When Your Overnight AI Coding Run Fails: A Troubleshooting Guide

You picked a real backlog task. You wrote a one-paragraph spec. You kicked off Ralph Workflow before bed and walked away. In the morning, you found one of these:

The agent looped on the same file six times and produced nothing.

The output is there but it does not compile.

The run crashed at step two with an API error.

The plan looks reasonable, the code looks reasonable, and the tests all fail anyway.

This is normal. Unattended coding is a system, not a magic wand. Most first-run failures are caused by one of five predictable problems. Fix the problem, and the second run usually succeeds.

1. The spec was too vague

The single most common failure mode — and the easiest to fix.

What it looks like: The plan has several steps that sound right ("set up the module structure", "write implementation") but the actual output drifts. The agent wrote a solution for a problem you did not ask it to solve. The fix-up loop kept running but kept missing the point.

Why it happens: "Add user authentication" sounds specific to you because you know your app. To an agent that has never seen your codebase, that directive could mean any of a dozen things — JWT, OAuth, session cookies, a login page, an API endpoint, all of the above.

Fix: Add a concrete correctness check to your spec. Instead of "add user authentication", write:

Add JWT-based API authentication. The user sends a POST to /api/auth with email and password. The endpoint returns a JWT token. Subsequent requests include the token in an Authorization: Bearer header. The middleware rejects unauthenticated requests to /api/me with 401. Tests must verify: a valid login returns a parseable token, an invalid password returns 401, and the middleware blocks requests without a token.

The difference is one sentence that tells the agent how to check its own work. If the agent can verify its output, it stops looping.

2. The task was too big for one run

What it looks like: The run took six hours, consumed 200K tokens, and still left you with a half-done feature that kind of works in places and definitely does not work in others. The planning phase produced a sensible outline, but the implementation phase never caught up.

Why it happens: Even with good planning, a single agent has a practical ceiling. Ralph Workflow's loop can handle multi-step work — that is the whole point — but a single PROMPT.md should target roughly 2-6 hours of work. If the plan step counts more than four meaningful implementation chunks, the agent runs out of context coherence before finishing.

Fix: Break the task into two PROMPT.md files. Run the first (foundation work — data models, API contracts, base module structure). Review the output. Then run the second (feature behavior on top of the foundation). Each run gets its own focused context window, and you get a review checkpoint in the middle.

This is not a limitation. It is the same discipline you would apply if you were pair-programming with a human — you would not hand them a week of work with no check-in.

3. Your model choices are mismatched to the task

What it looks like: The implementation quality is poor — the code compiles but the logic is wrong, the design is sloppy, or the solution cuts corners in ways that are technically correct but practically useless.

Why it happens: You used a cheap or mid-tier model for the implementation phase. Cheap models are fine for planning, analysis, and simple verification. They are not fine for writing production-quality code.

Fix: Check your config.yml model assignments. The implementation phase should use your strongest model:

models:
implement: "openrouter/deepseek-v4-pro" # or claude-code/claude-opus-4-5
plan: "openrouter/deepseek-v4-flash" # planning does not need frontier
analyze: "minimax/minimax-m2.7" # reading files costs very little

If you are unsure, run the same task with a stronger implementation model and compare the output. The cost difference is typically single-digit cents per run.

4. API rate limits hit mid-run

What it looks like: The run stopped partway through with an error about rate limits, quota exhaustion, or "too many requests." Some files were written, some were not, and nothing committed.

Why it happens: Your API provider has per-minute or per-day limits, and an unattended coding run can burn through them faster than you expect — especially if the fix-up loop runs multiple iterations.

Fix: Three things to check:

Provider tier. Free-tier API keys have low rate limits. Make sure you are on at least a paid tier with reasonable limits. For OpenRouter, check your credits. For Claude Code, check your Anthropic usage tier.

Fix-up loop budget. If the fix-up loop runs more than 3 iterations, something is wrong with either the spec (too vague → the agent cannot verify) or the model choice (too weak → the agent cannot fix correctly). Tighten the cause, and the fix-up loop stops burning tokens.

Alternative providers. If OpenRouter is rate-limiting you, Claude Code with Anthropic direct billing or OpenAI API billing may have different limits. Ralph Workflow supports any agent on your PATH — you are not locked to one provider.

5. The agent environment is missing dependencies

What it looks like: The run errors out on a ModuleNotFoundError, command not found, or import failure. The agent "finished" but nothing actually ran.

Why it happens: The agent needs the same tools you do — Python with the right virtualenv, Node with the right packages, a compiler, a formatter. If those are not available, the agent cannot verify its own output, which means it cannot complete the loop.

Fix: Before running, verify that a human can do the same task on the same machine:

# If the task involves Python:
source .venv/bin/activate && python -c "import your_project_module"

# If the task involves Node:
which node && npm run build --dry-run

# Run the existing test suite:
pytest

If the project does not build or test for a human, it will not build or test for an agent. This is not a Ralph Workflow problem — it is a project hygiene problem that the agent just exposed.

The only real failure is not retrying

The four criteria for a good first task are: clear boundary, clear correctness check, real but not critical, and 2-6 hours of work. If your task fit those criteria and the run still failed, the failure mode almost certainly maps to one of the five categories above. Fix that single thing and run it again.

The people who get value from unattended coding are not the ones whose first run works perfectly. They are the ones who look at a failed run, identify which of the five problems it hit, fix it, and go to bed again.

The deployment you are reading is the result of this exact process. This post was drafted, reviewed, and deployed through Ralph Workflow — spec-driven, agent-executed, human-reviewed. Inspect the workflow on Codeberg →