Your agent never gets the whole window. After the system prompt, tool schemas, memory, and a reserve for output, see what's actually left for conversation — and how many turns fit before you must compact.
181K tokens free for conversation after 10% of the window is locked by fixed overhead and the output reserve. At ~2.5K per turn that's about 72 agent steps before you must compact or summarize.
A context window is not a blank notebook the agent fills from the top. Three costs are fixed and re-sent on every single turn: the system prompt, the tool and function schemas (these balloon fast — a fat MCP catalog can be 10–20K tokens before you write a word), and any always-on memory or retrieved context. On top of that you must hold back a reserve for the model's output, because the answer has to fit too. Only what remains — window − (system + tools + memory) − output_reserve — is the real budget for conversation history.
That budget is finite in turns, not just tokens. Each agent step appends a roughly fixed chunk — the model's message, a tool call, and a tool result, which is often the largest part — so the usable space divides into a fixed number of steps before the window is full and you have to compact, summarize, or evict. Shrink the window (a 32K local model) or grow the overhead (dozens of tools, a big memory dump) and the turn count collapses long before you expected it to. This is also why "just use the 1M-token model" is not a free lunch: Anthropic frames context as a finite attention budget with diminishing returns, and Chroma's context-rot study shows recall degrading as the window fills — so the output reserve is partly an accuracy reserve, not only a place to put the answer.
The lever with the best return is almost always the tool schemas: load only the few tools a step needs instead of every tool every turn. The defaults here are illustrative order-of-magnitude figures — every field is editable, so paste in your own token counts. The deeper reasoning is in context engineering for AI agents and context editing vs. compaction. Sizing the hardware, the bill, or the speed instead? See the VRAM, cost, and latency calculators.
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