AI Agents in Production: Harness Engineering Masterclass

Course Overview

This course teaches how to build a complete AI agent harness from scratch, focusing on the agent loop, tool design, context management, guardrails, and observability. It covers building a production-ready support resolution agent that handles real customer tickets using raw SDKs without relying on frameworks.

Students will learn to design evaluation sets, write effective tools with schemas and error messages, engineer context windows for accuracy, and deploy agents with full observability on a VPS using Docker, Traefik, and Langfuse tracing.

Key Takeaways

• Build an AI agent harness including loop, tools, context management, and guardrails around raw LLM calls.
• Design and use evaluation sets to measure agent performance against real support tickets.
• Create tools with schemas, descriptions, and error messages to reduce failed tool calls.
• Engineer context windows with retrieval, compaction, and state management for long-run accuracy.
• Deploy production agents on VPS with Docker, Traefik, and Langfuse for full observability.

Prerequisites

• Comfortable writing Python and able to read functions and call HTTP APIs.
• Access to an Anthropic or OpenAI API key with some credit for exercises.
• No prior experience with agent frameworks is required.

Target Learners

• Developers who have built chatbots or prompt scripts and want to advance to production agents.
• AI engineers seeking to understand harness mechanics beyond frameworks.
• Automation builders using n8n or Make wanting to add code-level agents.
• Backend and full stack engineers tasked with shipping reliable LLM features.