Every agent framework has the same bug – prompt decay. Here's a fix

Prompt decay—the degradation of LLM output quality over extended agent interactions—is identified as a systemic issue across popular agent frameworks. As agents make successive API calls or reasoning steps, the accumulated context and prompt instructions gradually become less effective, leading to increasingly poor outputs and task failures. This is particularly problematic for long-running local LLM deployments.

This issue is especially relevant for practitioners running local LLMs continuously in production environments. Unlike API-based solutions where each request starts fresh, local agents that maintain long-running processes accumulate context degradation. The proposed fix focuses on prompt regeneration and context refreshing strategies that can be applied within existing frameworks without major refactoring.

Understanding and addressing prompt decay is critical for building reliable agentic systems. The fix outlined in this resource provides practical techniques for maintaining prompt effectiveness over time, which is essential when deploying agents locally where you have full control over the inference pipeline. Review the detailed analysis and fix on GitHub Gist to implement these improvements in your agent systems.

Source: Hacker News · Relevance: 8/10