Utilizing built-in Python kernels or JITs can streamline agent implementation, especially with Prime as a guide.
Developers often face challenges when integrating agents with programming languages. The complexity of setting up environments and ensuring compatibility can hinder progress. However, leveraging built-in Python kernels or JITs simplifies this process significantly, especially when using established references like Prime.
Begin by preparing your development environment with the necessary tools and libraries for Python and your chosen JIT.
Utilize the built-in Python kernel or your selected JIT, referencing Prime for guidance on implementation.
Run tests to ensure that the agent integrates smoothly with the Python kernel, checking for any compatibility issues.
Analyze performance metrics and optimize the setup to enhance the efficiency of the agent's operations.
Plan, execute and review a cross-cutting refactor in small batches, with Claude Code making the edits and GitHub Copilot reviewing each PR.
Copilot and Claude review each PR for bugs, missing tests and risky changes before a human looks, so reviewers can focus on design.
Claude Code reads your coverage report, picks the riskiest untested modules and writes unit tests that pass, one small PR at a time.