Observability for AI Agents
If you’re an AI engineer, platform engineer, SRE, ML practitioner, or application developer who is already building or operating agentic AI systems and needs a clearer way to understand, debug, and trust their behavior in production, the Observability for Agentic AI Systems Skill Badge is for you. It’s designed for people who are comfortable with modern AI application architectures and want to go beyond basic monitoring to build a stronger observability foundation for agents that reason, retrieve context, call tools, and operate across multi-step workflows. By the end of the course, you will understand what it takes to make an agentic AI system observable in production, instrument your agent, interpret the signals it emits, and track down silent failures that never throw an exception. Once you complete the skill check, you will earn an Observability for Agentic AI Systems digital badge that you can share across your professional network as proof of your ability to build and reason about observability for production AI agents.
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Upon completion of the Observability for AI Agents skill and assessment, you will earn a Credly Badge that you are able to share with your network. |
Learning Objectives
Identify Silent Failure Modes
Describe the four major failure categories for agents: reasoning, tools, context and data, and infrastructure. Explain how errors compound across multi-step workflows.
Investigate Agent Behavior
Use logs, metrics, and traces and understand how they work together to support debugging and decision auditing.
Understand Observability Stack Architecture
Describe the four layers of the observability control plane, and understand how they allow visibility into an agentic AI system.
Who is this Course Good for?
If you’re an AI engineer, platform engineer, SRE, ML practitioner, or application developer who is already building or operating agentic AI systems and needs a clearer way to understand, debug, and trust their behavior in production, the Observability for Agentic AI Systems Skill Badge is for you. It’s designed for people who are comfortable with modern AI application architectures and want to go beyond basic monitoring to build a stronger observability foundation for agents that reason, retrieve context, call tools, and operate across multi-step workflows.
By the end of the course, you will understand what it takes to make an agentic AI system observable in production, instrument your agent, interpret the signals it emits, and track down silent failures that never throw an exception. Once you complete the skill check, you will earn an Observability for Agentic AI Systems digital badge that you can share across your professional network as proof of your ability to build and reason about observability for production AI agents.
What to Expect in this Course
You will start by drawing a clear line between monitoring and observability and by examining why that distinction matters more for agents than for traditional software services. You will explore what makes agentic systems difficult to debug in production, including variable behavior, dynamic tool selection, changing context, and workflows where a small issue early in the chain can distort everything that follows.
From there, you will work through the four major failure categories that shape agent behavior in practice: reasoning failure, tool failure, context and data failure, and infrastructure failure. You will see why these issues often lead to the same outward result, an incorrect answer, and why observability is necessary to identify the real cause.
The course then moves into the three pillars of observability: logs, metrics, and traces. You will learn what each one reveals, where each one falls short, and why agentic systems depend on all three. You will examine structured logging for tool calls and model interactions, metrics frameworks such as RED and USE, agent-specific signals such as token consumption and cost, and traces that reconstruct the path of a request from input to output.
You will also explore the observability control plane and the architecture behind a production observability stack, including how telemetry is generated, collected, stored, and analyzed. In the second half of the course, you will go deeper into distributed tracing and the challenges of operating observability at scale. You will walk through traces to identify retries, bottlenecks, rate limits, and low-confidence retrieval results, and you will examine how traces support silent error detection, decision auditing, and compliance work. The course closes with the practical realities of running observability for agents in production, including telemetry volume, instrumentation upkeep, alert fatigue, token and quality signals, and the growing role of emerging standards and platform design.
Summary of the Course
- Explain why monitoring alone is not enough for agentic AI systems in production and distinguish observability as the capability that helps teams investigate unexpected, silent failures.
- Identify the four major failure categories in agent workflows: reasoning, tools, context and data, and infrastructure. Explain how they can collapse into the same outward symptom of incorrect output.
- Describe how silent failures compound across multi-step workflows and why per-step visibility is a hard requirement for diagnosing root causes in agentic systems.
