Traditional software systems operate reactively: they wait for explicit user input, route it through predetermined pipelines, and return a fixed output. This model works well for well-defined, single-step tasks, but breaks down whenever a problem requires autonomous decision-making, multi-step decomposition, or adaptive behavior in the face of uncertainty.
An AI agent fundamentally redefines this contract. Rather than waiting to be told exactly what to do at each step, an agent is an autonomous entity that perceives its environment through observations, reasons about goals and constraints using an underlying language model or reasoning engine, and takes sequential actions to achieve objectives without continuous human intervention.
Agentic workflows extend this concept further by orchestrating multiple agents or repeated agent iterations to tackle problems of greater complexity. These workflows enable capabilities such as tool use, memory management, error recovery, and dynamic planning, allowing systems to coordinate across components in ways a single prompt-response cycle cannot support.
The motivation for this architecture is grounded in the nature of real-world tasks. Problems such as customer support resolution, data analysis pipelines, software debugging, and scientific discovery all require breaking down ambiguous requests into concrete steps, interacting with external systems—APIs, databases, code execution environments—handling failures gracefully, and iterating until a satisfactory result is achieved. Without agents, each of these scenarios demands either bespoke integration code or human oversight at every step.
With agents, a single prompt and a framework can enable a system to autonomously navigate a problem space, try multiple approaches, and converge on solutions. This shift—from stateless request-response to stateful, iterative, goal-driven execution—represents a fundamental evolution in how we architect AI systems.