Harness Engineering: Designing the Operating Layer for Reliable Agents
Aishwarya Naresh Reganti
AI Founder & Advisor to F500s | Ex-AWS
Kiriti Badam
Applied AI @ OpenAI Codex | Ex-Google
Learn harness engineering from experts building harnesses at the frontier!
The harness is the operating layer that manages an agent’s context, memory, tools, autonomy, and checks. Learn to engineer it, then build a harness and improve a browser agent.
Self-paced: two core recordings, slides, readings, two assignments, and solutions. No Slack access, live sessions, or individual feedback. Any September schedule below belongs to the completed live cohort.
Current Offers:
1. Live-cohort offer (when scheduled): 25% off with 25OFF
2. 🎁 Full Stack AI Bundle — $2,950 early bird
Become a full-stack AI builder by year-end.
Get this self-paced course with the bundle using this link.
Problem First AI + the Agent-Native Operator workshop
Advanced self-paced courses: Harness Engineering and Beyond Evals
Hands-on Portfolio Build Labs with selected partners to build your resume + $800–$1,000 in Partner Credits (Deepgram, Arize, NVIDIA, Cursor, Lyzr, Grain, Cartesia, Vapi, Mem0, Composio, Exa).
What you’ll learn
Move from using agent frameworks to engineering and customizing the harness layer that makes agents reliable.
Map every lever a harness turns: loop, context, memory, autonomy, delegation, verification, observability.
Learn the failure each lever prevents: context rot, tool sprawl, compounding sub-agent error.
Build a mental model that transfers to any framework or model.
Dissect layers of context window with a budget: compaction, clearing, and offloading without losing coherence.
How do agents like Codex and Claude carry on really long conversations and tasks.
Add the control, state, and gates a bare plan-act-observe loop lacks (stop conditions, step and token budgets).
Actually implement how skills and plugins via progressive disclosure and just-in-time loading works in a harness.
Delegate for context isolation, specialization, or parallelism. Architecture shouldn't define this, user query should.
Understand how learning models like GPT 5.6, Fable delegate in their harnesses.
Decide what runs automatically and what needs human approval (allowlists, command approval).
Learn how sandboxing, execution environment etc. limit what agent can touch.
Implement objective verification: how features like /goal and /loop work, and when to use them.
Use tracing to find where harnesses break, and track improvement metrics.
Learn directly from Aishwarya & Kiriti
Aishwarya Naresh Reganti
AI Founder & Advisor to F500s | Ex-AWS
Kiriti Badam
Applied AI Lead | AI Advisor | Ex-Google
Who this course is for
Technical product managers directing agent work who need to reason precisely about autonomy, reliability, cost, and control tradeoffs.
Engineers building agents who want to move from wiring up a framework to deliberately engineering the harness underneath.
Founders and builders shipping agent products who want the harness to be a durable advantage rather than an afterthought.
Prerequisites
Basic familiarity with coding or the willingness to learn basic commands.
You will be building your own AI harness with the help of coding agents and Python libraries.
What's included
Lifetime access
Go back to course content and recordings whenever you need to.
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Course syllabus
Unit 1
Start here: Welcome to Maven
Welcome to Harness Engineering
[Please Read] Course Structure, Components & Time Commitment
FAQs
Unit 2
[CORE] Week 1: Anatomy of harness and building one from scratch
[Build] Environment Setup for Assignments
[Build] Assignment 1: Build your own harness
[Grow] How companies customize different layers of the agent harness
[Grow] How local sandboxes work in coding agents
Frequently asked questions
Maven for Teams
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