Grounded Extraction Agents
Experiments in multi-step extraction workflows that combine tool-calling, structured outputs, and evidence checks to make extraction more reliable than one-shot prompting.
Grounded Extraction Agents explores a narrower and more reliable version of agentic AI: systems that extract structured information through explicit intermediate steps instead of relying on a single prompt to do everything at once.
The main idea is that extraction gets stronger when the workflow is decomposed. Rather than asking for a final answer immediately, the system first identifies candidate evidence, then applies extraction logic, then validates whether the output is actually supported by the source. That creates a more inspectable path from raw input to structured result.
What interested me here was not “agentic” behavior as a trend. It was whether multi-step orchestration could reduce ambiguity and make extraction easier to debug. In practice, the value came from clearer boundaries: tool use for evidence retrieval, constrained output formats for consistency, and explicit checks for whether the extracted claim could be grounded in the available context.
This kind of workflow is especially useful when the source material is messy and when the cost of confident but weakly supported output is high. The experiment reinforced a broader lesson: agentic systems become more credible when they are designed around verification, not just autonomy.