In recent years, two of the most exciting advancements in AI have been generative AI and large language models (LLMs). However, the frontier is rapidly expanding with the emergence of AI agents and agentic AI, which represent a significant step towards more autonomous and capable AI systems.

• Generative AI: This is a type of AI that doesn’t just analyze data; it creates new content. Think of it as an AI artist, writer, or even coder. Generative AI learns the patterns and structures within vast amounts of data (text, images, code, and more.) and then uses that knowledge to produce entirely new, original content based on prompts. Tools like DALL-E for images and ChatGPT for text are prime examples.

• Large Language Models (LLMs): These are the engines powering many of today’s most sophisticated AI applications, especially in text-based tasks. LLMs are large AI models trained on massive datasets of text and code. They excel at understanding, generating, and manipulating human language. Because they’ve processed so much information, they can answer complex questions, summarize documents, translate languages, write creative content, and even generate computer code. These models are becoming increasingly capable, even developing “emergent abilities” like solving math problems and writing code, though it’s always wise for developers to review and validate AI-generated code. LLMs are also becoming multimodal, meaning they can understand and process not just text, but also images, audio, and video.

• AI agents: These are AI systems designed to perceive their environment, make decisions, and take actions to achieve specific goals. Unlike a simple chatbot that responds to a direct command, an AI agent can:
• Plan: Break down a complex goal into a series of smaller, manageable steps
• Reason: Use its knowledge and understanding to make decisions at each step
• Act: Interact with digital or even physical environments (through APIs or robotic interfaces) to carry out its plan
• Learn/Adapt: Potentially learn from its experiences to improve its performance over time
• Agentic AI: This refers to the capability of AI systems to operate autonomously in the manner described above.

For software developers, this may be particularly interesting because AI agents can be programmed to interact with software development tools, APIs, and even existing codebases. This opens up possibilities for AI to assist in more complex development tasks, such as automatically testing new features, refactoring large sections of code, or even managing project workflows. The ongoing research is focused on making these agents more reliable, efficient, and safe as they gain more autonomy.