CF

Production-Ready Systems with LLMs and Agents: An Intensive for Engineers

5h 58m 10s
English
Paid
Added to CourseFlix

Production-Ready Systems with LLMs and Agents: An Intensive for Engineers is a 10-lesson 5 hours 58 minutes self-paced course by Ehsan Gazar. Learn to create LLM and agent systems that withstand real traffic, cost constraints, and inevitable failures.

Course facts

Lessons
10
Duration
5 hours 58 minutes
Language
English
Added to CourseFlix
October 2026
Instructor
Ehsan Gazar
Price
Premium

Learn to create LLM and agent systems that withstand real traffic, cost constraints, and inevitable failures.

Almost any engineer can connect an LLM and assemble an impressive demo today. But upon moving to production, all the things that are usually not taught become apparent: costs start to rise, latency increases, the model confidently provides incorrect answers to the user, and without evaluations, you can't timely detect quality degradation and without tracing, you can't understand what exactly went wrong.

The demo is the easiest part.

If you are responsible for implementing AI features in production, you have to bridge this gap yourself: experiment, rely on tutorials that usually end on the happy path, and hope that the system will withstand real-world operation.

This intensive focuses on that engineering part which most educational materials skip: architectural solutions that allow LLM and agent systems to work under real loads, fit within the set budget, and handle failures correctly.

You will understand where the boundary should lie between code and model, how to control cost and latency, how to design systems considering the non-determinism of LLM, how to build evaluations and observability, and where human oversight is necessary.

As a result, you will have not just another demo, but production-ready artifacts that can be used in work as soon as next week: an architectural document of an AI agent, a cost and latency budget, an eval harness plan, and a runbook for handling failures.

What You Will Learn

Correctly Divide Responsibility Between Code and Model

  • Determine which tasks to assign to the LLM and which to leave in deterministic code—one of the most important architectural decisions in agent systems.
  • Identify tasks where the model adds more risk than benefit and replace it with conventional software logic.
  • Design prompts and tool interfaces as narrow and understandable contracts, so that the model's tasks remain limited and testable.

Control Cost and Latency Under Real Traffic

  • Set a cost and response time budget for each request and design the system with these constraints in mind.
  • Use caching, batching, and routing between models to reduce expenses without significant loss of quality.
  • Select the model for the specific task instead of using the most powerful model for everything.

Design the System Considering Non-Determinism and Failures

  • Implement retries, timeouts, and fallbacks so that a slow or unavailable model does not block the entire system.
  • Create graceful degradation for cases when the model makes errors, is unsure of the answer, or is completely unavailable.
  • Limit the influence of non-determinism with validation and guardrails before the result reaches the user.

Choose the Right Architecture for the AI Agent

  • Understand when to use tool calling, planning loops, memory, or multiple interacting agents.
  • Recognize situations when a well-designed single agent is more effective than a complex multi-agent architecture.
  • Link each architectural pattern to its potential failure modes and make decisions based on engineering trade-offs rather than hype.

Detect Quality Degradation with Evaluations

  • Create an eval harness that allows evaluating changes before they go to production, rather than after user complaints.
  • Combine offline test sets with online signals to detect drift and subtle quality drops.
  • Turn subjective "it seems worse" into measurable quality gates within the CI/CD process.

Confidently Operate Agent Systems

  • Trace each LLM call to understand what the system actually did, rather than assume what it should have done.
  • Monitor cost, latency, and quality through metrics and dashboards that allow early problem detection.
  • Determine where human oversight and prompt versioning truly mitigate risks.

Who This Course Is For

  • Product and Backend Engineers tasked with implementing LLM features and who are capable of creating AI demos but have not yet deployed such systems in production.
  • Senior and Staff Engineers responsible for AI systems in production and aiming to establish a systematic approach to architecture, cost, resilience, and quality.
  • Tech Leads and Architects who need to evaluate different agent patterns and choose solutions that can scale and operate stably under load.

This course is not about creating another impressive AI demo. It's about turning LLMs and AI agents into engineering systems that can be trusted in production.

Additional

https://hub.gazar.dev/llms-and-agents/

Who teaches Production-Ready Systems with LLMs and Agents: An Intensive for Engineers? Ehsan Gazar

Ehsan Gazar thumbnail

Staff Software Engineer with 16 years of experience in developing and scaling production systems in fintech, SaaS, and enterprise software. Throughout his career, he has made hundreds of architectural decisions for systems operating under real load, during incidents, and within organizational constraints.

