Codex vs Claude Code Token Management: Different Context Consumption Patterns
Explains how token-management strategy should be adjusted to the different context-consumption patterns of Codex and Claude Code while keeping the same harne...
Token Management
Archive of posts about token management, context design, working-state summaries, and compression strategy.
This archive collects posts about where tokens are really spent in agent systems, what state should remain live, and what should be compressed or moved out of the working context. The focus is on context control and execution stability rather than simple cost-saving tips.
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- Token Management 01. Why Token Management Matters in Harness Engineering
- Token Management 02. Shared Problems in AGENTS.md, CLAUDE.md, and System Prompts That Burn Tokens
- Token Management 03. Long Logs, Long Plans, Long Memory: Why Agent Context Keeps Growing
- Token Management 04. How to Design State Summaries That Save Tokens
- Token Management 05. Good Compression and Bad Compression: What to Keep and What to Drop
- Token Management 06. How Token Management Strategies Differ Between Codex and Claude Code
Agent Context Compression Criteria: What to Keep and What to Drop
Explains that token savings are not about deleting as much as possible, but about preserving control information while raising information density and priori...
Working State Summary Design: Save Tokens Without Losing Task Context
Explains how to replace full history with a short working-state summary that directly controls the current task.
Agent Context Bloat: How Long Logs, Plans, and Memory Create Risk
Explains why logs, plans, memory, and prior conversation history often bloat agent context and weaken execution quality.
AGENTS.md and CLAUDE.md Token Waste: Shared Problems in Long Instruction Files
Explains why AGENTS.md, CLAUDE.md, and system prompts often bloat into structures that waste tokens and weaken execution quality.
AI Agent Token Management: Context Stability Matters More Than Cost
Explains token management as a stability, latency, and context-control problem in agent systems, not just a cost issue.