🏆SFEIR is the Google Cloud EMEA Training Partner of the Year 2025🤝New partnership: Official GitLab Training🤖New training: AI-Augmented Developer
🏆SFEIR is the Google Cloud EMEA Training Partner of the Year 2025🤝New partnership: Official GitLab Training🤖New training: AI-Augmented Developer
GCPSPARKSPERFENG

Performance Engineering for Autonomous Systems on Google Cloud Training

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.

Google Cloud
✓ Official training Google CloudLevel Intermediate⏱️ 0.5 day (3h)

What you will learn

  • In Module 1 to: Audit live traces to find exactly where and why an agent fails in production. (Analyze)
  • In Module 2 to: Fix slow response times and logic errors using context pruning and sharp tool schemas. (Apply)
  • In Module 3 to: Score agent safety and compliance using clear, quantitative metrics instead of guesswork. (Evaluate)
  • In Module 4 to: Build a master logbook of golden datasets to stop performance from degrading over time. (Create)
  • In Module 5 to: Evaluate understanding of core course concepts through scenario-based production questions (Assess).

Prerequisites

  • 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.
  • Completion of the "Agentic Infrastructure for the Autonomous Enterprise on Google Cloud" course is highly recommended.
  • Helpful to be familiar with: cloud perimeter guardrails and system safety layers; how workflows connect with external APIs and data retrieval loops; the Google Cloud SDK for Python; BigQuery basics for performance trend analysis; and basic forensic trace or logging methodologies.

Target audience

  • 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.

Training Program

5 modules to master the fundamentals

Objectives
  • Isolate Root Failures: Map runtime errors directly to the specific pillar of the Failure Quartet (Brain, Past, Hands, or Perimeter) causing the breakdown.
  • Diagnose Lost Reasoning: Perform forensic trace analysis to pinpoint the exact moment an agent deviates from its logical execution path.
  • Fix Latency Bottlenecks: Identify and eliminate the "Brute Force" fallacy where over-provisioning compute tokens destroys system response times.
Topics covered
  • →The Failure Quartet (Theory)
  • →The Latency Tax (The Constraint)
  • →Trace Analysis (The Tool)
Activities

1 Use Case, 2 Demos

Objectives
  • Cure Memory Drowning: Apply context pruning and token summarization to keep the agent focused strictly on relevant, high-value data.
  • Eliminate Execution Errors: Structure tool schemas and API documentation so the agent executes external code without logical leaps.
  • Lean Out Prompts: Use instruction distillation to shrink bloated, expensive system instructions into tight, deterministic runtime logic.
Topics covered
  • →Tuning the Past (Retrieval Optimization)
  • →Tuning the Hands (Tool-Call Refinement)
  • →Tuning the Brain (Instruction Distillation)
Activities

2 Use Cases, 2 Demos

Objectives
  • Replace Vibe Checks: Deploy quantitative safety scorecards to measure compliance using hard, repeatable metrics instead of guesswork.
  • Optimize Security Latency: Tune platform safety layers to achieve maximum data protection without causing user-facing lag.
  • Master Human-in-the-Loop: Deploy a confidence-scored intervention workflow to loop in a human only when the agent's logic score drops.
Topics covered
  • →RAI as a Performance Metric (Compliance)
  • →Tuning the Perimeter (Gateway Security)
  • →The Forensic HITL Stamp (Human Loop)
Activities

1 Use Case, 2 Demos

Objectives
  • Build a Performance Baseline: Construct a master logbook of "Golden Datasets" to serve as your absolute ground truth for testing.
  • Automate Defenses: Establish an automated feedback loop that flags and patches logical drift the moment an agent begins to degrade in production.
  • Prove Financial ROI: Translate technical telemetry into board-ready metrics that prove real corporate capacity gains against cloud infrastructure costs.
Topics covered
  • →The Pilot's Logbook (Golden Datasets)
  • →Continuous Optimization (CI/CO)
  • →The Performance Tuning Roadmap
Activities

1 Use Case, 2 Demos

Objectives
  • Evaluate understanding of core course concepts through scenario-based questions.
Topics covered
  • →Review of Core Concepts
Activities

5 scenario-based multiple choice questions

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Quality Process

SFEIR Institute's commitment: an excellence approach to ensure the quality and success of all our training programs. Learn more about our quality approach

Teaching Methods Used
  • Lectures / Theoretical Slides — Presentation of concepts using visual aids (PowerPoint, PDF).
  • Technical Demonstration (Demos) — The instructor performs a task or procedure while students observe.
  • Case Study — Analysis of a real or fictional business scenario to derive solutions.
  • Quiz / MCQ — Quick knowledge check (paper-based or digital via tools like Kahoot/Klaxoon).
Evaluation and Monitoring System

The achievement of training objectives is evaluated at multiple levels to ensure quality:

  • Continuous Knowledge Assessment : Verification of knowledge throughout the training via participatory methods (quizzes, practical exercises, case studies) under instructor supervision.
  • Progress Measurement : Comparative self-assessment system including an initial diagnostic to determine the starting level, followed by a final evaluation to validate skills development.
  • Quality Evaluation : End-of-session satisfaction questionnaire to measure the relevance and effectiveness of the training as perceived by participants.

Frequently Asked Questions

AIOps and MLOps engineers, AI and cloud platform architects, and AI or engineering operations leads: anyone responsible for operationalizing, hardening and financially justifying autonomous agent fleets.
Experience helping build the infrastructure for autonomous agents and comfort navigating cloud architectures. Completing Agentic Infrastructure for the Autonomous Enterprise on Google Cloud first is highly recommended.
You will audit live traces to find exactly where and why an agent fails in production, fix slow response times and logic errors using context pruning and sharp tool schemas, score agent safety and compliance with quantitative metrics, and build a logbook of golden datasets to stop performance from degrading over time.
It is the diagnostic framework used in the course to isolate production-level logic breaks. It evaluates execution traces across four pillars, the Brain, the Past, the Hands and the Perimeter, to eliminate the system Latency Tax.
The course lasts 3 hours and is delivered in an instructor-led format by SFEIR Institute. Each module relies on use cases and demos, and the course ends with scenario-based multiple choice questions.
Yes. This is an official Google Cloud Sparks course delivered by SFEIR Institute, a certified Google Cloud Training Partner.

395€ excl. VAT

per learner

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