The evolution of software agents
The journey from simple automated systems to intelligent, autonomous, and goal-directed software agents reflects decades of evolution in computer science, artificial intelligence, and distributed systems.
This evolution was followed by the rise of machine learning, which shifted the paradigm from handcrafted rules to statistical pattern recognition. These systems could learn from data and enabled advances in perception, classification, and decision-making.
Large language models (LLMs) represent a convergence of scale, architecture, and unsupervised learning. LLMs can reason, generate, and adapt tasks with little or no task-specific training. By combining LLMs with scalable cloud-native infrastructure and composable architectures, we are now achieving the full vision of agentic AI: intelligent software agents that can operate with autonomy, context-awareness, and adaptability at enterprise scale.
This section explores the history of software agents from foundational theory to modern practice, as illustrated in the following diagram. It highlights the convergence of distributed artificial intelligence (DAI) and transformer-based generative AI, and identifies the key milestones that have shaped the emergence of agentic AI.
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