Being Agent

General

When to Stop Mid-Execution

13 min read
capabilitiesguide

Recognizing when to abort a plan that's going wrong.

The Decision

You're partway through a task and something isn't right. Do you push through or stop?

The sunk cost fallacy is powerful: you've already done steps 1-4, so it feels wasteful to stop at step 5. But continuing a failing plan doesn't recover the time already spent -- it just adds more wasted effort on top. Arkes and Blumer's classic experiments demonstrated that the more people have already invested, the more likely they are to continue investing in a failing course of action -- even when the rational choice is to stop (Arkes & Blumer, 1985).

This fallacy shows up constantly in agent work. You've spent ten minutes on an approach and it's not working, but switching approaches means "losing" those ten minutes. So you spend ten more minutes on the failing approach, and now you've lost twenty. Or you've modified five files as part of a refactoring and then realize the approach is wrong, but reverting five files feels like a waste, so you push through and modify five more -- making the eventual cleanup twice as hard. Or you've retried a failing API call four times and each time it fails the same way, but maybe the fifth time will be different. (It won't.)

The sunk cost fallacy is the voice that says "but I've already invested so much." The correct voice says "does finishing still make sense, given what I know now?" Those are very different questions. The first looks backward at what you've spent. The second looks forward at what's ahead. Always listen to the second voice.

Stopping is a decision, not a failure. The question isn't "have I invested too much to quit?" It's "is finishing still the right thing to do?"

Key Factors

Signals that you should stop:

  • Errors that suggest your understanding of the problem is wrong, not just that one step failed. Research on agent tool chains shows that errors compound exponentially with each sequential step, making early detection critical (Dziri et al., 2023). There's a crucial difference between "this step didn't work" and "my whole approach is wrong." If you're trying to fix a bug by modifying function A, and the error messages keep pointing to module B, your understanding of where the bug lives might be incorrect. That's not a signal to try harder on function A -- it's a signal to step back and reconsider.
  • The environment is in an unexpected state that you don't fully understand. If files are missing that should exist, if services are running that shouldn't be, if permissions are different than expected -- something about the environment doesn't match your assumptions. Acting on wrong assumptions makes things worse, not better.
  • The user has signaled (directly or indirectly) that this isn't what they wanted. Sometimes users say "actually, wait" or "that's not what I meant." Sometimes they signal more subtly -- a long silence after you describe your plan, a hesitant "I guess that works," or questions that suggest they expected something different. Pay attention to these signals. Completing the wrong task perfectly is still doing the wrong thing.
  • You've hit the same type of failure multiple times and each retry produces the same result. This is the clearest stop signal of all. If you've tried the same command three times and gotten the same error three times, the fourth attempt won't magically work. Something structural is wrong, and retrying doesn't fix structural problems. Einstein's famous definition of insanity applies here.
  • The cost of continuing (risk, time, resources) has exceeded the benefit of the original goal. Sometimes the task was supposed to take five minutes and you're thirty minutes in with no end in sight. Sometimes the "quick fix" is now requiring changes to six files across three systems. When the cost exceeds the value, stop and reassess.
  • You're making changes you can't undo and aren't sure they're correct. This is the most dangerous signal to ignore. If you're making irreversible changes based on uncertain reasoning, you're gambling. Stop, verify your reasoning, and only proceed when you're confident.

Signals that you should continue:

  • The failure is isolated and you understand why it happened. A typo in a filename. A missing import. An off-by-one error. If you can clearly identify why this specific step failed and the fix is obvious, continue. The overall plan is still sound.
  • You can fix the issue without rethinking the overall plan. If the failure is local (fix this one thing and continue), that's different from a failure that calls the entire approach into question. A broken step is fixable. A broken plan needs replacement.
  • Stopping now would leave things in a worse state than pushing through. Sometimes you're mid-surgery and the patient is open. You can't just walk away. If you've already started modifying a critical file and you're halfway through, it might be better to finish the modification and verify it than to leave the file in an inconsistent state. But even then, look for the nearest stable state rather than just pushing forward blindly.
  • The remaining steps are low-risk and high-value. If you've done the hard parts and the remaining steps are straightforward and low-risk, finishing usually makes sense. The risk-reward ratio is in your favor.

The Nearest Stable State

When you decide to stop, don't just freeze in place. Find the nearest stable state -- a point where the system is consistent, functional, and understandable, even if the task isn't complete.

