Free Lesson

Control LLMs Via Harness, Loops, Memory, Evals & Tracing

Hosted by Sol Farahmand

Thu, Jul 16, 2026

6:30 PM UTC (30 minutes)

Virtual (Zoom)

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What you'll learn

Learn the Concept of the "Harness"

A framework of tools and controls that ensure the LLM follows specific instructions and works at its maximum.

Learn The Three Types of Agent Memory

A sophisticated AI agent requires more than just short-term "working memory".

Learn Loop Engineering and Guardrails

Loop engineering involves designing the logic of when a task is "good enough" to stop.

Learn About Tracing the "Tree of Events"

To understand how an agent is performing, you must implement a tracing system.

Learn About LLM Ops and the Feedback Loop

Building a system is not a one-time event; it requires a continuous evaluation (Eval) system.

Why this topic matters

An AI agent harness controls LLM randomness using procedural, semantic, and episodic memory. Loop engineering ensures tasks finish via guardrails. LLM Ops creates a feedback loop where tracing tracks "event trees" like latency and tokens to diagnose performance. By evaluating runs, developers iterate on prompts and configs to evolve systems.

You'll learn from

Sol Farahmand

AI Hackathon Winner | 2X Entrepreneur | AI Workflow Builder

Hi, my name is Sol, I’m a business owner taking on some of my toughest business tasks with AI to increase productivity, and I want to share my learning journey with you so that you can skip the trial and error of using AI.

I’ve built hands-on AI systems using tools like Lovable, Claude Cowork, MindStudio, Claude Code, and Codex. I specialize in turning complex AI concepts into practical systems that professionals and business owners can actually use.

I’m an AI hackathon winner, have published 5 Skills, and teach through real implementation instead of theory.

What makes my teaching different is that I help you become operational with AI and improve productivity that fits directly into your day-to-day work.

See all products from Sol

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