Hello Firebase Studio Team and Community,
We are writing to report a critical bug we’ve discovered while developing with an integrated AI agent (Gemini) in Firebase Studio. This issue fundamentally impacts the reliability of AI-assisted development on the platform.
We have scientifically proven that there is a significant synchronization latency between an AI agent’s write_file operation and the consolidation of that write to the disk.
When the AI agent performs an Update on an existing file, an immediate Read operation on that same file returns the old, cached content, not the newly written content. The correct content is only readable after a significant and unpredictable delay (we’ve measured up to 5 seconds).
As our project’s blueprint.md file notes:
Cause of the Problem: There is a synchronization latency between write operations in memory and their consolidation on the disk. An immediate read after a write can return stale data. Furthermore, the Update capability of files is proven to be unreliable.
This creates a severe race condition with a gigantic impact:
-
Broken Self-Verification: It is impossible for an AI agent to reliably verify its own work. The agent writes a file, reads it back immediately to confirm success, sees the old content, and incorrectly concludes the operation failed.
-
State Corruption: This leads to the AI entering error-correction loops, attempting to “fix” writes that were actually successful but not yet synced, leading to file state corruption.
-
Unreliable Workflow: The entire AI-assisted workflow becomes unstable and untrustworthy, requiring constant manual intervention from the developer.
Our internal protocol had to be updated to forbid the AI from performing Update operations, forcing us into a manual workflow, as documented in our blueprint.md:
Executor AI Responsibilities:
Never update a file directly due to platform bugs.
Always follow the manual contingency protocol for updates.
This can be reproduced by any process that programmatically performs a fast sequence of write → read on the same file within the Firebase Studio environment.
-
Create a file:
write_file('test.txt', 'Initial content.') -
Verify creation:
read_file('test.txt')→ Correctly returns “Initial content.” -
Update the file:
write_file('test.txt', 'Updated content.') -
IMMEDIATELY read the file:
read_file('test.txt')→ Incorrectly returns “Initial content.” -
Wait 5 seconds and read again:
read_file('test.txt')→ Finally returns “Updated content.”
The underlying file system needs to ensure that a write_file operation is atomic and that its result is immediately visible to subsequent read_file operations from the same process. Without this I/O consistency, the reliability of any programmatic file manipulation is fundamentally compromised.
This is a massive issue for the future of AI-powered development on this platform. We hope this detailed report helps the engineering team investigate and resolve it.
Thank you.