Autonomy multiplies cost. An agent task fans out into many model and tool calls, and a striking share are semantic duplicates — retries, reasoning loops, multi-agent fan-out, and repeated tool/MCP calls for unchanged data. Exact-match caching never fires on this traffic, so the model and server recompute every time, paying twice in tokens, latency, and non-determinism.

This talk presents semantic caching for agent and MCP systems: key on meaning, not bytes. Embed the request, find the nearest prior entry, and — if close enough and verified equivalent — return the stored result. The hard part is safety: similarity alone serves wrong answers, so a verification gate is mandatory, and the key must carry identity, scope, and freshness or the cache leaks data and serves stale results.

We show where to place the cache — transparent middleware in front of models and MCP servers, a caching MCP proxy with context-aware keys — how to guard it, and how to evaluate both savings (hit rate, latency) and safety (false-hit, staleness, leakage) in shadow mode before it serves a user.

You leave with a safe cache design, an MCP-native placement, and an evaluation method for any agent stack.