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AI Agents in Production Harness Engineering Masterclass
Published 9/2026
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz, 2 Ch
Language: English + subtitle | Duration: 1h 25m | Size: 1.5 GB
The discipline behind reliable agents in 2026: agent loops, tool design, evals, context engineering, observability
What you'll learn
Build a complete AI agent harness from scratch: the loop, the tool layer, context management, and the guardrails around a raw LLM call
Design an eval set before you write agent code, then measure every change against 12 real support tickets
Write tools an agent can actually use, with schemas, Descriptions and error messages that cut failed tool calls
Engineer the context window with retrieval, compaction and state so your agent stays accurate over long runs
Ship a production agent on your own VPS with Docker, Traefik and Langfuse tracing for full observability
Requirements
Comfortable writing Python. You should be able to read a function and call an HTTP API
An Anthropic or OpenAI API key. A few dollars of credit covers every exercise in the course
No agent framework experience needed. We build on the raw SDKs, not LangChain or CrewAI
Description
Prompt Engineering Was 2023. Context Engineering Was 2025. Harness Engineering Is Now.
The field has moved three times in three years. First we argued about how to phrase instructions. Then we argued about what to put in the context window. Both mattered, and both stopped being enough the moment people started shipping agents that act on their own. The formula everyone converged on is simple: an agent is a model plus a harness. The model supplies reasoning. The harness supplies everything else, and the harness is what you are actually paid to build.
This course teaches you to build one.
What You Will Learn
We build a single system end to end, a Support Resolution Agent that reads a real customer ticket, looks up the order, decides on an action, and answers. You start from a bare API call and add each layer of the harness in turn: the agent loop, the tool interface, context delivery and compaction, memory and state, permissions and guardrails, verification, tracing, and a human approval gate for the actions that carry risk.
Along the way you will meet the failure modes that only appear in production. Agents that declare victory on work they never verified. Agents that get sloppy and rush as the context window fills. Agents that try to one shot a problem that needed four careful steps. None of these are prompt problems. Every one of them is a harness problem, and each has a concrete fix you will implement yourself.
You will write an evaluation set of twelve real tickets before you write the agent, so every change afterwards is measured rather than guessed. You will design tools with schemas and error messages a model can recover from. You will deploy the finished agent on your own VPS with Docker and Traefik, trace every run in Langfuse, and route risky actions through an n8n approval step.
Why This Course
There is no framework here. No LangChain, no CrewAI, no abstraction between you and the mechanics. You work directly with the Anthropic and OpenAI SDKs, because a harness you built by hand is one you can rebuild inside any framework, or replace when the next one arrives. Teams have moved agents from the middle of the pack to the top of public benchmarks by changing the harness alone, without touching the model. That is the leverage this course hands you.
Bring an API key and a few dollars of credit. We build the rest together.
Who this course is for
Developers who have built chatbots or prompt scripts and want to move up to real production agents
AI engineers tired of framework magic who want to know what a harness actually does under the hood
Automation builders working in n8n or Make who want to add a code level agent to their stack
Backend and full stack engineers asked to ship an LLM feature that has to work, not just demo well
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