7 Core Components of an AI Agent Architecture Explained

This post details the seven core components of an AI agent architecture: goal definition, perception and input processing, memory systems, reasoning and planning, tool execution and action, orchestration and coordination, and feedback and observability. It emphasizes enterprise governance, security, and auditability, highlighting how each component contributes to building scalable and trustworthy AI systems. Specific technical considerations include input sanitization, multi-modal awareness, hybrid memory models, various reasoning strategies (rule-based, chain-of-thought, tree-of-thought, Monte Carlo), action sandboxing, and observability mechanisms for agent performance and cost. The post also references Glean's existing capabilities in unifying perception pipelines, offering context-aware access to enterprise knowledge, and ensuring consistent control when agents act across systems.