Token Budget Strategies for Production LLM Applications
Most teams discover their context management problem the same way: a production agent that worked fine in demos starts hallucinating after 15 conversation turns. The logs show valid JSON, the model returned 200, and nobody changed the code. What changed was the accumulation — tool results, retrieved documents, and conversation history quietly filled the context window until the model was reasoning over 80,000 tokens of mixed-relevance content.
Context overflow is the obvious failure mode, but "context rot" is the insidious one. Research shows that LLM performance degrades before you hit the limit. As context grows, models exhibit a lost-in-the-middle effect: attention concentrates at the beginning and end of the input while content in the middle becomes unreliable. Instructions buried at turn 12 of a 30-turn conversation may effectively disappear. The model doesn't error out — it just quietly ignores them.
