Token budget allocation in the context window is a core technical challenge in agent engineering — no matter how large the model context length is, it's still finite, and the history generated by an agent during long-running operations quickly exceeds the window limit. Current mainstream agents implement three compression strategies. Sliding Window: keeps the most recent N conversation turns, discarding the oldest. This is the simplest approach but loses early critical decision context. Semantic Summarization: the agent periodically compresses conversation history into summaries kept in context, with original records offloaded to external storage. Keyframe Retention: the agent identifies key decision points (tool call results, important user instructions, state changes), keeping only these keyframes and their context. These three strategies can be combined hierarchically: layer 1 keeps the latest 20 full turns, layer 2 keeps all past keyframes, layer 3 keeps a global summary.
In the Zero to Codex course, token budget management is core to the agent cost optimization chapter. Learners need to understand: tokens are not just cost — they are the resource constraint for agents to work effectively. The course provides a hands-on experiment: have an agent execute a long task requiring 50 steps, observing performance differences under different token budget strategies. The conclusion: simply expanding the context window (from 32K to 128K to 200K) doesn't solve the 'lost in the middle' problem — agents lose mid-sequence information in long contexts. The effective strategy is not expanding the window, but optimizing the content structure within it.