Context Degradation Comes from Turns, Not Token Count

You're debugging the wrong variable.

Teams watch Gemini start hallucinating tool calls and blame the context window. Too much code loaded. Too many files indexed. Time to trim.

But the context window isn't the problem. The turn count is.

The symptom everyone misdiagnoses

Twelve exchanges into a debugging session, the model stops calling tools correctly. It hallucinates parameters. It references functions that don't exist. It confidently suggests the same broken fix it already tried.

The natural instinct: the context is too large. Too much information. The model is overwhelmed.

So you trim. You remove files from context. You start a new chat with less loaded. Sometimes it works. Sometimes it doesn't. And when it doesn't work, you assume you didn't trim enough.

The actual mechanism is different. The model isn't losing track of your codebase. It's losing track of your instructions.

"I actually have experienced not that it goes to crap once you hit a certain context level, but a certain number of turns. Maybe 10 or 12 turns, it starts to degrade and forget those instructions."

Hannes Rudolph, Office Hours S01E11

The model can still read your files. It can still parse your code. What degrades is the tool use: the structured calls, the parameter accuracy, the ability to follow the workflow you established at the start.

The compression fix

When context compression kicks in, it doesn't just shrink the token count. It resets the turn structure.

The model gets a fresh start with a summary of what happened and what's next. Instructions that were buried under twelve rounds of back-and-forth now sit at the top of the context, clear and recent.

"When you compress the context, it actually pulls it back to basically be a new chat with a summary of what was going on and what the next steps are. That gets it back on track."

Hannes Rudolph, Office Hours S01E11

This isn't about freeing up tokens. It's about resetting the conversation structure so instructions don't get lost in the archaeology of prior exchanges.

What actually degrades

The degradation is specific. Understanding stays intact. Tool use falls apart.

"You don't see it hallucinating or something like that. It is able to understand what you're saying. The only thing that degrades is basically the tool use at some point at certain amount of turns."

Matt Rubens, Office Hours S01E11

The model still comprehends your request. It still knows what you're asking for. But when it tries to translate that understanding into a structured tool call, the parameters drift. The file paths become approximate. The function names mutate.

If you're using Roo Code's agentic workflows, this matters. Tool execution is how the loop closes. When tool calls degrade, the agent starts running commands that don't match the actual state of your codebase.

How Roo Code maintains tool accuracy across long sessions

Roo Code closes the loop by running commands, executing tests, and iterating based on results. This agentic workflow depends entirely on accurate tool calls. When turn-based degradation kicks in, the agent loses its ability to execute reliably.

Context compression in Roo Code works as an instruction reset, not just a memory trim. The summary captures task state and next steps, then positions your original instructions at the top of the fresh context. This restores tool call accuracy without losing progress.

For teams using BYOK configurations, the turn threshold remains consistent across providers. The degradation pattern appears whether you're running Claude, GPT-4, or Gemini. The fix is the same: compress before turn twelve, not after token limits.

Token trimming vs. turn compression

Why this matters for your team

For a Series A - C team running long debugging sessions, the ten-to-twelve turn threshold hits faster than you'd expect. A complex bug investigation can easily burn through that budget: initial context, first attempt, test failure, second attempt, another failure, debugging output, third attempt.

By the time you're on attempt four, you're past the threshold. And the symptoms look like the model got dumber, when actually it just lost its grip on the structured workflow.

The fix is counterintuitive: instead of trimming context, trigger a compression. Instead of loading less, reset the conversation structure with a summary that puts instructions back at the top.

Track turn count, not token count. When you hit ten exchanges, consider compressing before the tool calls start to drift.