How Luthy Chihuahua works
Luthy Chihuahua watches runtime execution, detects when progress stops, and intervenes before wasteful runs compound into higher cost, latency, and unreliable outcomes.
Observe execution
Luthy Chihuahua watches runtime signals as the agent progresses across steps, tool calls, and model interactions.
Detect decay
It identifies looping, stalling, repetition, and non-converging behavior before the run becomes obviously failed.
Act before waste compounds
It applies runtime policies to stop, constrain, or safely fail execution before cost and latency keep rising.
What Chihuahua watches at runtime
Execution flow
Tracks whether the run is continuing meaningfully across steps
Progress signals
Looks for evidence that the run is still improving or converging
Repetition patterns
Detects loops, retries, repeated tool use, and low-value continuation
Operational limits
Applies cost, time, and step boundaries defined by runtime policy
What Chihuahua can do when a run drifts
Interrupt
Stop clearly non-productive runs before additional waste accumulates
Constrain
Apply boundaries when execution is degrading but not yet fully broken
Safely fail
End bad runs predictably so operators can trust system behavior
Why Chihuahua stays lightweight
External by design
Works on top of your stack rather than replacing orchestration
No model retraining
Does not require weight changes or fine-tuning
Easy to adopt
Fits existing Python-based runtimes and orchestration frameworks
Where Chihuahua fits in your stack
Luthy Chihuahua is designed to sit alongside existing agent infrastructure as a runtime protection layer, not replace it.
See Luthy Chihuahua in action
Start free in Observer Mode or view the product page to see how Luthy Chihuahua adds runtime governance without rebuilding your stack.