Course 4 (MCP Security & Authentication)
closed with a single lesson on rate limiting and cost control: a transient-based
counter, wired into a permission_callback, enough to prove the pattern works. Course 8
(Advanced WordPress MCP Architecture)
closed with a single lesson on performance under agent traffic: caching a readonly
ability’s execute_callback with the object cache, and spotting N+1 queries. Both were
deliberately brief, one lesson each, in courses that had other ground to cover. This
course is what happens when those two lessons stop being a footnote and become the
whole job.
This is a full production operations course for AI features running at real scale: real traffic, real token bills, real background load, and a real need to know what’s actually happening in production rather than guessing. Nothing here repeats Course 4’s counter or Course 8’s cache example verbatim. Instead, this course builds past both: caching AI-generated content correctly (not just ability results), invalidating that cache when source content actually changes, logging and reviewing token cost in a dashboard you build yourself, routing between cheaper and stronger models on purpose, moving slow AI calls out of the request cycle entirely with Action Scheduler, rate-limiting per-user and site-wide traffic with graceful degradation instead of a hard wall, and monitoring cost and latency together instead of as two separate, disconnected concerns.
If you haven’t taken Course 4 or Course 8, this course still stands on its own, it assumes general familiarity with the PHP AI Client SDK and the MCP Adapter from Courses 1 through 3, not those two specific lessons. But if you have taken them, treat this as the deeper version: same starting concepts, taken all the way to what an actual production deployment needs.
By the end of this course you’ll have a caching layer that knows the difference between AI output that’s safe to reuse and output that must always be fresh, cache invalidation tied to real content-change hooks instead of hopeful TTLs alone, a working cost log and admin dashboard, a deliberate cheap-first model routing strategy, background job processing for anything slow, production-grade rate limiting with real fallback behavior, and a monitoring setup that ties cost and latency to the same event instead of tracking them in two places that never talk to each other.