AI agent frameworks are structured software environments that provide necessary components and runtime logic to build, manage, and operate autonomous agents. These frameworks enable scalable, autonomous systems through structured reasoning, memory, and tool use, allowing developers to compose intelligent behaviors by integrating large language models, APIs, vector stores, planning logic, and feedback systems within a consistent control loop. The architectural foundation of AI agent frameworks includes layers such as perception, memory module, cognitive/reasoning engine, action executor, learning mechanism, and communication protocols. Modern frameworks offer customization, extensibility, safety and reliability mechanisms, feedback and tuning support, integration with external tools, role-based access control, event triggers and scheduling. The most widely used frameworks can be categorized into conversational agent frameworks, workflow automation agent frameworks, multi-agent system (MAS) frameworks, reinforcement learning (RL) agent frameworks, hybrid and specialized frameworks. Real-world use cases of AI agent frameworks include banking, healthcare, retail, IT operations, sales & marketing, enterprise knowledge. To ensure production readiness, implement best practices such as starting modular, defining task boundaries clearly, prioritizing observability, validating rigorously, securing tool access, iterating with feedback, testing in sandboxed environments, using guardrails where necessary.