[Proposal] LLM-Augmented Adaptive Replay Engine for Dynamic Process Generalization

[shephinphilip]

Hey OpenAdapt Team 👋,

I'm very excited about the Machine Learning Engineer role — your vision for adaptive automation and process intelligence aligns strongly with my background.

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🧠 Proposal: LLM-Augmented Adaptive Replay Engine

Goal:

To create an intelligent replay system that uses LLMs to generalize, abstract, and execute workflows across slightly varying UI/app states.

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🔍 Idea Highlights:

1. Process Abstraction with LLMs

   • Extract dynamic process templates from action sequences.
   • Use LLMs to describe high-level intent behind user actions.

2. Semantic Replay Matching

   • Match current UI state with recorded process intent using vector similarity or layout parsing.
   • Adapt actions in real-time to changed layouts or data.

3. Error Recovery via LLMs

   • Use LLMs to handle unexpected UI states and suggest recovery steps.
   • Log, retry, or ask for feedback dynamically.

4. Auto-Documentation

   • Generate human-readable summaries from recordings.

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💡 Implementation Steps:

• Analyze ActionEvent logs & recordings.
• Use structured prompts to query an LLM (e.g., GPT-4, Claude) for intent abstraction.
• Extend or create a new ReplayStrategy that’s LLM-powered.
• Build replay feedback loop for adaptive logic.

Would love your feedback and direction! Ready to prototype and iterate 🚀

Thanks for the opportunity!

Best,
Shephin Philip