Master the Coding Agent Harness: Plan With Frontier Models, Execute in 90% Less Time

Every serious coding agent – Claude Code, OpenCode, Codex, Cline – runs on the same coding agent harness pattern: one model plans, another model executes. This session shows you exactly how to build that split, cut your token costs by up to 90%, and walk away with your own coding agent harness pointed at SambaNova before the session ends.

What Is a Coding Agent Harness?

A coding agent harness is the architecture behind every modern coding agent. It splits work between two models with very different jobs:

• Planning layer – a frontier model reasons through the problem, breaks it into steps, and decides what needs to happen next
• Execution layer – a fast, low-cost model carries out the actual work: file edits, test runs, and the repetitive tasks that consume most of the token usage in any real development workflow

This separation matters because not every step in a coding workflow requires the same level of reasoning. Planning a multi-step refactor needs a model that can hold context and make judgment calls. Running the tenth test suite of the session does not. Treating both tasks the same way wastes money and slows the whole pipeline down.

This session is Part 1 of SambaNova’s Sponsored Webinar Series 2, focused on coding agents. It walks through the coding agent harness pattern end to end — what it is, why it works, and how a high-speed inference platform fits into the execution layer to reduce cost and increase speed without sacrificing quality.

What You Will Learn

This session is hands-on and built for practitioners who want something they can use immediately. You will learn:

• How the planning and execution split works across today’s leading coding agents
• Why offloading execution to a fast, purpose-built inference platform changes the economics of building with AI
• How models handle the execution layer in a real coding agent harness
• How the handoff between planning and execution is structured, step by step
• How to point your own coding agent at a fast inference platform in minutes

By the end of the session, the goal is for you to have a working mental model of the harness pattern and a concrete next step for applying it to your own development setup — not just theory.

Why the Coding Agent Harness Pattern Matters

Most teams building with coding agents are either overpaying or underperforming. Using a frontier model for every step – including repetitive execution tasks – is expensive and slow. Using a weak model for everything sacrifices the reasoning quality that makes agents useful in the first place.

The coding agent harness pattern solves this by matching model capability to task type. Frontier models are reserved for planning, reasoning, and decision-making. Fast, low-cost models handle the high-volume execution work that dominates token usage in any real workflow. The result is a system that is faster, cheaper, and just as capable where it counts.

This is especially relevant as coding agents move from experimental tools into daily development workflows. Teams running agents at scale feel the cost of every wasted token, and the harness pattern is one of the most direct ways to bring that cost down without touching output quality.

For more on inference infrastructure and agent performance, see the Data Science Dojo blog for guides on LLM deployment and agent architecture. The SambaNova Systems blog offers additional technical context on high-speed inference.

Who Should Attend This Webinar?

This webinar is built for:

• Developers integrating coding agents into their workflows and looking to reduce infrastructure costs without rewriting their tooling
• AI engineers exploring cost-efficient inference strategies for production agent systems
• Technical leads making infrastructure decisions for development teams and evaluating where to allocate model spend

No prior experience with the platform is required. If you are currently using or considering a coding agent in your workflow, this session gives you a practical framework for making it faster and cheaper to run.