Harness Engineering: State of the Art in Agent Harnesses
Harness Engineering is hot topic at the moment and this is a highly technical event focused on Harness Engineering for AI agents, especially cover the Coding Agents.
As models become more capable, the biggest performance gains are increasingly coming from how agents are orchestrated, evaluated, and controlled, not just the models themselves. It’s the tight, inseparable dance between harness and memory in agentic AI.
Recent advancements across the ecosystem are showing that smarter harness design (planning loops, context management, verification mechanisms, error recovery, and execution runtimes) can deliver outsized improvements, often outperforming model upgrades alone on the same underlying LLM.
This has made harness engineering one of the most critical and exciting areas in agent development right now.
🧰Harness Engineering Innovations in recent days making lot of waves in this space that we can cover in the event. Also discussion around memory and harness and self-optimising harness for coding agents. Coding Agents companies, Agents labs and observability labs are making tremendous progress in this spaces.
This session brings together engineers who are actively building these systems in practice. We’ll dive into how coding agent harnesses are really designed, what commonly breaks in real-world deployments, and how teams are evolving toward more robust, scalable, and maintainable agent architectures.
We are talking to amazing speakers and panelists in the industry and shortlisting for this event.
Talk 1: The Harness Is the Product: How to Build, Break, and Evaluate
🎙️Speaker: Dat Ngo (AI Architect, Arize AI)
Description:
AI agents do not work because the model is smart. They work because the surrounding system makes intelligence usable.That surrounding system is the harness: the sandbox, tools, context, memory, evals, traces, policies, and recovery loops that shape what the model can do. In practice, the harness often matters as much as the model itself.
This talk will dissect the AI harness piece by piece, then evaluate it as a whole. We will look at what happens when you run weaker models inside stronger harnesses, how execution conditions change task completion, why sandbox design affects behavior, and how observability and evals reveal whether the system is actually improving or just appearing to work.The core question: can your AI system survive outside the happy path?
Talk 2: Stop Tuning One Harness at a Time!
🎙️Arun Kumar : CTO and Cofounder of RapidFire AI
Description:
You've picked a frontier model for your agent. Great. But whether it attains production quality comes down to its harness - system prompts, retrieval strategy, workflow structure, hyperparameters and more. That is a massive design space, and most teams painstakingly trudge through it one config at a time. RapidFire AI's "hyperparallel experimentation" transforms that slog into a systematic search optimized for application outcomes: compare even 1000s of configs on one machine with live eval metrics, and control configs in flight programmatically, manually, or via a promptable actionbot - to reach better metrics faster and with lower token spend.
Speaker Bio:
Arun Kumar is CTO and Cofounder of RapidFire AI, an open-source platform that helps AI developers and FDEs engineer AI agent outcomes to escape pilot purgatory. He is also a professor of computer science and data science at the University of California, San Diego. He has wo
Agenda (Tentative):
6:00 – 6:30 PM → Doors open, networking & light snacks
6:30 – 6:40 PM → Welcome
6:45 – 7:05 PM → Dat Ngo (Arize AI)
6:05 – 7:25 PM → Arun Kumar (RapidFire AI)
7:30 – 8:10 PM → Panel Discussion (panelists from companies building coding agent harnesses)
8:10 PM onward → Open networking & deeper discussions
Speakers & Panelists: We are inviting engineers and researchers actively working on harnesses for coding agents from leading labs and companies in the agentic coding space.
What to expect:
Cutting-edge talks on real-world harness architectures, memory and harness alignment.
Approaches to design the Self optimising and self healing harnesses.
A fire panel with builders from leading agent labs and companies
High-signal networking with the people shaping this exploding discipline
Who should attend?
AI/ML engineers, agent builders, infrastructure teams, and developers focused on building or optimizing reliable coding agents beyond basic prompting or simple frameworks. Expect deep technical discussion and practical takeaways.
Space is limited, RSVP early.