9 posts about agent architecture by 6 contributors.
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Learn how to build an AI agent API with ASP.NET Core and Azure OpenAI, including architecture, tool integration, authentication, and enterprise deployment preparation.
AI agents become useful when they can do more than generate text. A production agent often needs to retrieve information, call business APIs, access databases, work with documents, or invoke specialized tools. This creates an architecture problem: how should an AI model connect to those capabilities
Choosing between LangChain and LangGraph in 2026: Understand DAGs vs. cyclic graphs for AI app architecture.
Learn what OpenClaw is and how to use it to build AI agents that reason, select tools, and execute real-world workflows using structured architectures and proven patterns.
Build reliable AI agents with robust memory! Learn to design long-running workflows that avoid forgetting, drifting, and hallucinating. State-as-data is key!
GSCP-15: A reference architecture for building reliable, scalable AI agents. Learn how to move beyond demos to production-ready systems with contracts, state, and verification.
A long, detailed, SEO- and GEO-optimized guide explaining AI agent frameworks, runtimes, and harnesses, including architecture, workflows, diagrams, and practical examples.
Learn how AI Agents operate internally. Understand their architecture, planning loops, memory systems, reasoning processes, tool use, and execution workflow. A detailed guide for developers building the next generation of autonomous software.
Explore the evolution of Large Language Models (LLMs) into smart agents through architectural shifts and the integration of GSCP-12. Learn how GSCP-12 provides a governance-driven framework for task decomposition, alignment, and tool use, enabling safe, auditable, and enterprise-grade AI agents. Discover the key architectural layers required to transform LLMs into goal-directed, auditable systems.
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