Creating a shared memory system for AI agents to streamline workflows and reduce redundancy.
When using multiple AI agents like Codex and Claude, users often face the challenge of starting over after reaching usage limits. This leads to inefficiencies as context needs to be re-established repeatedly. A solution is needed to maintain a common context without inflating token usage.
Determine which AI agents will be used in the workflow, such as Codex and Claude, and understand their limitations.
Develop a method to maintain a common context, initially using a simple list or markdown file to store instructions and relevant information.
Utilize a Retrieval-Augmented Generation (RAG) approach to efficiently load only the necessary context, preventing excessive token usage.
Plan, execute and review a cross-cutting refactor in small batches, with Claude Code making the edits and GitHub Copilot reviewing each PR.
Claude Code reads your coverage report, picks the riskiest untested modules and writes unit tests that pass, one small PR at a time.
Copilot and Claude review each PR for bugs, missing tests and risky changes before a human looks, so reviewers can focus on design.