Operate Autonomous AI Agents in 10 Interactive Lessons
When AI moves from chatbot to autonomous agent, the agent becomes the actor — and operating it safely is a new discipline. Master agent observability, guardrails & policy-as-code, non-human identity, human-in-the-loop escalation, failure & rollback, cost & drift monitoring, and prompt injection defense. The deepest Agent Operations course on any platform, with step-by-step walkthroughs on every lab. No videos required.
The Complete Curriculum
10 lessons covering the full agentic operations lifecycle — from the threat landscape through observability, guardrails, identity, escalation, failure & rollback, cost & drift, security, and incident response. Each lesson includes interactive walkthroughs with real commands.
The Agentic Operations Landscape
Understand the shift from AI as a chatbot to AI as an autonomous agent that takes actions — the six pillars of Agentic Ops, why traditional MLOps doesn't cover agents, and how it maps to NIST AI RMF, NIST CSF, ISO 27001, and GDPR Article 22.
Agent Observability & Reasoning Traces
Instrument an autonomous agent to capture its full reasoning trace — every thought, tool call, argument, result, and decision — and build a trace explorer and replay tool for failed runs.
Guardrails & Policy-as-Code for Agents
Build the guardrail layer that evaluates every agent action before it executes — tool-use allowlists, action rate limits and budgets, content filters, and OPA policy-as-code rules that block unauthorized actions.
Non-Human Identity & Permissions
Govern the agent as a non-human identity with least-privilege credentials, just-in-time elevation, a full credential audit trail, and rotation/revocation procedures.
Human-in-the-Loop Escalation
Define when an autonomous agent must stop and hand off to a human — the escalation triggers, the review queue, the SLAs, and the feedback loop that turns denials into agent improvements.
Agent Failure & Rollback
Build the failure response for autonomous agents — real-time failure detection, blast-radius containment, undo mechanisms (transactional rollback, compensating actions, backup restore), and the agent incident playbook.
Agent Cost & Drift Monitoring
Keep the agent healthy over time — monitor token spend, detect behavior drift, run production evals against a golden set, alert on regressions, and ship an agent health dashboard.
Agent Security & Prompt Injection Defense
Defend the agent against the security threats unique to autonomous systems — direct and indirect prompt injection, tool abuse, credential theft, data exfiltration, and a full agent security red team.
Agent Incident Response & Post-Mortem
Respond to agent-caused incidents — from a runaway agent that spammed 2,000 customers to a prompt-injection-driven data exfiltration — with triage, the incident playbook, cross-team coordination, regulatory notifications, and a blameless post-incident review.
Agentic Operations Flashcards
Master the core concepts — the six pillars, the reasoning trace, the tool-use allowlist, policy-as-code, non-human identity, JIT elevation, escalation triggers, blast-radius containment, rollback, drift, the golden set, indirect injection, and the blameless review.
Real Tools & Techniques You'll Master
These are the exact observability, guardrail, identity, and incident-response techniques used by Agent Operations engineers in production — across NIST, ISO, and SOC 2 frameworks.
Prove Your Skills with Real-World Capstones
After completing the lessons, tackle 3 capstone projects: a production agent observability platform with trace replay, a full guardrail & policy-as-code system that survives a red team, and an agent incident response & rollback drill for a runaway agent that emailed 2,000 customers. Each capstone is a multi-step challenge with no answer keys — just like the real job.
View Capstone ProjectsFrequently Asked Questions
What is an AI Agent Operations Engineer and why is the role emerging now?
An AI Agent Operations Engineer operates autonomous AI agents safely in production — agents that don't just generate text but take real actions (send emails, modify records, make purchases, execute code). The role is emerging because companies are moving from 'AI as a chatbot' to 'AI as an autonomous worker,' and traditional MLOps doesn't cover what happens when the model becomes the actor. This course teaches the six pillars that fill that gap: observability, guardrails, non-human identity, human-in-the-loop escalation, failure & rollback, and cost & drift monitoring.
Is this the most in-depth Agentic Operations course available?
Yes. This is the deepest Agent Operations course on any platform — 10 lessons covering the complete agentic operations lifecycle: agent observability and reasoning traces, guardrails and policy-as-code, non-human identity and permissions, human-in-the-loop escalation, agent failure and rollback, cost and drift monitoring, agent security and prompt injection defense, and incident response with post-mortem. Every lab includes complete step-by-step walkthroughs with real commands.
How is this different from MLOps or LLMOps?
MLOps tracks model metrics — accuracy, latency, drift — for models that predict while humans decide. Agents break that model because the agent IS the actor. Agentic Ops adds what MLOps leaves open: an action audit trail (what did the agent do, in what order, with what arguments), tool-use allowlists (can this agent send emails? delete records?), blast-radius containment (if it goes wrong, what can it touch?), rollback (how do we undo it?), and cost ceilings (who approved the $400 in API spend?). This course covers what MLOps doesn't: the agent as an autonomous actor.
Do I need prior AI or DevOps experience?
Some familiarity with AI systems or platform engineering helps, but Lesson 1 starts with the fundamentals — the shift from chatbot to agent, the six pillars, and the lifecycle. Every lesson includes step-by-step walkthroughs written so a beginner can follow along, with the depth that an experienced professional needs.
What frameworks does this course map to?
NIST AI RMF (GOVERN for agent authorization, MEASURE for monitoring, MANAGE for incident response), NIST CSF (DE.CM for agent observation, PR.AC for least-privilege, RS for incident response), ISO 27001 (A.8 asset management, A.9 access control for agent identities, A.16 incident management), SOC 2 (CC6.6 logical access, CC7.2 monitoring, CC7.3 incident response), and GDPR Article 22 (automated decision-making restrictions). You'll learn to map Agentic Ops to controls you already run so it's auditable and sustainable.
Is this course free?
Yes. The AI Agent Operations Engineer course is available on the free Initiate tier. You can start Lesson 1 immediately — no credit card required.
Will this help me get a job in agentic operations or AI reliability?
Yes. As companies deploy autonomous agents, they're hiring Agent Operations Engineers, Agent Reliability Engineers, and AI Platform SREs. This course teaches the exact skills those roles require: observability, guardrails, identity, escalation, rollback, drift monitoring, security, and incident response. Combined with the resume builder and blockchain-verified credentials, you'll arrive at interviews with real, demonstrable knowledge — and a capstone that proves you can operate an agent in production.
Ready to Operate Autonomous Agents Safely?
Start your first lesson today — free. Every completed lab earns a blockchain-verified Proof-of-Hack credential employers can verify instantly.
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