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

Researchers introduce agent just-in-time (JIT) compilation, a system that compiles natural language task descriptions directly into executable code for computer-use agents, achieving 10.4x speedup and 28% higher accuracy compared to existing sequential approaches. The method combines planning, scheduling, and tool protocol innovations to reduce latency and errors in browser automation tasks.

The emergence of computer-use agents represents a critical frontier in AI development, where language models automate complex digital workflows. Traditional sequential architectures—where each browser action requires a full LLM round-trip—create bottlenecks that limit practical deployment. Agent JIT compilation addresses this fundamental constraint by shifting from reactive execution to ahead-of-time planning, treating task automation analogously to software compilation.

This work builds on growing recognition that LLM-based automation requires architectural innovation beyond scaling model size. Prior systems like Browser-Use and OpenAI's CUA demonstrated feasibility but suffered from latency and error accumulation. JIT compilation tackles both through code generation validated against tool specifications, reducing hallucinated or invalid commands. The Monte Carlo-based scheduler explores parallelization opportunities, a critical insight since many web tasks have inherent parallelizable steps.

The practical implications extend across industries relying on digital task automation. E-commerce, customer service, data entry, and enterprise automation workflows could achieve dramatic efficiency gains. A 10x speedup transforms agent systems from research curiosities into economically viable alternatives to traditional RPA (Robotic Process Automation) solutions, particularly for tasks requiring reasoning.

Investors and developers should monitor whether these performance improvements generalize beyond the five tested applications. The invariant-enforcing protocol showing 28% accuracy gains suggests the approach addresses real failure modes rather than marginal optimizations. Future work will likely focus on making compilation strategies more generalizable and reducing the overhead of learned latency distribution modeling.

• →JIT compilation achieves 10.4x speedup over Browser-Use by compiling task descriptions into executable code rather than sequential fetch-screenshot-execute loops
• →Accuracy improvements of 28% stem from precondition-postcondition validation that prevents incorrect tool use and hallucinations
• →Monte Carlo-based parallelization scheduling adds 2.4x additional speedup by discovering concurrent execution opportunities in web tasks
• →The approach transforms computer-use agents from latency-bound systems into practical alternatives to traditional RPA automation
• →Invariant-enforcing tool protocols reduce downstream errors by validating agent outputs against formal specifications before execution