Agents introduce operational questions that ordinary application monitoring cannot answer. A request may succeed technically while selecting the wrong tool, using poor evidence, taking an unnecessarily expensive path or producing an unacceptable business outcome. Non-determinism also makes failures difficult to reproduce from a conventional error log.
AgentOps extends DevOps, SRE and LLMOps practices to these systems. It treats prompts, models, tools, policies, memory and evaluation datasets as versioned operational assets. It also joins traces, quality measures, costs and user outcomes so that teams can understand what an agent actually did.
This course gives participants a practical operating model for releasing, observing and continuously improving production agents without allowing experimentation to bypass governance.
Learning Outcomes
Upon completion of this course, participants will be able to:
- Define an AgentOps lifecycle spanning design, release, operation and improvement
- Evaluate agent tools, turns, sessions and end-to-end outcomes
- Build representative datasets and dependable evaluation criteria
- Instrument model decisions, retrieval, tool calls and state transitions
- Monitor quality, reliability, latency, security and cost together
- Diagnose non-deterministic and multi-step failures from traces
- Apply controlled release, rollback and incident-response practices
- Establish evidence and governance for continuous agent improvement
Course Outline
AgentOps as an Operational Discipline
- How AgentOps extends DevOps, SRE, MLOps and LLMOps
- Operational characteristics of non-deterministic agents
- Defining service objectives for quality as well as availability
- Ownership across engineering, product, security and domain teams
- Treating models, prompts, tools and memory as versioned artefacts
- Establishing a use-case and agent inventory
Designing an Evaluation Strategy
- Evaluating tools, individual turns, complete sessions and business outcomes
- Offline, pre-release, shadow and production evaluation
- Deterministic checks, model-based graders and human assessment
- Measuring task success, groundedness, policy compliance and efficiency
- Avoiding weak metrics and evaluator self-agreement
- Defining thresholds that support release decisions
Building Evaluation Data
- Collecting representative and risk-focused scenarios
- Creating source-of-truth cases with domain experts
- Including edge cases, adversarial inputs and tool failures
- Preventing contamination between development and evaluation sets
- Versioning datasets, rubrics and expected evidence
- Using production feedback without exposing sensitive information
Agent-Native Observability
- Tracing prompts, model calls, retrieval and tool execution
- Correlating distributed work across agents and services
- Recording plans, state transitions, approvals and side effects
- Metrics for latency, tokens, errors, retries and cost per outcome
- Protecting sensitive content in logs and traces
- Using open telemetry conventions and interoperable instrumentation
Diagnosing Agent Failures
- Separating model, context, retrieval, tool and orchestration failures
- Reconstructing non-deterministic execution from traces
- Detecting loops, repeated retries and unproductive tool use
- Identifying stale memory and permission-related failures
- Comparing successful and unsuccessful trajectories
- Turning incidents into regression cases
Release Engineering for Agents
- CI/CD for prompts, tools, policies and evaluation suites
- Pre-production integration, performance and security tests
- Model and prompt change impact analysis
- Canary, shadow and champion-challenger releases
- Rollback when dependencies or model behaviour change
- Preserving reproducibility across environments
Production Monitoring and Feedback
- Continuous sampling and evaluation of live traffic
- Quality drift, data drift and changing tool behaviour
- Alerting on business risk rather than metric noise
- Combining user feedback with observable evidence
- Monitoring guardrails, approval rates and denied actions
- Cost allocation and capacity planning
Governance and Incident Response
- Auditable action histories and evidence retention
- Runbooks for unsafe, incorrect or excessively costly behaviour
- Suspending tools, models or complete agent capabilities
- Roles for security, compliance and domain owners during incidents
- Post-incident review for probabilistic systems
- Controlled improvement and re-approval after material changes
Practical Capstone
- Instrument a representative multi-step agent
- Create a layered evaluation suite and release threshold
- Diagnose injected failures from traces and metrics
- Configure quality, reliability and cost monitoring
- Design a safe canary and rollback process
- Present an AgentOps dashboard and operating runbook
Intended audience
This course is designed for AI engineers, platform engineers, site reliability engineers, ML engineers, solution architects and technical leads responsible for agents in production. It is also relevant to risk and assurance professionals who need evidence that agent systems remain within approved boundaries.
Prerequisites
Those attending this course should meet the following:
- Experience building or operating an LLM or agent application
- Understanding of CI/CD, logging, metrics and distributed systems
- Familiarity with testing and production incident management
- Basic knowledge of model prompts, tool calling and retrieval