Savyre

@Tahir. Thanks for the question. Yes, all stage context lives in the stage files. If the developer switches AI agents, the next agent still has full context by reading those files (ai-output.md, final.md, review, etc.). Nothing depends on a single agent’s chat history.

@AidenPearce_1ef : Every stage in Savyre produces a versioned artifact that becomes the input to the next stage. If a test fails, you can trace it back through the test plan, implementation, design and even the original requirements to identify exactly where the decision originated, instead of debugging only the final code.

@FinleyCarter_363 : That's exactly one of the problems we built Savyre to solve. Instead of relying on individual review habits, every developer follows the same structured review workflow with stage-specific artifacts and checkpoints, making AI-assisted development much more consistent across teams.

@Kassidie : Thanks! We faced the same issues within our dev team. Built Savyre to address this issue. Savyre preserves context throughout the workflow, so requirements, codebase discovery, design decisions and reviews stay connected. That means less time rebuilding context every time you switch tasks or revisit a feature. Do try it and help to provide valuable feedback.

@Freya_Jensen : Thanks, that's a great suggestion. Since every stage is stored as an artifact, you can revisit earlier decisions without relying on chat history. We're also exploring making navigation across previous stages even smoother while keeping the full decision trail intact.

@mattias_blomqvist : Thanks for highlighting that. Security and developer trust were major design goals for Savyre. Keeping repositories local while providing a structured AI workflow lets teams adopt AI without compromising control over their source code.

Hi DevHunt community, We built Savyre AI Coding Workflow because AI can generate code quickly, but teams still need clarity around requirements, codebase understanding, testing, review and ownership. Savyre guides developers through 14 connected, reviewable stages inside VS Code and Cursor. Each validated output becomes the input for the next stage, helping preserve context and improve traceability. We’d love honest feedback from developers and engineering teams: **Which part of AI-assisted coding is hardest for you to review or trust today?** A 14-day free trial is available for anyone who would like to test it on a real or sample project. Demo URL: https://www.youtube.com/watch?v=-StqPB_TnwI Visit us at: https://savyre.com/products/ai-coding-workflow

Thanks @Anders. I’d actually start with small bug fixes or refactoring tasks. They’re easier to validate and let the team get comfortable with the workflow without adding too much process. Once it becomes natural, moving to larger features makes much more sense.

Thanks @Natalie. The core Savyre workflow is the same across Cursor and VS Code - same stages, artifacts, checkpoints and traceability. The main difference is how you interact with the AI capabilities available in each IDE. We intentionally designed the workflow so teams aren’t locked into a particular AI coding tool.

Thanks @Viktor, and glad the stage prompts helped! Yes, making the workflow adaptable to team-specific coding, architecture and review guidelines is something we’re working towards. The idea is to keep the core structure consistent while letting teams bring in their own engineering standards.

@Zain Sheikh : Great question. Impact Analysis is meant to go beyond just the files that were changed. It uses the codebase context to look for dependencies and other areas that could be affected by the change, the goal is to catch unintended impact before the code is submitted.

Exactly, Klara. We don’t want the reasoning behind a change to disappear into an AI chat. Keeping the requirements, design decisions, reviews and impact analysis as artifacts makes it much easier for someone to understand why something was done even months later.

Thanks @Johan. That’s exactly why we wanted the trial to work on real repos rather than a controlled demo. Would love to hear what works, what feels unnecessary, and where the workflow gets in your way. That feedback is genuinely useful for us.

Thanks @Brandon. That’s exactly the idea. Since each stage builds on the approved output of the previous stages, the reasoning and context don’t disappear into chat history. When requirements change, developers can revisit the relevant decisions and understand what downstream work may need to change instead of starting from scratch.

Great question, @Oskar. Yes, Savyre tracks token usage at each stage, including estimated tokens usage and the approximate cost. This makes it easier to see where AI usage is highest and identify stages where context or prompts could be optimized.

Great question Evie. Tech leads don’t need to start with a blank rubric. Savyre provides a structured evaluation framework covering areas like requirement understanding, code quality, testing, review quality and AI over-reliance. The goal is to make AI-assisted coding quality measurable and consistent across developers, while still allowing teams to adapt the criteria to their own standards.

Thanks @Ivan. We provide ready-to-use, role-specific assessments, so hiring managers don’t need to build everything from scratch. They can pick the relevant role and assessment and customize it based on their hiring needs, which makes setup fairly quick.