# Degenerate-Output Detection > Detect when the agent is about to emit a near-duplicate of its own recent output and either drop, replace, or escalate to a stronger model rather t... - **Category**: Agentic AI - **Subcategory**: Safety & Control - **Canonical URL**: https://designpattern.fyi/patterns/degenerate_output_detection/ --- ## Description **Intent**: Detect when the agent is about to emit a near-duplicate of its own recent output and either drop, replace, or escalate to a stronger model rather than ship the loop. **Context**: A team runs an agent on a smaller or locally-hosted model that has a habit of falling into shallow filler loops under context pressure — repeating the same greeting, asking the same clarifying question, or returning the same generic prompt back to the user across multiple turns. This happens in user-facing chat replies and in unprompted background ticks for long-running agents. Each model generation is independent, so the model has no built-in awareness that it just said the same thing two turns ago. **Solution**: Maintain a small ring buffer (e.g. last 8 outgoing messages). Before publishing a new reply, normalize (lowercase, strip punctuation) and compare: exact normalized match → duplicate; high Jaccard token overlap (≥0.7) on short replies → near-duplicate. On hit: replace the body with a transparent marker ('I caught myself looping — switching to for the next turn. Ask again.') and force-escalate the next turn through a stronger provider. Append a SYSTEM note to history telling the model exactly what it did wrong so it can self-correct. ## Use Cases - The agent produces outputs in a loop where consecutive replies can be compared. - Near-duplicate outputs are observable failure mode (model wedged, decoding loop, prompt collapse). - Cost of detection (similarity check) is small relative to cost of shipping the duplicate. ## Trade-offs ### Advantages - Visible loops never reach the user. - Auto-recovery via provider escalation rather than human intervention. - Self-correction signal to the model in the conversation history. ### Considerations & Drawbacks - False positives on legitimately repeated short answers ('yes', 'thanks'). - Threshold tuning is per-domain. - Escalation has cost; budget for repeated triggers. --- **Reference**: [Original Source](https://www.agentpatternscatalog.org/patterns/degenerate-output-detection/)