AI Agents
Deep Dive: Hierarchical Agent Systems — Supervisors, Workers, Delegation, and Quality Control
The single ReAct agent excels at single-domain tasks. Multi-agent systems extend this to multi-domain work by coordinating specialized agents. But there’s a specific multi-agent topology that deserves its own deep dive: the hierarchical supervisor pattern — where a supervisor agent...
Read more →Deep Dive: Multi-Agent Systems — Architectures, Coordination Patterns, Best Practices, and Pitfalls
The single ReAct agent handles an impressive range of tasks — but it has a ceiling. When a task spans multiple domains, requires different expertise for different phases, or benefits from verification and review, a single LLM with a single...
Read more →Deep Dive: The Single ReAct Agent — Architecture, Best Practices, and Pitfalls
The single ReAct agent is the most fundamental — and most widely deployed — agent architecture in production today. It places one LLM in a reasoning loop with access to tools, iterating through Thought → Action → Observation cycles until...
Read more →AI Agents Best Practices: Building Reliable, Safe, and Effective Agent Systems
AI agents — systems that use an LLM to reason, plan, and act in a loop — are among the most powerful patterns in modern AI engineering. They are also among the most fragile. A well-designed agent can autonomously resolve...
Read more →AI Agents: Autonomous Systems That Reason, Plan, and Act
Large Language Models are impressive text generators, but on their own they are stateless, passive, and confined to the information in their context window. An AI agent breaks all three constraints: it perceives its environment, reasons about a goal, selects...
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