This is literally how Jev makes AI agents dramatically cheaper. It filters the noise from Slack, GitHub, documents, memory, and the web then sends Claude Code only the context it actually needs. Fewer tokens. Faster decisions. Lower costs. The context firewall is insane:

Filtering context at the ingestion layer is the real infra play here. Most agents still pump raw noise straight to the model.

this context firewall is actually so bullish for agent costs

The filtering risk is when cost drops but context loss breaks the task. Jev needs to know what NOT to filter, which is harder than what to keep.

My wallet thanks anyone who stops Claude from reading my entire Slack history

关键不只是省 token，而是先把上下文里的噪声挡掉了。

Filtering Slack, GitHub, and docs before Claude Code sees them is the cost win. Most agent loops still re-feed the same noisy thread every turn.

The filtering is the easy part; the hard part is deciding what counts as noise before you know what the task needs. Pre-filtering too aggressively is how agents miss the one Slack thread that actually mattered.

Outages like that kill an entire evening of focus.

Clever filter sure but anyone counting the tokens Claude Code *spits out*? That's the real cost sink.

A plausible second-order effect is that context filtering shifts effort from prompt writing toward building and maintaining reliable retrieval rules.

When the filter quietly drops context the model actually needed, there is no recovery signal. The agent just continues, confidently wrong. Took me longer than I'd like to admit to realize the filter was the failure point, not the model.

Consider filtering unnecessary noise from documents and web data to send your AI agents only the context they actually need.

Context firewall before the expensive coding model is the pattern. Most cost isn't "thinking hard." It's stuffing Slack/GitHub/docs into the prompt and hoping the model sorts signal from noise.