Connecting AI agents to systems is crucial for their full potential, enabling them to interact with external software ecosystems and become true collaborators. The Model Context Protocol (MCP) offers a standardized approach to system integration, providing a client-server architecture where AI agents act as clients and connect to MCP servers that expose system capabilities through a common interface. This abstracts system access behind a shared protocol, unlocking reusability, consistency, and security across the agent ecosystem. In addition to MCP, other building blocks for system awareness include REST APIs, direct database access, message queues, and agent orchestration frameworks. These strategies define what AI agents can do, from simple API calls to complex workflows involving multiple systems. Security and reliability are non-negotiable in production, requiring proper authentication, access control, circuit breakers, rate limiting, retry logic, and performance optimization techniques like connection pooling, smart caching, and parallel execution. As the ecosystem matures, expect greater standardization, more powerful frameworks, better observability tools, and a focus on design for flexibility, security, and modularity.