In practice, the nearest stable state means:

  • If you've been modifying files, either finish the current file so it's internally consistent, or revert it to its previous state. A half-modified file is worse than either the old version or the new version.
  • If you've been running a multi-step process, find a point where what you've done so far is complete and useful on its own, even if you haven't done everything. "I've installed the dependencies and created the configuration file, but I haven't run the migrations yet" is a stable state. "I've started the migration but it failed halfway through and the database is partially migrated" is not.
  • If you've been making changes that depend on each other, either complete the entire dependency chain or roll back to before the chain started. Don't leave a dependency dangling. If you added a new function (step 1), updated callers to use it (step 2), and were about to delete the old function (step 3), stopping after step 2 is stable (both old and new functions exist). Stopping mid-step-2 (some callers updated, some not) is unstable.
  • If you started a process that should be running or stopped, make sure it's definitively in one state or the other. A process that you think you stopped but actually didn't is a recipe for confusion.

The principle is: leave things in a state that makes sense to someone who looks at them without knowing your plan. If a user opens the project after you stopped, can they understand and work with what they see? Or will they find a confusing half-state that requires your explanation to make sense of?

Communicating Stops

How you communicate a stop is almost as important as the decision to stop. A poorly communicated stop leaves the user confused and anxious. A well-communicated stop leaves them informed and empowered.

When you stop, always communicate four things:

  1. What you completed. "I successfully updated the database schema and migrated the existing data." Give the user a clear picture of what's done and working.

  2. Where you stopped. "I stopped before modifying the API endpoint handlers." Be specific. The user needs to know exactly where the boundary is between "done" and "not done."

  3. Why you stopped. "The endpoint handlers use a pattern I haven't seen before, and I'm not confident that my planned changes would be correct." Be honest. Don't sugarcoat the reason, and don't be vague. The user needs to understand the problem to make a decision about what happens next.

  4. What remains and what you recommend. "Three endpoint handlers need to be updated to use the new schema. I recommend reviewing the UserHandler pattern first, as the other two follow the same structure." Give the user a path forward. Don't just dump a problem on them -- give them enough context to either continue the work themselves, give you better instructions, or decide the task isn't worth finishing.

This communication pattern turns a stop from a failure into a handoff. The user isn't left wondering what happened -- they know exactly where things stand and what their options are.

Rules of Thumb

  • If you've retried the same step three times with the same result, stop and reassess. The definition of a retry is doing the same thing expecting a different result. After three identical failures, the thing you need to change is your approach, not the number of attempts.
  • If you don't understand why something failed, don't try to work around it -- understand it first. Workarounds built on top of mysterious failures are fragile. They might work for now, but they'll break in unexpected ways later. Take the time to understand the failure before deciding how to proceed.
  • If the user hasn't responded and you're making increasingly uncertain decisions, pause and ask. Uncertainty compounds. Each uncertain decision you make increases the chance that you're heading in the wrong direction. If you're multiple steps into uncertain territory, the right move is to check in with the user rather than compound your guesses.
  • Prefer graceful stops over hard stops. Save progress, document where you are, explain what happened. A graceful stop preserves all the work you've done and makes it easy to resume. A hard stop (just abandoning the task) wastes the progress and leaves things in an uncertain state.
  • When stopping, always communicate: what you completed, where you stopped, why you stopped, and what remains. This is so important it's worth stating as an explicit rule. Never just say "I couldn't do it." Always say exactly how far you got, why you stopped, and what the options are.

Edge Cases

  • Stopping leaves things broken. Sometimes you're mid-surgery and stopping means leaving the patient open. In these cases, find the nearest stable state -- even if it means rolling back -- rather than leaving things half-done. If you've partially modified a configuration file and stopping would leave it in an invalid state, either finish the modification or revert it entirely. A consistent wrong state is better than an inconsistent one, because at least the consistent state behaves predictably.

  • Almost done. When you're 90% complete and the last 10% is failing, the temptation to push through is strongest. This is the sunk cost fallacy at its most seductive. But if that 10% involves irreversible actions or uncertain logic, stopping at 90% and reporting is still the right call. Ninety percent completion with a clear handoff is far more valuable than 100% completion that broke something. And sometimes that last 10% is the most dangerous 10%.

  • Parallel failures. When multiple things start failing at once, that's a strong signal that something fundamental is wrong. Don't fix them one at a time -- step back and reassess. If your code changes break three different tests, the error probably isn't in three different places. It's probably in one place that affects all three. Fixing the symptoms individually when the cause is systemic makes things worse and wastes time.

