CF

Harness Engineering Workshop

10h 23m 2s
English
Paid
Added to CourseFlix

Harness Engineering Workshop is a 5-lesson 10 hours 23 minutes self-paced course by Rajat Dandekar, Vizuara AI Labs. Harness Engineering: A Practical Masterclass — a 5-day practical workshop on creating your own working harness for AI coding agents .

Course facts

Lessons
5
Duration
10 hours 23 minutes
Language
English
Added to CourseFlix
November 2026
Instructor
Rajat Dandekar, Vizuara AI Labs
Price
Premium

Harness Engineering: A Practical Masterclass — a 5-day practical workshop on creating your own working harness for AI coding agents. During the training, you will dissect the architecture of the agent loop, learn to manage tools, context, memory, and session recovery, and in the end, build and present your own coding-agent harness.

What This Masterclass Is About

The course is dedicated to the practical development of a harness—the management layer for an AI coding agent that integrates the model, tools, environment, memory, context, and human participation into a unified working system.

Instead of abstract theory, you sequentially go through the entire cycle of creating an agent system: from basic architecture and tool integration to error processing, state recovery, subagent work, and preparing a production-ready solution.

Practical benchmarks like Claude Code, pi, and Hermes are considered, as well as current approaches to building AI coding agents and autonomous development systems.

What You Will Learn

  • Design the architecture of the agent loop for AI coding agents.
  • Create your own harness to manage agent actions.
  • Connect tools, environments, and external commands to the agent system.
  • Configure the management of context, memory, and execution history.
  • Implement session recovery after errors and interruptions.
  • Use subagents for task segmentation and increased reliability.
  • Integrate human-in-the-loop scenarios to control critical decisions.
  • Prepare the coding-agent harness for demonstration and further development.

5-Day Workshop Program

Day 1: AI Coding Agent Architecture

You will explore the components of a modern coding agent: model, decision-making cycle, tools, file system, execution environment, and management interface.

  • The role of harness in agent architecture.
  • Basic agent loop: observation, planning, action, result verification.
  • Exploration of Claude Code, pi, and Hermes operating principles.
  • First version of your own minimal harness.

Day 2: Tools, Environment, and Command Execution

The second day focuses on the practical integration of the tools that the AI agent interacts with: shell commands, file system, tests, linters, repository, and development environment.

  • Safe command execution by the agent.
  • Interfaces for reading, modifying, and analyzing code.
  • Connecting quality checks and tests.
  • Logging agent actions and execution results.

Day 3: Context, Memory, and State

Learn to manage context so the agent retains relevant information, doesn't lose task objectives, and can continue work over long sessions.

  • Strategies for prompt formation and working context.
  • Short-term and long-term agent memory.
  • Compression, updating, and prioritization of context.
  • Project state storage and decision history.

Day 4: Session Recovery, Subagents, and Human-in-the-Loop

On the fourth day, you will add resilience mechanisms to the harness: error recovery, task delegation to subagents, and human participation in critical decision-making.

  • Recovery of agent session after an error or interruption.
  • Task division among specialized subagents.
  • Control of risky actions through human-in-the-loop.
  • Enhancing reliability and predictability of AI coding agents.

Day 5: Final Assembly and Project Demonstration

In the final phase, you will integrate all the components into a working coding-agent harness, prepare a demonstration, and receive feedback on architecture, implementation quality, and possible improvements.

  • Assembly of a production-ready harness version.
  • Verification of the complete agent loop on a practical task.
  • Participant project demonstrations.
  • Analysis of typical errors and directions for further development.

Practical Results After Training

The main result of the course is not a transcript, but your own working project. By the end of the masterclass, you will have assembled a coding-agent harness that can be developed for your specific tasks: development automation, repository management, agent system research, or creating internal AI tools for the team.

  • Working architecture of an AI coding agent.
  • Code checkpoints for each module.
  • Understanding of key engineering decisions in harness engineering.
  • Experience comparing different approaches through examples of Claude Code, pi, and Hermes.
  • Final demonstration of your own project.

Who the Course Is Suitable For

  • Developers who want to create AI tools for coding and automating engineering tasks.
  • ML and AI Engineers who need to understand how agent systems around LLM are built.
  • Technical Leads evaluating the implementation of AI coding agents in development processes.
  • AI Researchers and Enthusiasts looking to move from prompt experimentation to creating full-fledged agent systems.

What Is Included in the Training

  • 5 days of intensive practice on harness engineering.
  • Daily session recordings for material review.
  • Code checkpoints for each module.
  • Step-by-step assembly of your own AI coding agent harness.
  • Live analysis of participant projects and feedback.
  • Practical examples based on Claude Code, pi, and Hermes.

Prerequisites

To comfortably engage in the masterclass, programming experience and a basic understanding of command-line work, Git, and API are recommended. Deep expertise in LLM is not required: key concepts of the agent loop, context, memory, and tools are covered during the training.

