This 3-hour hands-on course teaches teams how to safely run autonomous AI in production. Learn AgentOps principles, reliability patterns, and automation pipelines to control cost, enforce policy, and prove compliance. Designed for engineers, SREs, and platform teams scaling agentic systems. Based on AEBOP T4.1.
AEI members save 20% with code MEM_C18_20.
Most AI agents fail not from poor reasoning but from operational gaps—unbounded loops, untracked retries, and invisible cost explosions. This module reveals why traditional DevOps fails for probabilistic systems and introduces AgentOps as the discipline for running autonomous systems safely in production.
You'll learn to diagnose your organization's operational maturity using the AgentOps Maturity Ladder and build cross-functional readiness across SRE, ML, compliance, and finance teams. Practical exercises include mapping current handoff gaps and creating accountability structures that circulate rather than cascade downstream.
Autonomous systems require different operational patterns than deterministic software. You'll master five core design patterns including Supervisory Loops, Bounded Autonomy, and Progressive Fallback, while learning to avoid common anti-patterns like Retry Hell and Silent Success that plague production deployments.
Reliability in agentic systems means predictable behavior under uncertainty, not just uptime. We'll engineer reliability through checkpointing, budget-aware execution, and verifiable recovery—transforming operational concepts into executable control mechanisms using policy-as-code and evidence-based verification.
Bridge the four critical tooling gaps in today's observability stack: semantic logging, cross-layer root cause analysis, operational optimization, and safe automation. You'll implement reasoning trace schemas, multi-layer correlation IDs, and a three-tier recommendation layer that turns alerts into prescriptive actions.
Build the complete AgentOps automation pipeline—from semantic instrumentation through supervised automation—with hands-on implementation of policy-as-code, supervisor agents, and daily operational rhythms. Learn to establish SLAs for detection and rollback time while implementing weekly rituals that transform reactive firefighting into proactive governance.
Module 4 tackles the enterprise reality of scaling autonomous systems from isolated prototypes to organization-wide operations. You'll learn practical patterns for managing multiple agents, including policy federation for consistent control, resource coordination to prevent contention, cross-agent incident protocols, and unified telemetry. We cover the operational architecture needed to maintain reliability, security, and compliance when dozens of specialized agents interact across teams, focusing on portfolio-level management rather than individual agent metrics.
The module then shifts to learning from real-world failures through documented anti-patterns and field lessons. You'll learn to recognize and remediate the five most critical operational anti-patterns that break production systems, implement evidence-driven recovery processes, and establish daily operational rhythms that prevent repeated incidents. This final segment transforms incident post-mortems into proactive organizational learning, ensuring your team doesn't just build autonomous systems but sustains them reliably at scale.