- Define logs, metrics, and traces in the context of agentic systems and explain what question each pillar answers, where its blind spots are, and why all three are required together.
- Use structured logs, RED and USE metrics, and trace attributes to investigate model behavior, tool calls, latency, token usage, and other key observability signals in production AI systems.
- Explain the four layers of the observability control plane: Application, Instrumentation and Collection, Storage and Processing, and Analysis, Visualization and Alerting. Describe the role each one plays in a production observability stack.
- Walk through distributed traces to pinpoint bottlenecks, retries, low-confidence retrieval, and other hidden failure points that logs or aggregate metrics alone may not reveal.
- Evaluate the real-world challenges of agent observability, including telemetry volume, instrumentation maintenance, alert calibration, signal correlation, token tracking, and quality evaluation.
- Recognize how OpenTelemetry and OpenInference help standardize instrumentation for agent-aware observability and support more portable, unified observability platforms over time.
Aaron Becker | Technologist, Education
Aaron Becker is a Technical Trainer, Instructional Designer, and Training Manager who has worked in the tech sector for over 13 years. Before joining the Curriculum team at MongoDB, Aaron worked in DevOps at CircleCI, creating their first Certification course (CircleCI Associate Developer) and leading a team responsible for creating and managing the educational content for CircleCI Academy for external/customer training, as well as CircleCI University for internal team member training.
Prior to that, Aaron worked in data protection, redundancy, and security at Carbonite, where he headed up the Training team, created and delivered ILT training courses for Carbonite's Mid-Market and Enterprise level products, and assisted over 150 employees in earning Microsoft certifications.
Aaron enjoys writing, performing, recording, mixing and mastering music, playing video games, and writing biographical text in the third person.
Your agent has been live for a couple of weeks. Requests are flowing. Dashboards look green.
However, a small but painful percentage of users are getting completely wrong answers. There are no errors, just confident, fluent, wrong answers.
You open your logs, and there’s nothing obviously broken. No errors, no crashes.
Where do you even start? Is the problem the model, a tool, the data you retrieved, or the infrastructure under it all?
Hi. I’m Aaron Becker, I’m a Curriculum Engineer at MongoDB. In this skill badge, we’re going to build the observability foundation you need to answer exactly those kinds of questions with confidence.
We’ll start by drawing a clear line between monitoring and observability, and discuss why that line matters so much more for agentic systems than for traditional services.
We’ll look at what makes agents uniquely tricky: non-deterministic behavior, dynamic tool selection, and multi-step workflows where a small mistake early on quietly poisons everything that follows.
From there, we’ll dig into the three pillars of observability: logs, metrics, and traces.
Each pillar answers a different question about your system, and none of them is optional if you want to debug agents in the real world.
We’ll also introduce the observability control plane: the layers of infrastructure that collect, process, store, and surface signals from your agent.
Then we’ll go deeper on distributed tracing, and how traces let you reconstruct exactly what happened inside a complex, multi-step request.
We’ll use real examples to show how traces help you surface silent errors, audit decisions, and meet compliance requirements.
Finally, we’ll examine both the core operational challenges and the current landscape of agent observability: managing telemetry volume and sampling, maintaining instrumentation and avoiding alert fatigue, and tracking token and quality signals. We’ll also discuss how emerging standards and platform design are helping teams address these challenges.
Luckily, many tools exist. So, we’ll survey the current observability tooling landscape for AI and agents, and sketch what an ideal observability stack should provide.
By the end of this course, you’ll understand what it takes to make an agentic AI system observable in production, not just in a demo.
You’ll be able to instrument your agent, interpret the signals that it emits, and track down silent failures that never throw an exception.
Most importantly, you’ll be able to build and reason about an observability foundation that makes your AI systems trustworthy, debuggable, and production-ready.
If you’re ready to learn more about observability and why it’s so important, you’ve come to the right place. I’ll see you in the next video!
