Context compression is useful, but I stopped trusting the “same answers, fewer tokens” pitch after watching an agent lose one field name and make the
I used to hear the pitch for context compression and think: sure, makes sense.
Smaller prompts. Lower latency. Lower cost. Same output quality.
Then I watched an agent blow a perfectly good debugging session because one field name disappeared from compressed memory.
That changed my opinion fast.
Three hours into a Claude Code run, the agent made the wrong API call with full confidence. The plan looked coherent. The reasoning looked clean. The summary of prior steps sounded smart.
I avoided MCP from day one. No schema overhead, no token tax. The agent called my GraphQL API...
Your AI coding agent reads its instruction files on every session start. CLAUDE.md, steering files,...
The same agent, same prompts, did markedly different work on two codebases I work in. One has a test...
The model was not the problem. One layer of my agent was quietly doing three jobs at once, and it cost me nights. Here is the…
Every time I started a new agent session, I was re-explaining the same things. The architecture...
Context engineering matters for two reasons: reliability and cost. If your agent's context window is...
