# 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… - **URL:** https://zomra.io/courses/agentops-for-private-llm-systems-eud1y6 - **Type:** Course - **Published:** September 14, 2026 - **Last updated:** September 15, 2026 - **Category:** AI - **Price:** EGP 8,000.00 - **Taught in:** English ## About this course 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. ## What you will 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 ## Who this course is 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 ## 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 ## What's included - Interactive live lessons - Projects to apply learnings - Community of peers - Certificate of completion - Lifetime access to all course materials ## Cohorts - **Cohort 1** — November 20, 2026 → December 18, 2026 · 5 weeks · 4 live lessons ## Instructor ### Mahmoud AbdelAziz Founder & CEO @ DevisionX Mahmoud has 15+ years of spearheading products in many fields of Computer Vision, Robotics, Artificial intelligence, Digital Transformation, fintech, eKYC and SaaS. He is leading his startup DevisionX to disrupt the AI Computer Vision Industry by building No-Code workflow builder "Tuba" and offering it with different multimodal RAG solutions for High Regulated Sectors. Also Mahmoud worked as AI consultant for the Workforce Egypt USAID project supervising the new AI Technical schools in Egypt. - https://www.linkedin.com/in/mahmoudaziz/ ## Teaching team ### Mohamed Rashad Co-Founder and CTO @ DevisionX - https://www.linkedin.com/in/rashaddism ## Frequently asked questions ### What happens if I can't make a live session? All live sessions are recorded and available for replay within 24 hours. You can watch them at your convenience. ### Will I receive a certificate after completing the course? Yes, you will receive a certificate of completion after finishing all required modules and assignments. ### What is your refund policy? We offer a 7-day money-back guarantee. You can request a full refund within 7 days of enrollment. ## Enrolment Enrol at https://zomra.io/courses/agentops-for-private-llm-systems-eud1y6