AgentOps & Governance Framework for Enterprise AI Systems
Duration
2 Day
Level
Intermediate to Advanced Level
Design and Tailor this course
As per your team needs
Overview
This 2-day Intermediate to Advanced program focuses on AgentOps — the operational discipline of managing, monitoring, evaluating, and governing AI agents in production environments. The training equips participants with frameworks, architecture patterns, governance controls, and observability strategies necessary to deploy and scale agentic AI systems responsibly and reliably.
Participants will learn how to operationalize LLM-powered agents, implement monitoring pipelines, detect drift and hallucinations, enforce governance policies, optimize cost, and establish enterprise-ready AI operating models.
The program emphasizes real-world architecture thinking, risk management, and production-grade implementation strategies.
Audience
- AI/GenAI Engineers
- ML Platform Engineers
- MLOps Engineers
- Enterprise Architects
- AI Governance Leads
- Responsible AI Teams
Prerequisites
- Strong understanding of LLM/Agentic AI systems
- Experience deploying AI applications
- Familiarity with APIs and cloud infrastructure
- Basic knowledge of MLOps concepts
Curriculum
Introduction to AgentOps
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What is AgentOps?
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Difference between MLOps and AgentOps
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Unique challenges of agentic systems
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Deterministic vs non-deterministic systems
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Operational lifecycle of AI agents
Agent Observability Framework
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Logging agent decisions
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Tool invocation tracking
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Conversation trace capture
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Token usage monitoring
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Latency measurement
Evaluation & Quality Assurance
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Task success metrics
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Automated evaluation pipelines
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Human-in-the-loop validation
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Hallucination detection
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Prompt injection detection
Drift & Risk Monitoring
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Data drift vs behavior drift
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Model degradation detection
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Safety monitoring
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Escalation workflows
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Incident response planning
Hands-on Labs
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Implement agent logging system
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Build evaluation dashboard
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Simulate failure scenario
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Implement automated evaluation workflow
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Analyze token & latency metrics
Governance Framework for Agentic Systems
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AI policy design
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Role-based access control (RBAC)
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Prompt governance
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Model approval workflows
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Audit logging & compliance
Security Architecture
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Secure tool invocation
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Context isolation
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PII protection
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Data residency considerations
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Authentication & authorization patterns
Cost & Performance Optimization
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Token optimization strategies
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Caching layers
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Concurrency management
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Rate limiting
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Cost monitoring dashboards
Enterprise Agent Operating Model
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AI Center of Excellence
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Governance committee structure
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Risk classification framework
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Change management
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KPI & ROI tracking
Capstone: Enterprise AgentOps Blueprint
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Define governance policy
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Design observability architecture
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Implement monitoring & risk controls
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Create incident response framework
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Present scalable AgentOps strategy
Upon completion, participants will be able to:
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Design a comprehensive AgentOps framework
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Implement observability and evaluation pipelines
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Detect and mitigate hallucinations and risks
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Enforce enterprise governance controls
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Optimize cost and performance of AI agents
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Establish a scalable AI operating model
Duration
2 Day
Level
Intermediate to Advanced Level
Design and Tailor this course
As per your team needs
