Scientific automation often faces a critical "robustness gap": traditional linear scripts are brittle and crash silently when encountering the messiness of real-world biological data. This poster presents a Self-Correcting Protein Analysis Agent that bridges this gap by replacing fragile pipelines with a resilient, cyclic agentic architecture. Rather than simply executing a sequence of steps, this system acts as an autonomous research assistant capable of reasoning, validating, and repairing its work.
We focus on the architectural shift from "happy path" automation to State-Aware Cyclic Graphs. Attendees will visualize how the agent manages complex context and employs a "self-healing" loop: when validation checks fail (e.g., detecting geometric errors in a molecule), the agent doesn't crash. Instead, it autonomously diagnoses the issue, selects a refinement strategy (like adjusting chemical bonds), and re-evaluates the result, mimicking the iterative trial-and-error process of a human scientist.
Finally, we demonstrate how the agent closes the research loop by autonomously authoring comprehensive PDF reports. By combining structured data extraction with synthesis capabilities, the agent produces defensible, publication-ready artifacts. This poster offers a blueprint for developers looking to build robust agentic systems that can interact with the physical world, recover from errors, and deliver tangible scientific results.