Leobit
Live Webinar
The Efficient AI Harness: What Your Project Really Needs
Topics Covered
- Efficiency starts with the right setup: how scope, complexity, speed and risk decide whether a solo developer, a small team or a full team delivers on time at the lowest cost
- The four stages of AI engineering: from prompt and context engineering to agentic and harness engineering, and why the bottleneck has moved from writing code to reviewing it and deciding what to build
- What an AI harness is, and why AI still needs one: context, tools, feedback, gates and orchestration, and what Brooks’ law means for AI agents
- Harness levels explained: what each harness level is good at, where it breaks, and how to choose the one your project really needs
- Over-harnessing is the new over-engineering: the warning signs of too much process, and two quick tests for what to cut
- The harness as a foundation for AI loops: what inner and outer loops are, and how a harness gives them context, checks and a stop condition
- Leobit AI Enablement Kit: a ready-made harness for your live codebase and tools, with approved specs before work starts, review gates, and project knowledge confirmed by a person
- Real project results: one Leobit project, 20 tasks across BA, QA, dev and PM, three harness levels, 120 blind-graded runs, and what changed in time, cost and quality
Who should watch
Researchers from Stanford and MIT found that changing only the harness around the same AI model can make it perform up to 6 times better, which makes the harness one of the strongest levers for getting real value from AI agents.
CEOs & Founders
Leaders of startups and growing companies who are building or improving a product with AI agents and want to know what kind of AI harness their project needs to move faster without losing quality.
CTOs & Tech Leaders
CTOs, CIOs, VPs of Technology, VPs of Engineering, IT Directors, and other technical leaders who need to choose the right level of AI harness for each project and avoid adding process the team doesn’t need.
Heads of R&D, Tech Product Leaders
VPs of R&D, Heads of R&D, and Tech Product Leaders whose teams already write code with AI and now spend most of their time reviewing it and deciding what to build next.
Heads of Departments
Executive Leaders, Senior Business Leaders, and Heads of Operations who want to see how AI changes the work of BAs, QAs, developers and PMs, backed by real project data on time, cost and quality.
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