What you'll learn
Design and implement identity, policy, and observability controls that govern autonomous AI agents in production enterprise environments
Build OpenTelemetry-based tracing and evaluation pipelines that provide auditor-grade evidence of every agent action, tool call, and decision
Architect zero-trust agent sandboxes with scoped credentials, runtime circuit breakers, and automated rollback for safe production deployment
Plan and execute governed legacy system modernization using agentic discovery, characterization testing, and progressive migration with rollback
Operate a centralized governance control plane with policy-as-code (OPA/Cedar), shared telemetry schemas, and multi-agent delegation governance
Requirements
Working knowledge of software engineering and cloud-native infrastructure (containers, CI/CD, APIs, microservices)
Basic familiarity with large language models and how LLM-based applications work (prompt, response, tool calling)
Architect zero-trust agent sandboxes with scoped credentials, runtime circuit breakers, and automated rollback for safe production deployment
Description
This course contains the use of artificial intelligence. AI agents are moving from demos to production. They call tools, access data, make decisions, and take actions autonomously. Most teams ship them with application-level guardrails and hope for the best.This course teaches you to govern AI agents as runtime infrastructure - not as chatbots with extra steps.You will learn to build the production control plane that enterprises, SaaS platforms, and regulated industries require before an autonomous agent touches real users, real data, or real money.WHAT MAKES THIS COURSE DIFFERENT:This is not a prompt engineering course. It is not a framework tutorial. It is the engineering discipline that sits between "the agent works in staging" and "the agent is trusted in production."Every lesson is built around a single running scenario - a fictional enterprise insurer called Thornfield - so you see how each control (identity, tracing, evaluation, policy, approval, rollback) works in a realistic system with real constraints.WHAT YOU WILL BUILD (CONCEPTUALLY):- A zero-trust identity model where every agent has scoped, short-lived credentials and no ambient authority- An OpenTelemetry-based observability pipeline that captures full agent trajectories - every LLM call, tool invocation, retrieval, and decision - as structured, queryable traces- A continuous evaluation framework with offline test suites, online monitors, and human-in-the-loop escalation- Policy-as-code enforcement using OPA/Rego and Cedar that gates agent actions at runtime, not just at review time- Approval workflows for high-stakes agent actions with named human accountability- Incident response procedures designed for agents that keep acting while broken- Compliance mapping that binds EU AI Act, ISO 42001, SOC 2, and model risk obligations to technical controls and machine-verifiable evidence- A governed legacy modernization program using agentic code discovery, runtime telemetry, characterization testing, and progressive migrationCOURSE STRUCTURE:Module 1: Why agents need governance (the problem space)Module 2: Identity and access control for agentsModule 3: Observability - tracing agent behavior end to endModule 4: Evaluation frameworks - testing agents continuouslyModule 5: Human oversight and approval architecturesModule 6: Production operations - incidents, drift, releases, ownershipModule 7: Governance at scale - control planes, policy, compliance, multi-agent systemsModule 8: Legacy modernization with AI agents (integrative case study)Each module contains 5 lessons. The course follows a single enterprise scenario throughout, so controls build on each other rather than being taught in isolation.WHO THIS COURSE IS FOR:- Platform engineers deploying agents to production- SREs who will operate and monitor agent systems- Security engineers who need audit trails and policy enforcement- Engineering leaders making build-or-buy decisions for agent infrastructure- Compliance teams mapping AI regulations to technical evidence- Architects designing multi-agent enterprise systemsWHAT THIS COURSE IS NOT:- Not a prompt engineering or LLM fine-tuning course- Not tied to a specific framework (LangChain, CrewAI, AutoGen, etc.)- Not a beginner AI/ML course - you should understand how LLM-based applications work- Not a vendor pitch - concepts are tool-agnostic and apply across cloud providers and agent frameworksThe course is designed for engineers and technical leaders in enterprise, SaaS, fintech, and regulated industries who need to ship AI agents that are observable, governable, auditable, and safe.
Platform engineers and SREs building infrastructure to deploy, monitor, and govern AI agents in production,Engineering managers and tech leads responsible for shipping AI agent features safely in enterprise or regulated environments,Security and compliance engineers who need to audit, control, and produce evidence for AI agent systems under SOC 2, ISO 42001, or EU AI Act,Solutions architects designing multi-agent systems, LLMOps pipelines, or AI-powered automation for enterprise and SaaS products



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