SaaS Migration Budget and AI Agent Hallucination Management
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2026년 7월 13일
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Computing/Software원본 영상을 바탕으로 AI의 도움을 받아 작성했습니다. 원본 영상이 기준입니다.
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원본 영상을 바탕으로 AI의 도움을 받아 작성했습니다. 원본 영상이 기준입니다.
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Attempting to migrate a large codebase all at once is a recipe for disaster. The reason the Bun project ended up with 27,000 lines of unsafe code while porting 530,000 lines to Rust in just 11 days was due to an overly ambitious big-bang approach. Do not entrust the entire project to AI; instead, break it down into independent functional units of 500 lines or less.
First, analyze code dependencies using dependency-cruiser to identify circular references. Next, use repowise to visualize AST and commit history to select components with low coupling. By defining these boundaries, you can reduce the frequency of AI agents generating incorrect code and the subsequent need for repetitive revisions by 40%.
Agents often mix current API specifications with outdated patterns because their context is too loose. Whenever you port a module, provide a YAML file containing the specification and testing requirements as context.
The format is simple. Define functional specifications, type signatures, and technical constraints—such as prohibiting unsafe blocks—within an auth-migration.yaml file. Including this as a reference file when calling the agent significantly reduces hallucinations and enables automated verification from the start.
Who has the time to manually review every line of code generated by AI? Build a loop that automatically executes existing test scenarios using promptfoo. Create a gatekeeper script that only allows code to be merged into the main branch if it passes all tests.
If the build fails, feed the error logs back to the agent so it can fix the issues itself. Implementing this self-healing loop can save more than 60% of manual debugging time.
Reckless migration leads directly to budget explosions. As seen in the Branch8 case, you must actively utilize prompt caching and set token limits. Remember that Claude 4.6 Sonnet costs $3 per 1M tokens.
Configure daily usage limits in ~/.claude/settings.json and set per-module limits using the --token-budget flag when executing the API. It is safer to implement an interception routine in a Python script that forces the process to stop immediately if it exceeds $2 per session.
If migrated code fails in a production environment, it can crash the entire web server. When binding Rust code to Node.js, implement catch_unwind as a bulkhead. Even if a native panic occurs, it will gracefully convert into a JavaScript error object, preventing the server from crashing.
Use tools like Unleash or PostHog to gradually increase the exposure rate of Rust-based modules from 0% to 100%. If an error occurs, having middleware that immediately rolls back to the legacy code will help you maintain availability of 99.9% or higher.