilities. Start with simple patterns and evolve complexity as needed.
1 min read
Agent Orchestration Patterns for Production
Building single agents is one thing. Orchestrating multiple agents for complex workflows is another. Today I'm exploring production-ready orchestration…
1 min read
Automation in Fabric: Scheduling and Orchestration
Automation is key to maintaining reliable data pipelines. This guide covers scheduling, orchestration, and automation patterns in Microsoft Fabric.
1 min read
Fabric Data Pipelines: Orchestration Patterns
A Fabric pipeline isn't just a sequence of activities — it's an orchestration layer with conditional branching, parameter passing, loops, and error handling…
1 min read
Fabric Pipelines: Orchestrating Data Workflows
Fabric Pipelines enable robust data orchestration with full control flow capabilities. Tomorrow, I will cover Dataflows Gen2.
1 min read
AI Orchestration Patterns: Coordinating Multi-Model Systems
AI orchestration patterns enable building sophisticated AI systems from modular components. The key is designing for reliability, observability, and…
1 min read
Databricks Workflows: Orchestrating Data and ML Pipelines
Key features: Jobs : Multi task workflows with dependencies Triggers : Schedule, file arrival, or API based Compute : Job clusters or serverless Monitoring…
2 min read
Databricks Notebook Workflows for Data Pipelines
Databricks notebook workflows—chaining notebooks together using dbutils.notebook.run()—are the stepping stone between "notebooks as scripts" and proper…
1 min read
Azure Durable Functions: Orchestration Patterns
Function chaining, fan-out/fan-in, human interaction, and eternal orchestrations — Durable Functions exposes a small set of patterns that cover most…