If you've spent any serious time with Antigravity, you've probably lived through this: the AI generates code, a build error appears, you hit Retry, the same error comes back, you hit Retry again—and again—until you've lost twenty minutes doing nothing but hoping the outcome will be different.
Retry is one of Antigravity's most useful features, but it's also one of the most misused. The difference between developers who get unstuck quickly and those who stay stuck isn't just technical skill—it's knowing when retrying is the right move and when it's a waste of time. Here's how to tell them apart.
When Retry Actually Works
Let's start with the legitimate cases, because Retry genuinely earns its place for certain types of failures.
Transient external errors are where Retry shines. If the Gemini API throws a timeout or a 429 rate limit error, a simple retry almost always resolves it. Network blips, temporary service degradation, connection timeouts—these are non-deterministic failures where the same request is likely to succeed on the next attempt. Look for these keywords in your error output:
rate limit/429timeout/ETIMEDOUTnetwork errortemporarily unavailableservice unavailable/503
If you see these, Retry first. It will often be enough.
Non-deterministic AI output is another strong case. Because language models don't produce identical outputs on each run, sometimes a retry genuinely generates a better solution. If the previous attempt was syntactically valid but logically off, a different generation might approach the problem from a more useful angle. This is especially true for complex refactoring tasks where there are multiple valid paths to a solution.
Resource contention also responds well to retrying with a short delay. Port conflicts (EADDRINUSE), file lock issues, or race conditions during startup are timing problems. Give the system a moment to settle, then try again.
# Example: temporary port conflict
Error: listen EADDRINUSE: address already in use :::3000
# A quick check before retrying blindly
lsof -i :3000 | grep LISTEN
# Kill the conflicting process, then retry
kill -9 $(lsof -t -i :3000)The key insight here is that all of these cases share one characteristic: the failure is caused by something outside the code itself. When the problem lives in the code, no external retry will fix it.
Signs That Retrying Won't Help
The problem isn't knowing when Retry works—it's recognizing the situations where it can't possibly work, no matter how many times you press it.
Identical error on two consecutive retries is the clearest signal. If the same error message appears twice in a row with no variation, the underlying conditions haven't changed. A third retry is almost never going to help. Same input, same model state, same context—expecting a different result is the definition of wasted effort.
Errors that describe logical or structural problems are inherently immune to retry:
// These errors don't get better with retry
Type 'string' is not assignable to type 'number'.
Cannot find module './userService' or its corresponding type declarations.
Property 'email' does not exist on type 'User'.
Object literal may only specify known properties, and 'userId' does not exist in type 'SessionPayload'.Type errors, missing module errors, and property access errors reflect concrete problems in how the code is structured. The AI can try generating a fix again, but if it doesn't have accurate information about your type definitions or project structure, it will make the same wrong assumption each time. The error is telling you exactly what's wrong—reading it carefully is almost always more productive than retrying.
The AI changes different files on every retry but the error persists. This is a diagnostic red flag. It means the AI is guessing at root causes rather than identifying them. You might see it modify userService.ts on the first attempt, then authMiddleware.ts on the second, then types/index.ts on the third—each time missing the actual source of the problem. No amount of retry will solve a problem the AI hasn't actually located yet.
The error message refers to an external dependency that's missing or misconfigured. If your environment is missing a required package, the wrong Node version is active, or an environment variable isn't set, Retry will fail every single time until that external condition is resolved.
# This kind of error needs environment investigation, not retry
Error: Cannot find module 'sharp'
# → Run: npm install sharp
SyntaxError: Unexpected token '??'
# → Check Node.js version, update if below 14
Error: Missing required environment variable: DATABASE_URL
# → Set the variable before retryingThe Three-Retry Rule
I've settled on a simple heuristic: if the same approach has failed three times in a row with no meaningful improvement, I stop retrying and change the approach.
Here's the logic: genuinely non-deterministic failures tend to resolve within two or three attempts. If something is still failing on the third retry, it's almost certainly a structural problem—not a timing problem. Continuing to retry past this point is just burning time and, in some cases, burning API credits.
