9 min read
Transitions, Not Tasks: Designing AI for the Gap Between Meetings
Most AI assistants help inside a task. The expensive part of knowledge work is the switch between commitments, and that needs a different agent design.
Agents
29 articles
9 min read
Azure AI Foundry Is Now Microsoft Foundry: Hubs, Projects, Choices
What the Microsoft Foundry rename means in early 2026: Foundry projects vs hubs, the preview portal, prompt flow's place, and how I'd decide.
6 min read
Microsoft Agent Framework in .NET: A February 2026 Preview Field Guide
What Microsoft Agent Framework for .NET really is in February 2026: agents, sessions, tool approvals and handoffs, plus where it fits before it reaches GA.
9 min read
AI Agents in 2026: Where They Earn Their Keep and Where They Don't
A practitioner's reality check on AI agents going into 2026: the use cases that work, where they break, and how to pick a framework without the hype.
1 min read
2025 AI Year in Review: The Year Agents Became Real
The most significant shift this year was the maturation of autonomous AI agents. What started as experimental frameworks evolved into production-ready…
1 min read
Agentic Workflows: Designing Autonomous AI Systems
Safe agentic workflows balance autonomy with appropriate controls.
1 min read
Tool Orchestration: Managing Complex Tool Interactions
Robust tool orchestration enables complex, reliable AI workflows.
1 min read
Agent Memory Systems: Building Persistent AI Memory
Sophisticated memory systems enable agents to learn and improve over time.
1 min read
Advanced Agent Patterns: Complex Multi-Agent Systems
Choose the right execution pattern based on task complexity and agent capabilities.
1 min read
Azure AI Foundry Agents: Building Production AI Agents
Azure AI Foundry simplifies the path from agent prototype to production deployment.
1 min read
AI Orchestration Frameworks: Building Complex AI Workflows
Choose orchestration frameworks based on workflow complexity and team expertise.
1 min read
Agent Safety Patterns: Building Trustworthy AI Systems
Safety in AI agents is not optional - it's foundational. Build safety in from the start, and your agents will be both powerful and trustworthy.
1 min read
Memory Consolidation for AI Agents: From Short-Term to Long-Term
1. Set appropriate thresholds : Not everything needs to be remembered 2. Consolidate regularly : Don't let short term memory overflow 3. Abstract…
1 min read
Procedural Memory for AI Agents: Learning How To Do Things
Procedural memory captures: Action sequences : Steps to accomplish tasks Conditions : When procedures apply Parameters : Variables in the process Success…
1 min read
Semantic Memory for AI Agents: Knowledge Representation
Aspect Semantic Memory Episodic Memory Content Facts, concepts, relationships Events, experiences Context Context free Time and place specific Example…
1 min read
Episodic Memory for AI Agents: Learning from Experiences
Unlike semantic memory (facts) or procedural memory (how to), episodic memory captures: Events : What happened Context : When and where Outcomes : Results…
1 min read
Long-Term Agent Memory: Persistent Knowledge Storage
1. Categorize memories : Different types need different handling 2. Extract automatically : Don't rely on explicit save commands 3. Maintain regularly :…
2 min read
Short-Term Agent Memory: Context Window Management
Sounds like a lot, but it fills up quickly with conversation history, system prompts, tool outputs, and retrieved documents.
1 min read
Agent Memory Patterns: Beyond Simple Context
Periodically consolidate memories for efficiency: 1. Layer your memory : Different types for different purposes 2. Manage capacity : Always have eviction…
1 min read
Human-in-the-Loop AI Agents: Building Trust Through Oversight
Irreversible actions : Deletions, payments, deployments High cost operations : Expensive API calls, resource provisioning Sensitive data : PII handling,…
1 min read
Cycles in Agent Graphs: Iterative Refinement Patterns
Real tasks often require iteration: Refinement : Improve output quality through multiple passes Retry : Handle failures with backoff strategies Search :…
1 min read
Conditional Edges in LangGraph: Dynamic Agent Flows
Route based on multiple state attributes: Sometimes you don't know all routes ahead of time: Multiple decision points in sequence: Route with randomization…
1 min read
Stateful AI Agents: Managing Context and Memory
All require state management beyond simple request-response.
1 min read
Graph-Based AI Agents: Design Patterns and Architecture
Think of your agent as a state machine: Nodes : Processing steps (functions that transform state) Edges : Transitions between steps State : Accumulated…
1 min read
LangGraph Introduction: Building Stateful AI Agents
LangGraph provides these capabilities through a graph-based execution model.
1 min read
Building AI Agents: From Chatbots to Autonomous Systems
AI agents represent the next evolution of AI systems. By combining planning, tool use, and memory, they can autonomously accomplish complex goals while…
1 min read
Self-Hosted Agents in Azure DevOps
Self-hosted agents provide flexibility for specialized build requirements and secure access to internal resources.
1 min read
Azure DevOps Agent Pools
Agent pools provide flexible compute resources for diverse build requirements.
1 min read
Azure Monitor Agent: The Future of Azure Monitoring
Azure Monitor Agent provides a unified, secure, and flexible foundation for all monitoring needs.