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Machine Learning

Agent JIT Compilation for Latency-Optimizing Web Agent Planning and Scheduling

Caleb Winston, Ron Yifeng Wang, Azalia Mirhoseini, Christos Kozyrakis

Featured June 2, 2026

Original Paper

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Simply

Instead of slow, step-by-step thinking, this system quickly turns what you want done on the web into smart, optimized code, making computer helpers much faster and more reliable.

In depth
The paper introduces agent just-in-time (JIT) compilation to drastically improve the efficiency and reliability of web automation. Instead of relying on slow, sequential Large Language Model (LLM) calls for every action, the system compiles natural language tasks into optimized executable code plans. This approach leverages an invariant-enforcing tool protocol for static correctness checks, a cost-optimizing planner to select the most efficient code, and a cost-aware scheduler to dynamically choose optimal parallelization strategies.

Key Takeaways

  • 1
    The system compiles natural language tasks into optimized executable code, drastically reducing reliance on per-step LLM inference and achieving up to 10.4x speedup over traditional Browser-Use agents.
  • 2
    An invariant-enforcing tool protocol ensures plan correctness by defining preconditions and postconditions for tool use, significantly improving planning accuracy and reducing error rates.
  • 3
    A cost-optimizing planner and cost-aware scheduler dynamically select the most efficient code plans and execution strategies (serial, parallel, or hedging) based on estimated costs and learned latency distributions.

Conceptual Flow

HIGH LEVEL
1
Methodology: How was it done?

The system takes your request, turns it into smart code, and then runs that code super fast on the internet.

Your Request
Turn into Smart Code
Fast Web Actions
2
Results: What did they find?

The new way is much faster and makes fewer mistakes than the old way of doing things.

Old Way (Slow, Errors)
Improved By
New Way (Fast, Accurate)