Google Agentic Case Study - Episode 6: Continuous Evaluation, Shadow Operations & Closed-Loop MLOps

This episode highlights the "day-2" operational pipeline: pre-deployment evaluation gates, shadow-mode testing, demographic fairness monitoring, and the continuous learning flywheel.

This episode details how to build and secure a HIPAA-compliant Healthcare Landing Zone using modern cloud security frameworks and automated infrastructure tools.

This episode covers the crucial safety mechanisms, deterministic validation gates, de-identification pipelines, and fallback strategies that protect clinical systems from failure.

This episode focuses on isolating individual microservices, prompt engineering system instructions, and leveraging concurrent execution to keep latency to a minimum.

This episode explores how to coordinate specialized AI agents, design multi-tier memory systems, and manage active session states without data race conditions.

This episode focuses on defining the administrative problem space, curating the "golden dataset," establishing clinical rules, and aligning with national healthcare interoperability mandates.

Deep dive into post-deployment monitoring and tracing for complex, multi-turn, multi-agent systems. Explain how to instrument agent code with OpenTelemetry GenAI semantic conventions (tracking span attributes like model, tokens, and temperatures). Detail how standard W3C traceparent headers must propagate across inter-agent calls, network boundaries, and Model Context Protocol (MCP) hops to create unified execution waterfalls in Cloud Trace. Finally, explain how to stream structured JSON logs via Cloud Logging sinks into date-partitioned, session-clustered BigQuery tables to analyze real-time execution latencies, semantic drift, and error rates.

Detail how to construct automated pre-production evaluation pipelines for non-deterministic agents. Walk through building an evaluation harness (e.g., tests/eval_agent_trajectory.py using pytest and Cloud Build) executed against static "golden datasets." Contrast three evaluation types: Pointwise (single-turn metric checks), Pairwise (using gemini-2.5-pro as a Model-as-a-Judge), and Trajectory Evaluation (auditing tool invocation sequences using graph edit distance). Focus on the exact mathematical thresholds required to block releases in CI/CD: tool_call_accuracy >= 0.95, trajectory_match_score >= 0.90, and groundedness_score >= 0.90.

Explain how to eliminate non-deterministic behavior when models interact with transactional enterprise systems. Detail the strict parameter tunings required: setting temperature=0.0, enabling greedy decoding (top_k=1), and enforcing strict JSON schemas on model outputs. Explain how to isolate functional, high-risk tools (like mathematics or database writes) from unstructured search and RAG tools to avoid plan-phase confusion. Describe the "AgentTool Wrapper Pattern," which wraps complex tool configurations inside isolated, specialized sub-agents with dedicated, clean interfaces.

Analyze the runtime semantic firewalls required to protect models from real-time exploits. Focus on the integration of Vertex AI Model Armor as an inline security gateway to detect and block prompt injections, jailbreaks, and sensitive data leaks (configuring the filter confidence level to BLOCK_LOW_AND_ABOVE). Explain how to integrate Cloud Sensitive Data Protection (DLP) templates to intercept, inspect, and redact sensitive outbound identifiers (such as PII, PCI, or credential patterns) in real-time before they are returned to the user or logged.