He has conducted over 500 mentoring sessions with senior engineers, receiving a rating of 5.0, helping them transition from code writing to architectural thinking. He has also taught over 10,000 students.

The author emphasizes not academic theory, but practical experience: real architectural solutions, trade-offs, and mistakes. His goal is to teach how to think like a Staff Software Engineer: not to seek the only "correct" answer, but to understand trade-offs and choose solutions that truly withstand real-world challenges.

What lessons are included in Production-Ready Systems with LLMs and Agents: An Intensive for Engineers?

This is a demo lesson (10:00 remaining)

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#1: 001 Why demos die in production, and when you even need an agent
All Course Lessons (10)
#Lesson TitleDurationAccess
1
001 Why demos die in production, and when you even need an agent Demo
43:00
2
002 The deterministic model boundary
28:52
3
003 Context engineering what goes in the window, and why cramming hurts
48:03
4
004 Workshop design your context pipeline
46:28
5
005 Budgets, routing, and designing for failure
45:05
6
006 Workshop budget + failure-mode map
27:18
7
007 Agent patterns and their failure modes
33:56
8
008 Securing agents + choose your architecture
27:32
9
009 Trajectory evals, not just outputs
25:41
10
010 Workshop eval harness, tracing & runbook
32:15
Watch the other 9 lessons

One payment unlocks this course and every other premium course. No auto-renew.

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Frequently asked questions

How much LLM experience should I have before starting?
You'll get more out of the course if you're comfortable with software engineering and already understand what it means to call an LLM from an application. No formal prerequisites are specified, but the emphasis is on engineering decisions beyond a working demo, rather than introductory programming. If you've tried building an AI feature and started wondering about reliability, cost, or debugging, you'll have useful context for the material.
What will I actually work on during the course?
Expect to work through system-design artifacts rather than just follow a demo. The workshops focus on designing a context pipeline, working out budgets and failure modes, and planning evaluations, tracing, and operational responses. The intended outputs include an agent architecture document, a cost and latency budget, an evaluation harness plan, and a runbook. Those are concrete planning tools; don't confuse the evaluation plan with a promise of a fully implemented testing platform.
Is this useful if I'm still deciding whether my feature needs an agent?
Yes—you don't need to arrive committed to an agent architecture. Part of the course is deciding when an agent is warranted and where predictable application code should remain in control. That's useful if you're responsible for an AI feature but want to avoid adding autonomy unnecessarily. You'll also examine agent patterns and their failure modes, so the architecture decision is tied to operational consequences rather than just what makes an impressive demonstration.
How is this different from a tutorial that shows me how to build an LLM app?
The difference is the attention paid to what happens after the app appears to work. You'll consider budget limits, response times, failure handling, security, and how to detect quality problems. Evaluation also goes beyond judging a final answer: there's attention to the steps an agent takes along the way. If your main need is another happy-path implementation walkthrough, this has a different emphasis: making defensible engineering choices around an LLM-based system.
Should I take this to learn how to train my own language model?
That's not the focus here. The course is about designing and operating software systems that use LLMs and agents, with attention to context, architecture, evaluations, and operational constraints. You shouldn't choose it expecting a model-training curriculum. It's a closer fit if your questions are about what information to give a model, what decisions to keep in code, and how to manage an application's behavior when model responses aren't deterministic.
How much time should I set aside?
The listed runtime is 5 hours and 58 minutes across 10 lessons, so allow about six hours for the lesson content alone. If you want to work through the design exercises rather than just watch them, set aside additional time. There's no fixed workshop completion time provided. A practical approach is to leave room between sections to develop your context pipeline, budget, and evaluation plan, especially if you're applying them to an existing project.
What can I carry over to other AI projects afterward?
The most reusable part is the way you reason about system boundaries and operational trade-offs. You can apply decisions about what belongs in code versus the model, how much context to supply, and how to budget cost and latency to other LLM-backed features. Evaluation planning, tracing, and runbooks are also useful beyond a single agent design. These are engineering practices you can adapt, rather than a guarantee that one architecture will suit every project.