Key Takeaways
- Artificial intelligence in software products has rapidly evolved from working behind the scenes to enabling direct user interaction.
- Key phases include AI models powering features indirectly, chat interfaces like ChatGPT, context-enhanced systems (RAG), and AI “agents” that use tools to perform tasks.
- Recent developments include agents working autonomously in the background, managing multiple processes.
- Future possibilities involve AI agents collaborating directly with each other.
- Software engineers and related fields have been key early adopters, pushing the boundaries of AI application.
Artificial intelligence has been part of software for quite some time, but the way we interact with AI products has dramatically changed over the last few years.
Initially, powerful AI models mostly worked behind the scenes. Think of how Google Translate improved translations or YouTube fine-tuned video recommendations using deep learning years ago. The AI was crucial but invisible to the user.
The launch of ChatGPT marked a major shift. Suddenly, millions were directly interacting with an AI model through a chat interface. This brought AI from the background right into the foreground.
Developers quickly learned that AI models perform better when given more context. This led to retrieval-augmented generation (RAG), where AI systems fetch relevant information—like searching the web or specific databases—before responding. As detailed on lukew.com, many systems, including ChatGPT itself, now use retrieval to enhance their answers.
The next step gave AI access to tools. Instead of just providing information, AI models could now perform actions: fact-checking, analyzing data, creating presentation slides, or generating images. These are often called AI agents.
In these “agentic” systems, users give instructions, and the AI plans the steps, selects the right tools, and executes the task. Users can guide the process, but the AI handles much of the decision-making.
Data from AI labs like Anthropic confirms that computer and math professionals have embraced these tools most readily, incorporating AI like Claude into daily tasks. According to the original source, this rapid adoption is mirrored in AI coding tools like Augment, which quickly evolved from code suggestions to chat and then to agent capabilities.
As users become more comfortable, constantly watching AI agents work becomes tedious. This has led to systems where multiple AI processes run autonomously in the background, notifying the user only when necessary.
So, what comes after background agents? The emerging idea is for AI agents to start interacting and collaborating directly with each other. Google has even announced protocols to facilitate this kind of multi-agent communication.
It’s clear the evolution of AI products isn’t slowing down. This journey from hidden helpers to potential collaborators represents where we are now, but the landscape is changing fast, prompting ongoing updates and discoveries in the field.
