Your automated agent looks busy. It isn’t getting anywhere.
Picture an automated assistant that keeps trying the same fix over and over, or that undoes its own work every other step. It looks busy, but nothing useful is getting done.
Most agent loops follow the same simple pattern: observe, decide, act, observe. Left alone, that cycle has no automatic stop button. Two failure modes emerge from that gap, retry loops and oscillation.
Retry Loops: Blunt and Obvious
Retry loops are the easy failure to spot. The agent takes an action, the result is wrong, and the agent tries the same action again sometimes with only tiny tweaks.
This happens when the agent:
- Misreads why something failed
- Has no memory that it already tried the action
- Trusts its original plan too much
The log ends up full of repetitions until an external timeout finally interrupts the process.
Oscillation: Quieter and Trickier
Oscillation is harder to catch. Instead of repeating one move, the agent flips between two or more states and cancels its own progress.
Imagine a file edit that gets made, then later gets reversed, then reintroduced again. Neither state ever stabilizes, because the agent keeps re-evaluating and undoing its own prior choices.
Here’s why it sneaks past automated checks: a naive monitor that only looks for the same command appearing twice will miss an agent that alternates actions. Detecting oscillation means tracking the recent history of states — not just the last command.
An agent that cannot remember its past will keep repeating its mistakes.
Three Practical Fixes
There are practical ways to reduce both problems without making the model itself smarter in the moment.
- Keep an explicit record of attempts and outcomes – so repeated actions are recognized as repeats, not fresh starts.
- Compare current state to recent past states – using state hashing or diffs to spot reversals and cycles.
- Require a different plan after failure – limit retries and escalate to a fallback, like asking for help or switching tools.
Treating “it failed” as a prompt to do something different not just try harder is the core idea here. Force the system to state what it will change before retrying, or cap how many times a single approach can run. And check for stability before declaring a task complete, so you’re not just capturing a snapshot mid-cycle.
The Underlying Insight
These fixes all point to the same thing: the system needs memory and a sense of its own history.
A human naturally remembers what they tried and shifts strategy when something keeps failing. An agent will only do that if the surrounding software is built to keep track, and to require meaningful change after failure.
Let’s Build This Together
At Manisoft, we build automation with those guardrails in mind adding progress tracking and stability checks so solutions finish reliably instead of looping indefinitely.
If an automated workflow in your project feels stuck or keeps undoing itself, our team can help design the memory and escalation paths that stop the loops and get the work across the finish line.
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