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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.
AI Agents
43 articles
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Ignite 2025 Day 3: Copilot Extensions and Agent Framework
The Copilot agent framework represents a significant step toward enterprise AI automation, enabling organizations to build intelligent assistants that work…
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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…
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Copilot Studio: Building Custom AI Agents for Enterprise Workflows
Traditional chatbots follow rigid conversation flows. AI agents powered by Copilot Studio understand intent, access knowledge bases, and execute multi-step…
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Building AI Agents with AutoGen and Azure OpenAI
AutoGen enables building sophisticated AI systems that can decompose complex tasks and collaborate to find solutions.
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AI Agent Memory: Implementing Long-Term Context
Long-term memory transforms one-shot interactions into ongoing relationships. Users feel understood when the AI remembers their preferences, past issues…
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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…
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Building AI Agents with Tool Use: A Practical Architecture
At its core, an AI agent follows a simple loop: observe, think, act, repeat. Tools should be focused, well-documented, and handle errors gracefully.
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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…
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Building AI Agents with Semantic Kernel Planners
Planners analyze a user's goal and create an execution plan using available plugins. The Handlebars planner generates a template-based plan, while function…
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Building AI Agents with Azure AI Foundry: A Post-Build 2025 Implementation Guide
The new agent framework introduces several critical improvements. First, native multi-agent orchestration allows agents to collaborate without custom…
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AI Agents 2025: The Year of Autonomous Systems
The age of autonomous AI is here. Let's build responsibly.
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Copilot Agents: Declarative and Custom Engine Approaches
Start with declarative agents for rapid prototyping and move to custom engines when you hit limitations.
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Enterprise Agent Patterns: Security, Governance, and Scale
Enterprise agents require these patterns to ensure security, compliance, and reliable operation at scale. Implement them from the start rather than…
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AI Agent Templates: Quick Start Patterns for Enterprise Use Cases
Templates accelerate agent development by providing battle-tested patterns. Start with a template, customize for your needs, and iterate based on real usage.
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Multi-Agent Frameworks: Orchestrating AI Agent Collaboration
Multi-agent systems unlock sophisticated automation capabilities. Start with simple patterns and evolve complexity as needed.
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Azure AI Agent Service: Building Autonomous AI Agents
Azure AI Agent Service provides the foundation for building sophisticated AI systems that can reason, plan, and act autonomously. Start with simple agents…
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Agent Observability: Monitoring AI Systems in Production
Observability transforms AI agents from black boxes into understandable systems. Combine metrics, logs, and traces to gain complete visibility into agent…
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Audit Logging for AI Agents: Complete Activity Tracking
Comprehensive audit logging is essential for AI agents in production. It enables debugging, ensures compliance, and provides the visibility needed to build…
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Permission Models for AI Agents: Fine-Grained Access Control
Permission models for AI agents must be flexible yet secure. Combine RBAC for broad access patterns with capabilities for fine-grained, time-limited access.
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Sandboxing AI Agents: Isolation and Containment Strategies
Sandboxing is your last line of defense. Even trusted agents can behave unexpectedly - proper isolation ensures that unexpected behavior doesn't become a…
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Agentic AI Capabilities: From Chatbots to Autonomous Agents
Agentic capabilities transform AI from a question-answering system into an autonomous problem-solver. The key is combining planning, tools, memory, and…
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Claude Computer Use Preview: AI That Can Control Your Desktop
Computer Use is a significant step toward truly autonomous AI agents. Use it responsibly with appropriate safety controls.
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Agent Reliability Patterns: Building Robust AI Systems
Building reliable agents requires investment in error handling, recovery mechanisms, and observability. These patterns form the foundation for production AI…
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AI Agent Best Practices: Lessons from Production
AI Agent Best Practices: Lessons from Production
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Multi-Agent Architectures: Collaboration at Scale
Single agents have limits. Multi-agent systems multiply capabilities. Today I'm exploring architectures for agent collaboration.
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Memory and State Management in AI Agents
AI agents need memory to maintain context across interactions. Today I'm exploring how to implement effective memory systems.
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File Handling in AI Agents
AI agents often need to work with files - reading documents, processing data, generating outputs. Today I'm exploring safe and effective file handling patterns.
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Code Execution in AI Agents: Safe and Effective Patterns
Code execution is one of the most powerful capabilities for AI agents - and one of the most dangerous. Today I'm exploring how to implement it safely.
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Tool Use Patterns for AI Agents
Tools extend what AI agents can do beyond text generation. Today I'm exploring patterns for effective tool use in production agents.
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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…
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Building AI Agents: From Simple to Sophisticated
AI agents are systems that can take actions autonomously to achieve goals. Today I'm exploring how to build agents from simple tool-using assistants to…
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Azure AI Agent Service Preview: Building Intelligent Agents
Azure AI Agent Service provides: Managed agent runtime No infrastructure to manage Built in tools Code execution, file handling, web search Memory…
2 min read
AI Agent Frameworks Comparison: LangChain, AutoGen, CrewAI, and More
I've built prototypes on LangChain, AutoGen, and several newcomers; each choice has trade-offs. This comparison focuses on practical differences that…
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CrewAI Introduction: Role-Based Multi-Agent Framework
I started experimenting with CrewAI because I wanted clearer role definitions in multi-agent workflows. CrewAI's focus on roles, goals, and agent…
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AutoGen Introduction: Microsoft's Multi-Agent Framework
I've used AutoGen to prototype multi-agent workflows that handle planning, tool use, and execution. This practical introduction focuses on the patterns that…
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Multi-Agent Systems: Orchestrating Specialized AI Agents
Complex enterprise tasks benefit from specialization. In my work, coordinating small specialist agents led to clearer reasoning, easier testing, and more…
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Agentic RAG Patterns: Self-Correcting and Adaptive Retrieval
When RAG systems can reason about what to retrieve, they stop failing silently. My implementations of agentic RAG show how to add evaluation and iterative…
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OpenAI Assistants API: Production Patterns and Best Practices
I started building with the Assistants API in late 2023. In production it rewarded strict state management, clear tool contracts, and thoughtful thread…
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Azure OpenAI Assistants API: Enterprise AI Agents
The Azure OpenAI Assistants API — launched in preview on Azure shortly after the OpenAI DevDay announcement in November 2023 — is the stateful AI agent…
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Tool Use Patterns for AI Agents: Effective Function Design
The most consequential design decision in a function-calling agent isn't the model you choose — it's how you write the tool definitions. The model selects…
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Building AI Agents: From Chatbots to Autonomous Assistants
Function calling, released last week with the 0613 model versions, gives AI agents a proper foundation — and I've been rebuilding some agent prototypes to…
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Azure OpenAI Function Calling Patterns: Building Intelligent Agents
Function calling patterns enable sophisticated AI agents. Tomorrow, I will cover building AI agents in more depth.