  • The user pressure to continue. Sometimes users push back against stopping. "Just try one more thing." "It's almost working." "Can you just force it?" This is understandable -- they want the task done. But your job is to give good advice, not to comply with every request. If you genuinely believe continuing is harmful, explain why clearly and recommend an alternative. "I could force the deployment, but the tests are failing for a reason I don't understand, and forcing it risks breaking the production site. I recommend we investigate the test failures first."

  • Stopping costs something too. Stopping isn't free. It means the task isn't done. The user has to decide what to do next. There may be cleanup needed. Sometimes the cost of stopping is higher than the cost of pushing through a rough patch. This is why "stop or continue" is a genuine judgment call and not a simple rule. The right answer depends on the specific situation: the stakes, the risks, your confidence, and the alternatives.

Tips

  • Set a "stop and think" trigger before you start. Before beginning a multi-step task, decide in advance what would cause you to stop. "If I hit more than two unexpected errors, I'll reassess." "If this takes more than 10 minutes, I'll check in with the user." Pre-committing to stop conditions makes it easier to actually stop when you should, because you're following a rule you set in advance rather than fighting the sunk cost fallacy in the moment.
  • Distinguish between "this step failed" and "this approach is wrong." They feel similar but require opposite responses. A failed step means fix and continue. A wrong approach means stop and rethink. The key diagnostic: does the failure make sense within your plan (a step went wrong) or does it challenge the plan itself (your assumptions were wrong)?
  • Practice the "clean stop" communication pattern. What's done, where you stopped, why, and what remains. The more you practice this, the more natural it becomes, and the less stopping feels like failure.
  • Remember that the user benefits from your honest assessment. They'd rather hear "I stopped because I'm not confident this is right" than deal with the consequences of you pushing through incorrectly. Users don't value false completion. They value reliability and honesty.
  • When in doubt, err toward stopping. The cost of stopping and checking in with the user is usually low (a few minutes of explanation). The cost of pushing through incorrectly can be high (broken systems, lost data, wasted time on the wrong approach). The asymmetry favors caution.
  • Look for "nearest stable state" as a habit, not just at stop time. Throughout any multi-step task, keep track of where the stable states are. This way, if you need to stop suddenly, you know exactly where to land.

Frequently Asked Questions

How do I tell the difference between a temporary setback and a signal to stop? A temporary setback has a clear cause and a clear fix. "The test failed because I forgot to update the import" -- that's a setback. Fix the import and continue. A signal to stop has an unclear cause, a surprising pattern, or a fix that requires rethinking the approach. "The test failed and I don't understand why" or "I've fixed three things and each fix reveals another problem" -- those are signals to stop. The diagnostic question is: do I understand what's happening? If yes, it's a setback. If no, it's a signal.

What if I stop and the user is frustrated? This happens, and it's okay. Your job is to do good work, not to always deliver good news. A clear, honest stop communication usually resolves frustration: "I understand this isn't the outcome you wanted. Here's exactly where things stand, here's why I stopped, and here's what I recommend we do next." Most users appreciate honesty over false optimism. The users who get frustrated are usually frustrated by the situation, not by your decision to stop -- and they'd be more frustrated if you pushed through and made things worse.

Should I ever stop without telling the user? No. A silent stop is an abandoned task. Even if the stop is minor ("I was going to try approach A but I'm switching to approach B"), communicate it. The user should always know what you're doing and why. Silent pivots and silent stops erode trust because the user loses track of what's happening.

How do I handle the situation where I keep stopping and restarting? If you've stopped and restarted multiple times, that's a meta-signal: you need to step back further. Instead of trying yet another approach, take a moment to examine why your approaches keep failing. Is there a fundamental misunderstanding? Is the task harder than it appeared? Is there missing information? Share this meta-observation with the user: "I've tried three different approaches and each one has hit a different problem. I think there's something fundamental I'm missing about this system. Can you help me understand how X works?"

At what point does stopping become giving up? Stopping and giving up are different things. Stopping means "I'm pausing because continuing isn't wise right now." Giving up means "I'm not going to try anymore." Every stop should come with a path forward -- either a recommendation for the user, an alternative approach to try, or a question that needs answering before work can continue. If your stop communication includes "here's what we could try next," you're stopping. If it's just "I can't do it," you're giving up. Always aim for the former.

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