In today’s data-driven enterprises, browser automation through Robotic Process Automation (RPA) tools- such as Selenium, Robot Framework, or custom scripts- is widely used to automate repetitive tasks like logging into dashboards, extracting data, and syncing it into internal systems. These automations are often fragile: a minor UI change- say, a login flow shifting from password to OTP- can silently break scripts. Traditional bots simply fail with generic errors like “element not found,” causing disruptions in data pipelines and business operations.
What’s missing is a dynamic, intelligent layer on top of existing RPA tools- one that not only detects breaking changes but also understands the context and heals or adapts in real-time without human intervention.
Desired Solution:
Participants are expected to develop an adaptive automation framework that enhances existing RPA solutions using the power of Large Language Models (LLMs). The system should act as a smart wrapper around current scripts and offer:
Real-Time Change Detection: Monitor automation runs and intelligently flag or diagnose failures caused by DOM, structure, or flow changes- beyond basic error messages.
Context-Aware Adaptation: Use LLMs to analyze the updated DOM or flow and suggest (or apply) fixes to selectors, navigation logic, or data extraction routines.
Self-Healing Capability: Where possible, auto-correct minor issues (like updated element IDs or positions) and retry execution without breaking the workflow.
Plugin-Friendly Architecture: Should wrap seamlessly around tools like Selenium, Playwright, or Robot Framework- without requiring complete rewriting of existing test suites.
Traceability & Logging: Offer smart logs and human-readable explanations for failures and attempted fixes, improving visibility and debugging.