Operational AI Incident Mastery: Handling LLM & Agent Failures in Live Environments

What you will learn:

Implement robust instrumentation for LLM and agent architectures to ensure comprehensive visibility into incident signals, including prompt interactions, generated outputs, tool invocations, data retrievals, and operational costs.
Master the critical first five minutes of AI incident triage, accurately assessing severity based on impact scope, autonomy level, data sensitivity, and incident reversibility.
Effectively contain uncontrolled AI agents without escalating issues, utilizing strategies such as termination, rate limiting, credential revocation, system rollbacks, and isolation techniques.
Proficiently apply eleven distinct operational playbooks tailored to address common LLM and agent failure patterns.
Securely gather and preserve digital evidence, enabling precise root cause analysis for AI incidents that defy simple reproduction.
Safely restore operational stability by eliminating compromised states, gradually reintroducing services, and implementing rigorous evaluation gates to validate fixes before full re-enablement.
Conduct impartial, model-aware post-incident reviews and establish a mature, proactive AI incident response capability within your organization.

Description

This program delves into the operational aspects of managing artificial intelligence.

Picture this: It's the dead of night. Your sophisticated AI agent has autonomously dispatched critical communications to thousands of clients, without any prior approval. Your monitoring tools show everything is optimal – zero errors, perfect uptime, low latency. All indicators are green, yet a major incident is unfolding. This deceptive calm is precisely the challenge we address.

This is a practical operations course, not a theoretical deep dive into governance, red teaming, or architectural design. You are the one on call, responding to the urgent alert. Our focus is on equipping you to effectively identify, assess, mitigate, preserve crucial data, investigate root causes, and restore stability following failures in deployed Large Language Model (LLM) and autonomous agent systems. The curriculum is structured around distinct failure modes, ensuring practical applicability over abstract frameworks.

Every scenario you'll tackle is set within the immersive environment of Meridian Health, our fictional insurance provider. Here, you'll manage an LLM-powered claims assistant and an autonomous operational agent with direct access to a claims database, external email capabilities, and an internal MCP server. Each incident you learn to resolve originates first within Meridian’s operational context.

Distinguishing Features of This Course:

Built upon the robust foundations of the Coalition for Secure AI (CoSAI) AI Incident Response Framework V1.0 – the leading authoritative guide in this emerging field – and transformed into actionable, operational procedures.
Features eleven clearly defined incident playbooks, each adhering to a consistent, comprehensive structure: identifying signals, executing containment, gathering evidence, implementing eradication, and validating recovery gates.
Incorporates critical insights from real-world events, including a landmark Canadian tribunal ruling that dismissed the "AI chatbot as a separate legal entity" defense, and documented cases of runaway operational costs from 2026.
Provides dedicated, in-depth coverage of cost-related incidents – a critical yet often under-developed area even within established frameworks, and a prominent failure mode observed in 2026.

Upon completion, you will possess five tangible resources ready for immediate deployment in your organization: a tailored AI incident severity assessment matrix, stack-specific containment protocols, a personalized incident response playbook, an evidence collection checklist complete with chain-of-custody guidelines, and a generative model-aware post-incident review template. Your final capstone project integrates these five assets with a comprehensive gap analysis and a strategic 90-day implementation roadmap.

Nine immersive, hands-on laboratory exercises are conducted entirely on your local machine, utilizing a local model – eliminating the need for API keys or cloud expenditures. You will gain practical experience instrumenting an agent, quantitatively scoring live incidents, performing a timed containment drill, meticulously reconstructing complex incidents from raw telemetry, and rebuilding an evaluation gate that strictly prevents redeployment until the fix is unequivocally verified.

No prior experience in developing attacks is required. Every incident presented in the labs is already in progress when you encounter it. Your role is solely that of the incident responder, never the aggressor.

Curriculum

Module 1: Foundations of AI Incident Response for Production Systems

This introductory module sets the stage for understanding the unique challenges of AI incident response in live production environments. We'll explore why traditional monitoring often fails to detect AI-specific issues, introduce the critical operational mindset required, and dive into the immersive 'Meridian Health' case study that will serve as our ongoing simulation. You’ll also get an overview of the industry-leading Coalition for Secure AI (CoSAI) AI Incident Response Framework V1.0, establishing a robust theoretical foundation for the practical skills you'll develop.

Module 2: Detection & Triage – Unmasking Hidden AI Failures

Learn to instrument your LLM and agent stacks effectively, ensuring comprehensive visibility into critical signals such as prompt inputs, model completions, tool calls, data retrievals, and operational costs. This module focuses on the art of initial incident triage, teaching you to assess the severity of an AI incident within the crucial first five minutes. You will master scoring incidents based on their blast radius, degree of autonomy involved, data classification, and the reversibility of the damage, enabling rapid and informed decision-making.

Module 3: Containment Strategies – Controlling Rogue LLMs and Agents

This module equips you with advanced techniques to contain misbehaving AI agents and LLM systems without exacerbating the situation. Explore a range of containment actions including graceful termination, aggressive throttling, credential revocation, system rollbacks, and network isolation. You'll delve into the containment phases of eleven distinct, named playbooks, each designed to address specific LLM and agent failure modes like prompt injection, tool abuse, agent loops, and runaway costs.

Module 4: Investigation & Root Cause Analysis: Unraveling Complex AI Incidents

Master the critical process of preserving evidence in AI incident scenarios, ensuring forensic integrity for subsequent analysis. This module teaches you how to meticulously reconstruct root causes on systems where incidents may not reproduce on demand, a common challenge in complex AI environments. You will learn to gather and analyze logs, telemetry, and system states to understand the chain of events leading to a failure, even in non-deterministic systems.

Module 5: Recovery & Remediation: Restoring Stability and Trust

Learn safe and effective strategies for recovering from AI incidents. This includes purging poisoned or compromised states, progressively restoring services to minimize further risk, and implementing rigorous evaluation gates. You will gain expertise in verifying that fixes are unequivocally proven before full re-enablement of AI capabilities, ensuring long-term stability and preventing recurrence of critical failures.

Module 6: Building an AI Incident Response Program & Review

This module focuses on establishing and refining your organization's AI incident response capabilities. You will learn to conduct blameless, model-aware post-incident reviews that foster learning and continuous improvement without assigning blame. The course culminates in helping you stand up a comprehensive AI incident response program, utilizing the five practical artifacts developed throughout the course: an AI incident severity matrix, containment procedures, an incident playbook, an evidence checklist, and a model-aware post-incident review template.

Module 7: Hands-on Labs & Capstone Project: Practical Application

Engage in nine intensive, hands-on lab sessions conducted entirely on your local machine using a local model, ensuring a zero-cost, private learning environment. These labs provide practical experience instrumenting an AI agent, scoring live incidents, performing a timed containment drill under pressure, meticulously reconstructing incidents from raw evidence, and rebuilding an evaluation gate that enforces strict fix validation. The capstone project integrates all learned concepts and artifacts, guiding you to create a personalized AI incident response program, a gap statement, and a 90-day roadmap for implementation within your own operational context.

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