Course Overview
This course provides a forensic guide to performance engineering, shifting the operational focus from passive system monitoring to active, code-level tuning and debugging on the Google Cloud Agent Platform. Participants will learn to isolate production-level logic breaks using the Failure Quartet framework—evaluating execution traces across the Brain, Past, Hands, and Perimeter pillars to eliminate the system Latency Tax.
The course bridges the gap between subjective, "vibes-based" system checking and continuous, quantitative optimization. Learners will master advanced technical diagnostic methods, parallelized Governance DAGs, and the Pilot's Logbook of Golden Datasets required to maintain high-performing autonomous systems, ensuring absolute AI reliability while protecting corporate infrastructure ROI.
Who should attend
- AIOps / MLOps Engineers: Responsible for forensic diagnostics, tuning, and certifying agent performance within the Google Cloud Agent Platform.
- AI / Cloud Platform Architects: Tasked with engineering stable, scalable agent topologies and securing perimeter guardrails.
- AI / Engineering Operations Leads: Responsible for translating technical performance telemetry (like Logic Faithfulness) into business-capacity ledgers and boardroom-ready ROI metrics.
- Essentially, anyone who is responsible for operationalizing, hardening, and financially justifying autonomous agent fleets as high-value enterprise assets.
Prerequisites
To make the most of this training, a foundational understanding in a couple of key areas is assumed:
- Foundational agent building: Experience helping build the underlying infrastructure for autonomous agency.
- Systems architecture role: Comfort navigating cloud architectures, as the training moves quickly into forensic diagnostics.
- Note: Completion of the Agentic Infrastructure for the Autonomous Enterprise on Google Cloud (AIAEGC) course is highly recommended.
Helpful to be familiar with:
- Infrastructure and security: Familiarity with cloud perimeter guardrails and system safety layers.
- API and data orchestration: Understanding how workflows connect with external APIs and data retrieval loops.
- Google Cloud SDK for Python: Ability to read and interpret basic programmatic execution scripts.
- BigQuery basics: Understanding how to run basic queries for performance trend analysis.
- Systems troubleshooting: Familiarity with basic forensic trace or logging methodologies.