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AI agents degrade due to prompt bloat, not context limits, experts say

By PulseAugur Editorial · [1 sources] ·

A common failure mode in AI agents, even those using powerful models like GPT-5 or Claude, is a degradation in performance over time due to prompt bloat rather than context window limitations. This "context rot" leads to slower responses, forgotten facts, and increased looping. The solution lies not in larger context windows, but in better memory architecture, such as using bounded live context, separate long-term memory, and explicit token budgets, as demonstrated by frameworks like LangGraph and MemGPT. AI

IMPACT Highlights the need for robust memory architectures in AI agents to prevent performance degradation and ensure reliability.

RANK_REASON The item discusses a common failure mode and potential solutions for AI agents, offering analysis and architectural advice rather than announcing a new product or research finding.

Read on dev.to — LLM tag →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

AI agents degrade due to prompt bloat, not context limits, experts say

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The item discusses a common failure mode and potential solutions for AI agents, offering analysis and architectural advice rather than announcing a new product or research finding.
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  1. dev.to — LLM tag TIER_1 English(EN) · Lars Winstand ·

    My agent kept getting worse after 24+ hours, and the fix was using less context, not more

    <p>I keep seeing the same failure mode in agent systems.</p> <p>Someone wires up GPT-5, Claude, or another strong model, gives it a huge context window, and decides the safest thing is to keep everything:</p> <ul> <li>every user turn</li> <li>every tool call</li> <li>every failed…

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