The Production AI Systems Engineering Course — Build, Deploy, Operate

Production AI systems engineering is the application of software engineering discipline to AI systems: treating reliability, observability, and maintainability as requirements that must be designed for rather than properties that emerge naturally. It matters because AI systems that are not engineered for production fail in predictable ways (context overflow, hallucination, coordination failures, and inability to diagnose problems) that become expensive and damaging at real-world scale. Engineering discipline prevents these failures.

For multi-agent systems, production engineering principles apply at every level: architectural design using the Glass-Box Context Engine pattern (reliability), MCP-based typed communication (testability), semantic blueprint versioning (maintainability), Glass-Box logging (observability), and incremental deployment with backward-compatible schemas (deployability). Each engineering discipline maps directly to specific architectural decisions in the multi-agent system design.

The testing pyramid for production AI systems has four layers: unit tests at the base (testing individual components like the blueprint generator and context router with mocked LLM responses), integration tests (testing component interactions with controlled test LLM responses), system tests (testing complete agent workflows end-to-end with a realistic test environment), and adversarial tests at the top (testing safeguards and failure handling under intentionally challenging inputs). The workshop covers implementing tests at every layer.

Continuous integration for production AI systems runs the test pyramid automatically on every code change: unit tests and integration tests on pull requests (fast feedback), system tests and golden tests on merge to main (comprehensive verification), and adversarial tests on a scheduled basis (safeguard effectiveness verification). The Glass-Box logging provides the ground truth for what the system actually did during CI runs, making test failures informative rather than opaque.

A production AI system operational runbook covers: health check verification procedures (how to verify all MCP servers are healthy), common failure diagnosis steps (how to use Glass-Box traces to diagnose specific failure patterns), incident escalation procedures (when to engage senior engineers or model providers), scheduled maintenance procedures (how to update LLM versions, reindex RAG knowledge bases, and archive episodic memory), and change management procedures (how to deploy semantic blueprint updates without disrupting active sessions).

Model API changes from providers are handled through abstraction: the LLM client layer in the Glass-Box Context Engine wraps the provider API, so provider-specific changes are isolated to one component. Schema changes to model APIs are caught by integration tests before deployment. Model capability changes (new features, deprecated parameters) are detected through the Glass-Box monitoring layer that tracks which model API features are actually used in production, enabling proactive migration planning.