Why AI Agents Feel Like the Next Step After RAG

We have been noticing an interesting shift in how teams approach data extraction in life sciences.

For a long time, NER (Named Entity Recognition) was the default. It worked well for pulling out structured elements like drugs, diseases, and outcomes from text. But it starts to break down when you need context, reasoning across documents, or anything that resembles a real workflow.

Then came LLMs, which improved things by enabling better contextual understanding, summarization, and insight generation. RAG helped ground those outputs with external data. Even then, most setups still feel like tools rather than systems. You prompt, you get an answer, and a human stitches everything together.

What we are seeing now is a move toward AI agents. Instead of single-step outputs, these systems can break down tasks, use tools, validate results, and operate across multiple steps. In life sciences, where workflows need to be traceable and reproducible, this shift feels significant.

We are also seeing the idea of an agent factory gaining traction. Instead of building one-off AI solutions, teams are creating reusable, orchestrated components that can plug into different workflows.

Curious if others here are seeing a similar pattern. Are you still working mostly with LLM and RAG setups, or starting to experiment with agent-based workflows?

For broad insight on this: https://capestart.com/technology-blog/new-era-of-data-extraction-in-life-sciences-from-traditional-ner-to-ai-agents/