The field of AI is shifting from simple assistants to agentic systems — tools that can independently form multi-step plans, execute tasks, and even coordinate with other agents. Instead of just responding to prompts, these agents can read codebases, plan changes across multiple files, run tests, and iteratively fix bugs on their own. In enterprise settings, teams of specialized agents are orchestrating complex workflows: one handles code review, another generates tests, a third manages security, and they all work together. Protocols like Model Context Protocol (MCP) and Agent2Agent (A2A) are standardizing how these agents interact and share data, paving the way for more sophisticated automation.
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