Have you ever caught yourself slapping the Retry button four times in a row, watching the same agent fail in exactly the same place? I did that for a few embarrassing days when I first started with Antigravity. Same prompt, same failure, same disappointment — just lighter on credits each time.
Antigravity's Retry isn't a single button: it's a small family of recovery actions, and choosing the right one decides whether you spend the next ten minutes fixing the issue or the next hour going in circles.
This article walks through what Retry actually does well, the three modes worth knowing, and the small habits that save me the most credits in day-to-day work.
When Retry Helps and When It Doesn't
The first thing to internalize is that Retry only fixes a specific class of failures. Press it on the wrong kind of failure and you will just produce that failure faster.
Retry tends to work for non-deterministic failures:
- API rate limits or transient timeouts
- LLM output variance — the same prompt occasionally produces a usable result and occasionally doesn't
- Flaky package downloads or temporary CDN issues
- Brief network blips
Retry will not help for deterministic failures:
- The prompt is genuinely ambiguous and the agent keeps misreading it
- A required file is missing from the agent's context (
agents.md, project files) - A real type error or structural code issue
- Missing API keys or insufficient permissions
A simple personal rule: if the same Retry has failed three times in the last hour, stop. Whatever Retry does next will not be different.
There's also a third pattern that lives in between: failures caused by a transient issue that creates a deterministic problem. A network blip during dependency install can leave the project in a half-broken state where Retry now consistently fails. In those cases, the original failure was retryable, but you have to fix the resulting state before pressing Retry will help.
The Three Retry Modes Worth Knowing
Antigravity surfaces Retry in a few different shapes when an agent task fails. The three I reach for most are these.
1. Quick Retry — same prompt, same context
This is the default behavior when you click Retry without thinking about it. Same input, same files, run again.
- Good for: API timeouts, rate limits, network flickers
- Bad for: anything that involves the agent misunderstanding what you want
2. Edit & Retry — modify the prompt before retrying
You edit the failed task's prompt and run again. This is the mode I use the most. After reading the agent's last few log lines, it's usually obvious what part of the prompt was misread, and a one-sentence clarification is often all it takes.
- Good for: ambiguous wording, missing target file, unclear acceptance criteria
- Bad for: problems where the original design itself is wrong — in that case, scrap the task and start a fresh one
One small note about Edit & Retry: when I rewrite the prompt, I try to add only one or two sentences of new context, not rewrite the whole thing. The reason is honest pragmatism — if I rewrite the whole prompt, I no longer know which change actually fixed the problem, and I can't generalize the fix to future tasks. Surgical edits compound into a personal playbook over time; full rewrites just produce more guesses.
3. Retry from Checkpoint — resume from the last successful step
For longer multi-step tasks, Antigravity keeps internal checkpoints. If the agent made it through steps 1–4 and tripped on step 5, you can resume from step 4 instead of regenerating everything.
- Good for: late-stage failures in a long task
- Bad for: tasks where the early steps were already on the wrong track
Once you actually choose a mode instead of mashing Retry by reflex, the felt difference in resolution speed is significant.
Reading the Failure Log in 30 Seconds
Before pressing Retry, I spend about 30 seconds with the log. Not reading the whole thing — just scanning for keywords.
Some keywords map cleanly to causes:
429/rate limit/timeout→ temporary, Quick Retry has a real chancecannot find module/not found→ missing dependency, fix the install before retryingunexpected token/syntax→ structural code issue, use Edit & Retry to expand the promptpermission denied/unauthorized→ auth issue, Retry won't fix it
The single highest-value habit here is reading the last ten lines of the log first. The actual error almost always lives near the bottom. Scrolling back through hundreds of lines wastes time and concentration.
I keep a tiny shell helper in my ~/.zshrc for when I'm running long agent tasks with output redirected to a file:
# Pull the most recent error lines from an agent log
# Usage: agentlog ./latest-agent.log
agentlog() {
local log_file="${1:-./latest-agent.log}"
if [ ! -f "$log_file" ]; then
echo "Log file not found: $log_file"
return 1
fi
echo "=== Last 20 lines ==="
tail -n 20 "$log_file"
echo "=== Error lines only ==="
grep -iE "error|fail|exception|denied" "$log_file" | tail -n 5
}
# Expected output:
# === Last 20 lines ===
# (the tail of the task log)
# === Error lines only ===
# Error: ECONNRESET
# Error: timeout after 30000msEven this small habit changes Retry from a guess into a choice.
I also pay attention to where in the chain the error appeared. If the agent successfully wrote three files and then failed during the test step, that is a very different signal than failing on the first action. The former usually means a localized issue in test setup; the latter often means the prompt or context never got off the ground.
Three Things to Do Before Retrying
To keep my own decision-making predictable, I run through three small checks before pressing Retry.
First, I refresh the agent's context. In long sessions, the agent's view of your project gets stale. Before an Edit & Retry, I make sure the prompt explicitly references the latest version of any files involved — old context is one of the most common causes of repeat failures that look "random."
When I refresh context, I'm not pasting whole files into the prompt. I usually point the agent at the file path and add one sentence about what changed since the last attempt — for example, "the schema in db/schema.ts now includes a deletedAt column." Small, surgical context updates outperform giant blob pastes nearly every time.
Second, I reproduce the failure with a smaller test task. If the failing task is large, every Retry costs real credits. I'll often spin up a tiny separate task that triggers the same problem in 1/10 the tokens. Once I find the fix there, I bring it back to the original task.
Third, I consider switching models. I've had failures on Gemini that resolved instantly when I moved the same task to a local Gemma 4 setup, and the reverse has happened too. The two models tend to misunderstand different things. If you haven't tried this yet, Antigravity × Gemma 4 Local LLM Integration covers the setup end to end.
Designing Workflows So Retry Is Rare
The last piece of advice is the meta one: if you're using Retry constantly, the upstream design probably needs work.
Smaller tasks help more than anything else. A task that "creates five files, writes the tests, and deploys" has five places to fail and one Retry button to undo all of them. I aim for tasks that finish in 5–10 minutes; recovery from a small failure is almost free, and the agent rarely loses the thread.
There's a related habit I picked up from working on AI-assisted projects for a while: I write a one-line goal at the top of every agent task, even when it feels redundant. Something like "// goal: replace the legacy auth middleware with the new session-based one, no behavior changes for end users." Future-me, when reviewing why a Retry keeps failing, can quickly check whether the agent's interpretation actually matches that line. Half the time, it doesn't, and that's where I redirect rather than retry.
A second habit that sounds odd but works: after three consecutive failures in the same place, I close Antigravity and write out the situation longhand on paper. Not typed — actually written. Something about handwriting forces me to slow down and notice the assumption I missed.
If you want to see how this pairs with broader agent operations, Fixing Infinite Loops in Antigravity Agents and Optimizing Antigravity Credit Consumption cover the same problem from neighboring angles. Retry quality and credit efficiency are essentially two sides of the same coin.
If there is one thing to take away today, it's this: before pressing Retry, read only the last ten lines of the log. That single habit will probably cut your wasted Retries in half. When you hit a failure that still won't budge, I hope this article is useful to come back to.