The key phrase is "the same approach." Retry can be appropriate beyond three attempts if you've changed something between attempts—the prompt, the context, the model, or the scope of the task. What I'm cautioning against is pressing Retry three times with zero changes and expecting something different to happen.
There's also a psychological trap worth naming: the more time you've already invested in a failing approach, the harder it feels to abandon it. Three retries is a natural boundary that forces a reassessment before sunk cost bias kicks in.
Three Ways to Break Out of a Retry Loop
When you've hit the retry ceiling and nothing has changed, here are the approaches I reach for.
Reset the context. Antigravity builds its understanding from the conversation history. If early messages contain incorrect assumptions or misleading partial information, that context can contaminate every subsequent attempt in the same session. Starting a fresh chat and providing only the error message plus the relevant files often produces a completely different—and correct—diagnosis. The AI approaches the problem without carrying forward any prior mistaken conclusions.
# Effective format for a fresh context reset
Here's the error I'm seeing:
[paste the exact error message]
The relevant file is this one:
[use @file reference to attach it]
Please identify the root cause only — don't make any changes yet.
Notice the instruction "don't make any changes yet." This prevents the AI from jumping to a fix before fully understanding the problem, which is often what causes the retry loop in the first place.
Switch the model. Antigravity lets you choose which model handles a given request. If Gemini 2.5 Pro has been consistently approaching a problem the same way with the same result, Gemini 3 Flash might come at it from a different angle. This isn't about one model being objectively "better"—it's about getting a genuinely different perspective on the problem. Different models have different strengths and failure modes, and what stumps one sometimes resolves immediately with another.
Decompose the problem. "Fix the build error" is a large, ambiguous instruction. "Fix only the type error on line 47 of userService.ts" is precise and actionable. Breaking the problem into smaller units gives the AI a clearer target and dramatically reduces the chance of it chasing the wrong issue. This decomposition approach is especially valuable for TypeScript projects where errors can cascade—one type mismatch triggers five more downstream.
// Instead of: "Fix all the TypeScript errors"
// Try: "Fix only this specific error first:
// Type 'string | undefined' is not assignable to type 'string'
// in src/lib/auth.ts at line 34"
// Then address remaining errors one at a timeUsing Plan Mode as a Diagnostic Tool Before Retrying
Before retrying a failing fix for the third time, consider using Plan Mode to get a map of the problem space.
Ask Antigravity: "What are all the possible root causes for this error? List them without making any changes." The response will often surface an assumption the AI has been operating under that you can immediately correct—which is far more efficient than hoping the next retry will somehow land on the right answer.
# Effective Plan Mode diagnostic flow
1. Enable Plan Mode
2. Paste the error message and attach the relevant files
3. Ask: "List all possible root causes for this error.
Do not make any changes yet."
4. Review the list—identify which cause is most plausible
5. Ask: "Fix only [specific cause] and nothing else."
6. Disable Plan Mode and execute
This approach forces a two-stage process: diagnosis, then treatment. Most retry loops happen because the AI skips straight to treatment without a solid diagnosis. Plan Mode makes the diagnosis explicit and reviewable, so you can catch a wrong assumption before it burns another retry.
A Note on Credits and Cost
If you're on a credit-based Antigravity plan, failed retries still consume credits. This makes the cost of blind retrying more concrete: every unnecessary retry is both wasted time and wasted budget. The three-retry rule isn't just about efficiency—it's also a reasonable way to protect your usage quota from being eaten by a problem that requires a different approach, not more attempts.
When you find yourself in a retry loop, switching to a faster, cheaper model for the diagnostic step (figuring out what's wrong) and saving the more powerful model for the fix step (actually correcting it) is a practical way to manage both time and credits.
Retry is a powerful feature precisely because it acknowledges that development is non-deterministic. External services go down, network conditions vary, AI outputs differ between runs. But that power is only useful when the problem is actually non-deterministic.
The habit worth building is simple: before you hit Retry, spend five seconds reading the error message. Ask yourself whether it's describing a temporary external condition or a structural problem in your code. Is this the kind of thing that might resolve on its own? Or is it telling you something specific about your codebase that needs to be understood and fixed?
That five-second pause—reading the error before reaching for Retry—will save you far more time than any number of retries on the wrong type of problem.