AgentOps for Private LLM Systems
AgentOps for Private LLM Systems
By the end of this course, students will run AI agents as production systems instead of demos — stateful, resumable workflows on a private LLM, with sandboxed tools, least-privilege permissions, human approval gates, and full tracing. They will measure agent quality with task-success, tool-selection, and trajectory metrics, catch regressions automatically before release, and harden runtimes against runaway loops, hung tool calls, and token and GPU cost blowouts.
What you'll learn
Decide when a deterministic workflow beats an autonomous agent — and design either one on private LLM endpoints with model routing and fallback strategies- Build stateful agents with durable execution — checkpointing, persistence, resumable workflows, and short-term, long-term, and organizational memory
- Define clean tool contracts and expose tools through the Model Context Protocol (MCP) so agents, services, and clients share one interface
- Secure tool execution with least-privilege permissions, secrets and credential isolation, sandboxing and execution boundaries, and human-in-the-loop approval gates
- Defend against prompt injection and tool abuse using input, output, and action guardrails, data-access policies, and audit trails
- Instrument agents end to end — traces of decisions, tool calls, and state transitions — and evaluate them with task-success, tool-selection, trajectory, and response-quality metrics using LLM-as-judge and rule-based checks over regression datasets
- Harden the runtime: queues and concurrency control, timeouts, retries, idempotency, circuit breakers, loop detection, workflow replay, versioning of prompts, tools and models, plus rollout, rollback, and token/latency/GPU budgets
This course includes:
Course requirements
Production MLOps or backend engineering experience — Python, APIs, containers, and async work are assumed, not taught- Access to a private or self-hosted LLM endpoint (vLLM, Ollama, or similar). Course 1 covers this, but it isn't required — any OpenAI-compatible endpoint works for the labs
- Some prior LLM application work — prompting, function or tool calling, or basic RAG. No agent framework experience needed
- Comfortable with Git and debugging distributed services from logs and traces
Description
The agent worked in the demo. Then it ran a tool twice, looped for forty minutes, and nobody could explain what it did or why.
That gap — between an agent that works once and one you can put in front of users — is what this course closes. Not prompting tricks. The engineering underneath: state that survives a crash, tools that can't do damage, evaluation that catches regressions before release, and a runtime that fails safely instead of expensively.
Over four four-hour sessions you'll build and harden a governed agent on a private LLM.
Across the month you will:
Design stateful agents with durable execution — checkpointing, resumable workflows, memory architecture, model routing and fallback
Define clean tool contracts and expose them through MCP
Secure execution: least-privilege permissions, credential isolation, sandboxing, human approval gates, and defenses against prompt injection and tool abuse
Make agent behaviour visible and measurable — traces, task-success and tool-selection metrics, regression datasets, LLM-as-judge and rule-based evaluation in an automated pipeline
Harden the runtime — queues, timeouts, idempotency, circuit breakers, loop detection, workflow replay, versioning, rollout and rollback, and token/latency/GPU budgets
Every session ends with a lab that feeds the applied project, so the agent gets harder to break each week.
You'll finish with a private LLM agent running real tools under permissions and approval controls, tracing and evaluation dashboards with automated regression scenarios, and an operating runbook — plus the judgment to know which actions should never be autonomous.
Who is this course for
Engineers who've shipped an LLM feature and are now being asked for an agent — where the thing has to hold state, call real tools, and be trusted with actions that have consequences
Backend and platform engineers who own the systems the agent will touch — the database, the CRM, the payment flow — and need permissions, sandboxing, and approval gates in place before anything autonomous gets near them
AI engineers and technical leads whose agent works in the demo but not in production — looping, retrying side effects, failing in ways nobody can explain afterwards, with no way to tell whether last week's prompt change made it better or worse
Who is this course for
Engineers who've shipped an LLM feature and are now being asked for an agent — where the thing has to hold state, call real tools, and be trusted with actions that have consequences
Backend and platform engineers who own the systems the agent will touch — the database, the CRM, the payment flow — and need permissions, sandboxing, and approval gates in place before anything autonomous gets near them
AI engineers and technical leads whose agent works in the demo but not in production — looping, retrying side effects, failing in ways nobody can explain afterwards, with no way to tell whether last week's prompt change made it better or worse
Course syllabus
5 Weeks • 5 Modules • 4 lessons (4 live lessons)
Most agent demos work once and can't be restarted. This module is about the difference between a script that calls an LLM in a loop and a system that survives a crash, a restart, and a bad model day.
Module content:
Lesson 1 — Deterministic workflows vs autonomous agents
Where each belongs, what autonomy actually buys you, and the failure modes you inherit when you hand over control of the loop.
Lesson 2 — Stateful orchestration and durable execution
Separating agent state from process memory. What "durable" means when a tool call takes ninety seconds and the pod restarts.
Lesson 3 — Checkpointing, persistence, and resumable workflows
Where to checkpoint, what to store, and how to resume mid-trajectory without repeating side effects.
Lesson 4 — Tool contracts and MCP
Designing tool interfaces the model can actually use — naming, schemas, error shapes — and exposing them through the Model Context Protocol.
Lesson 5 — Memory architecture
Short-term working context, long-term per-user memory, and shared organizational memory: what belongs in each and what it costs in tokens.
Lesson 6 — Model routing and fallback
Routing by task and cost across private endpoints, with fallback paths for when a model is down, slow, or refusing.
Lab: Build a stateful agent workflow on a private LLM that survives a restart mid-execution.
Production Agent Architecture
Frequently Asked Questions
If you still have questions, contact instructor or Zomra on LinkedIn
All live sessions are recorded and available for replay within 24 hours. You can watch them at your convenience.
You're one step away from learning new skills, achieving your career goals, & earning a certificate.
Certificate of Completion
AgentOps for Private LLM Systems
Graduated from AgentOps for Private LLM Systems at Zomra, with strong grasp of its fundamentals, completed all coursework and activities.