An agent, derived from the Latin word agere or agens, implies something capable of producing an effect when authorized by another. In software, agents commonly refer to programs acting on behalf of a user or another computer program. The concept derives from a model of concurrent computation in the 1970s. With the advent of artificial intelligence, agents also exhibit additional properties such as basic reasoning, autonomy and collaboration.
The emergence of software-based automation over the past few decades has occurred alongside advancements in robotics and artificial intelligence. Enterprises have progressively adopted new waves of automation paradigms – from simple scripts and bots to robotic process automation (RPA) and cloud-based automation platforms.
Today, agentic AI — software agents that exhibit autonomy, adaptiveness and reasoning — represents the frontier. Yet real-world adoption remains uneven, as we will discuss later. While certain enterprises pilot small-scale “AI assistant” prototypes, others are grappling with how to orchestrate multiple agents across diverse and complex business processes. The recent emergence of agentic protocol frameworks like model context protocol (MCP) from Anthropic and agent to agent (A2A) from Google are trying to address interoperability and integration functionality, which adds another layer to the AI stack.
