Bounded Loops: Stop Runaway Agents Before They Burn Tokens

Where this fits

You have an agent that re-plans for the 40th time, retries the same failing tool call, or bounces between subagents until it hits GraphRecursionError. This skill puts guardrails outside the LM's control: a counter, an exit predicate, a stagnation signal, and an escalation path. You get loops that end predictably instead of burning tokens.

Why agents benefit

• Terminate reliably. The LM is never the final judge of done; an external counter and predicate decide.
• Detect stagnation. Repeated outputs and dead ends trigger escalation instead of a silent infinite loop.
• Fail with a route. Exhausted agents go to a human or fallback rather than returning garbage.
• Stay framework-agnostic. The same discipline maps to LangGraph, CrewAI, AutoGen, DSPy, Aider, and plain SDKs.
• Reduce production risk. No more agent-won't-stop incidents.

Practical scenarios

Multi-agent handoff spiral

Two agents keep handing the same task back with small edits. Add a max_handoffs counter and a stagnation signal; route to a human after the threshold.

Test-fix cycles

An agent generates code, runs tests, patches the same failure, and repeats. One flaky test can generate twenty near-identical fix attempts. Bounded-loop caps that loop and escalates.

Planner/optimizer loops

A plan-act-observe agent rewrites its plan every step because the world changed. Instead of letting the LM decide when to stop, set an exit predicate on goal completion and a budget for replans.

Add it to your agent workflow

Install with:

npx skills add agentsope/SkillAlchemy --skill agentsop-bounded-loop

Then encode the guard outside your agent. For example, configure the loop limits before starting any run:

{
  max_steps: 12,
  exit_predicate: 'goal_met',
  stagnation_window: 3,
  on_exhaustion: 'escalate_to_human'
}

The exact integration differs by framework, but the rule is the same: termination control stays outside the LM's reach.

Compare before adopting

Compare against framework-native limits — LangGraph recursion_limit, CrewAI max_iter, AutoGen max_consecutive_auto_reply — to see whether this skill adds reusable discipline or just another layer. Look at maintenance freshness: commit recency, open issues, and response to changes in major agent frameworks. Check whether it fits your workflow as a configurable guard rather than a rigid abstraction.

Why it is worth tracking

This skill earns attention because it names a real failure mode and packages the fix as framework-agnostic discipline. With 357 stars and an explicit list of search terms pulled from real developer pain, it has clear community momentum. Track it if you are putting agents into production, and evaluate it as soon as you see a loop exceed its budget. For a snapshot of repo health and skill details, see https://www.openagentskill.com/skills/agentsope-agentsop-bounded-loop.