Context Rot
Also known as: Conversation degradation
Long-running chats degrade: early instructions get forgotten, and tool calls become less reliable.
Degradation of LLM behavior as the conversation history grows long: earlier turns get less attention, instructions get re-interpreted, and tool calls become less reliable. Mitigated by summarization, context engineering, and periodic resets.
In practice
Single most underappreciated failure mode in production agents. Senior LLM app interviews probe summarization and context refresh patterns.
How it compares
Lost-in-the-middle is a positional attention bias; context rot is a longitudinal degradation as history grows.
Related topics
Related terms
Retrieval-Augmented Generation (RAG)
Fetch relevant docs at inference time and stuff them into the prompt so the model can answer from real data.
Chunking
Cutting documents into retrievable pieces before embedding them.
Prompt Engineering
Crafting the input text so the model produces the behavior you want.
AI Agents
LLMs that loop: plan → call tools → observe results → repeat until done.
Model Context Protocol (MCP)
An open protocol (from Anthropic) for letting LLMs call tools and read resources from any compatible server.
Chain-of-Thought (CoT)
Ask the model to think step by step before answering. It boosts accuracy on reasoning tasks.