Monitoring And Maintaining Agent Performance

Learn to monitor, optimize, and scale AI agent performance with real-world frameworks, tools, and best practices

What you'll learn

Design and implement performance monitoring frameworks for AI agents

Set up telemetry pipelines to track latency, cost, and success metrics

Detect regressions, anomalies, and ethical risks in agent outputs

Apply continuous optimization techniques using logs, A/B tests, and dashboards

Requirements

Basic understanding of AI concepts or software systems is helpful

Description

Are you building, deploying, or managing AI agents and want to ensure they operate at peak performance? Monitoring and Maintaining Agent Performance is the comprehensive course designed to give AI engineers, MLOps professionals, system architects, and product managers the skills they need to monitor, optimize, and continuously improve AI-driven systems.In this course, you’ll learn how to design performance monitoring frameworks tailored for AI agents, from single-task tools to complex multi-agent workflows. We’ll cover how to track essential metrics such as latency, cost, token usage, success rates, and hallucination frequency. You’ll discover how to implement telemetry pipelines using tools like OpenTelemetry, Prometheus, Grafana, and Weights & Biases to collect, visualize, and act on performance data.The course guides you through detecting and addressing anomalies, regressions, and silent failures—helping you ensure reliability, resilience, and ethical compliance. You’ll learn practical techniques for continuous improvement, including log analysis, A/B testing, and prompt optimization. With real-world case studies inspired by enterprise deployments (e.g., IntelliOps AI Solutions), you’ll gain insights into scaling agent systems without sacrificing quality or control.By the end of this course, you’ll have the knowledge and templates to design a complete monitoring plan for your own agents, supporting cost efficiency, security, and long-term performance. Whether you’re working on internal tools, customer-facing assistants, or large-scale agent frameworks, this course will equip you with the tools and techniques to succeed.

Overview

Section 1: Introduction

Lecture 1 Introduction

Lecture 2 Use Case Company - IntelliOps AI Solutions

Section 2: Monitoring and Maintaining Agent Performance

Lecture 3 Introduction to Agent Performance

Section 3: Core Performance Metrics for Agents

Lecture 4 Core Performance Metrics for Agents

Section 4: Monitoring Infrastructure and Telemetry Pipelines

Lecture 5 Monitoring Infrastructure and Telemetry Pipelines

Section 5: Cost Management and Optimization

Lecture 6 Cost Management and Optimization

Section 6: Reliability and Resilience in Agentic Systems

Lecture 7 Reliability and Resilience in Agentic Systems

Section 7: Quality Assurance and Regression Detection

Lecture 8 Quality Assurance and Regression Detection

Section 8: Observability in Multi-Agent Environments

Lecture 9 Observability in Multi-Agent Environments

Section 9: Security, Privacy, and Ethical Monitoring

Lecture 10 Security, Privacy, and Ethical Monitoring

Section 10: Continuous Improvement and Optimization

Lecture 11 Optimization and Final Project

Section 11: Final Project and Conclusion

Lecture 12 Conclusion

AI engineers, MLOps professionals, system architects, and product managers seeking to monitor and optimize AI agent performance