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build-with-agent-runtime

Name: build-with-agent-runtime
Availability: InStock
Author: Tangle

Automates AI agent genome creation, benchmarking, optimization, and certification using AgentProfile or AgentSurfaces and @tangle-network/agent-runtime/agent-eval.

3|Updated May 3, 2026

Search Tags:#optimization #AI agent #benchmarking #agent runtime #agent genome

Ready to run

npx skills add https://github.com/tangle-network/agent-runtime --skill build-with-agent-runtime

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: build-with-agent-runtime
Source: https://github.com/tangle-network/agent-runtime/tree/main/skills/build-with-agent-runtime
Command: npx skills add https://github.com/tangle-network/agent-runtime --skill build-with-agent-runtime

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires @tangle-network/agent-runtime, @tangle-network/agent-eval, and includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill automates the process of creating, benchmarking, and optimizing agent genomes, reducing the complexity of building and improving AI agents.

Core Features & Use Cases

Agent Genome Creation: Generate agent genomes using AgentProfile or AgentSurfaces.
Benchmarking: Run agent genomes on benchmarks to evaluate performance.
Optimization: Optimize agent genomes through a gated loop and certify wins on a frozen holdout.
Use Case: If you're developing an AI agent for a specific task, this Skill can help you create the genome, run it on a benchmark, optimize it, and observe its performance.

Quick Start

Use the build-with-agent-runtime skill to create an agent genome, run it on a benchmark, and optimize it for performance.

Frequently Asked Questions about build-with-agent-runtime

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I automate AI agent genome creation and benchmarking?▼

You can automate agent genome creation and benchmarking by generating genomes using AgentProfile or AgentSurfaces, then evaluating performance on benchmarks. This Skill streamlines the entire workflow from creation to evaluation.

What is an agent genome and when do I need to optimize it?▼

An agent genome defines the core configuration of an AI agent. You need to optimize it through a gated loop when you want to systematically improve agent performance and certify wins on a frozen holdout dataset to prevent overfitting.

Do I need @tangle-network/agent-runtime and agent-eval to benchmark agent genomes?▼

Yes, executing agent genome benchmarking and optimization requires both @tangle-network/agent-runtime and @tangle-network/agent-eval dependencies. These packages provide the necessary environment to run and evaluate agent performance.

What's the best way to optimize AI agents and certify performance wins?▼

The best way to optimize AI agents is through a gated optimization loop that certifies wins on a frozen holdout. This approach ensures performance improvements are validated against unseen data, preventing overfitting during agent genome optimization.

Can I use this workflow to evaluate AI agent performance for specific tasks?▼

Yes, if you are developing an AI agent for a specific task, this workflow supports creating the genome, running it on a benchmark to evaluate performance, and optimizing it to observe how well it executes the target task.