Why Study Harness Engineering

AI coding agents are becoming an increasingly important part of modern development, but their effectiveness depends not only on the model. The harness—a technical system that defines agent work rules, manages tools, controls state, and helps turn LLM responses into real coding actions—plays a key role.

By mastering harness engineering, you will not only use ready-made AI tools but also design your own agent solutions for specific tasks, processes, and requirements of your team.

Who teaches Harness Engineering Workshop?

Rajat Dandekar

Rajat Dandekar thumbnail

Rajat Dandekar is an AI engineer and educator focused on building modern large language models from first principles.

His CourseFlix listing carries Build a DeepSeek Model (From Scratch) — a from-scratch implementation walkthrough of the DeepSeek family of open-weight LLMs, covering the architectural decisions, training pipeline, and the engineering patterns underneath the DeepSeek approach.

Material is paid and aimed at engineers picking up modern LLM internals through hands-on implementation. For broader content, see CourseFlix's AI category pages.

Vizuara AI Labs

Vizuara AI Labs thumbnail

Vizuara is an international educational school in the field of artificial intelligence, founded by graduates of IIT Madras, MIT, and Purdue University. The company is developing with the support of the MIT ecosystem and aims to make quality AI education accessible to students and professionals worldwide.

Learning at Vizuara is based on the principle of "learn by creating": instead of studying theory alone, students work on real projects in the fields of artificial intelligence, machine learning, and generative AI. The programs combine fundamental knowledge, practical assignments, individual mentoring, and constant feedback, helping to quickly acquire in-demand skills.

In addition to technical courses, the school supports the development of academic and professional profiles: it assists with preparing portfolios, research papers, capstone projects, applying to foreign universities, as well as interview preparation and starting a career in AI.

The mission of Vizuara is to democratize access to modern education in the field of artificial intelligence by providing structured programs, practical experience, and mentorship. The school aims to become one of the most reliable educational platforms in emerging markets, where students not only gain knowledge but also create projects, build strong portfolios, and receive real career opportunities.

The philosophy of Vizuara is based on high-quality standards, a practical approach to learning, transparency, honest feedback, and a focus on the success of each student.

What lessons are included in Harness Engineering Workshop?

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What courses are similar to Harness Engineering Workshop?

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Frequently asked questions

Is this useful if I want to build an agent, rather than just use one?
Yes—that's the main distinction here. You'll work on the layer that coordinates an AI coding agent: how it calls tools, keeps track of context, handles interruptions, and brings a person into important decisions. It's aimed at someone who wants to build and control that system, rather than simply get better at prompting an existing coding assistant. Expect practical implementation work, not just a tour of agent features.
What will I actually build during the course?
You'll build your own working harness for a coding agent and present it at the end. The harness is the management layer connecting the model with tools, its environment, context, memory, and human input. You'll work through the agent loop and then address practical concerns such as errors, session recovery, and delegating work to subagents. The focus is the system that runs the agent, rather than a particular app the agent might create.
Does this go beyond a basic agent-loop demo?
Yes. The work extends beyond getting a model to call a tool and repeat. You'll also tackle execution history, memory and context management, recovery after failures or interruptions, and human control over critical decisions. Subagents are part of the work too, particularly for splitting tasks and improving reliability. That gives you more of the surrounding engineering needed to keep an agent running, without treating a successful first demo as the finish line.
Will I be working with Claude Code, or building something separate?
The main project is your own coding-agent harness. Claude Code, pi, and Hermes serve as practical reference points for examining how these systems work; the course isn't framed solely around operating one existing product. You'll focus on connecting a model, tools, external commands, and an execution environment into your own system. A required programming language, model provider, or SDK isn't specified, so don't assume a particular stack is mandatory.
Is this about training AI models, or about making agents work reliably?
It's about the engineering around a model, not training the model itself. You'll concentrate on how an agent takes actions, accesses tools, manages information, and recovers when execution goes wrong. Human-in-the-loop controls and subagent coordination also sit in that layer. If your main goal is model training or fine-tuning, that's outside the stated focus; the practical goal here is a working harness that you can build and demonstrate.
How much time should I set aside?
The listed runtime is 10 hours and 23 minutes, organized across five day-based sessions. Use that as your baseline for the course content, rather than assuming it represents all the time you'll spend building. Since you're also creating and presenting a working harness, allow room for implementation and troubleshooting. There's no separate estimate for that project work, so the total commitment will depend on your pace and how your build goes.
What could I reuse when building other agent systems afterward?
The most reusable pieces are the design patterns around the agent: structuring its action loop, connecting tools, managing context and memory, and preserving enough state to recover after an interruption. You'll also practice deciding where human approval belongs and how to divide work among subagents. Those concerns can carry into other agent projects, even when the model or tools change, though the hands-on work here stays focused on coding agents.