Agents
The infrastructure that runs evals end to end: it feeds tasks to the model or agent, records every step, applies graders, and aggregates the results into scores.
The infrastructure that runs evals end to end: it feeds tasks to the model or agent, records every step, applies graders, and aggregates the results into scores. Anthropic separates it from the agent harness, which is the system that lets the model act; when you evaluate an agent you are measuring the harness and the model together. EleutherAI's open-source lm-evaluation-harness is a well-known example, running over 60 standard academic benchmarks against many model backends.
Primary source: anthropic.com
Example
In practice, developers reach for Eval Harness when they need the capability described above as part of an AI feature or workflow.
Hands-on guides, comparisons, and tutorials that cover Agents.
FAQ
The infrastructure that runs evals end to end: it feeds tasks to the model or agent, records every step, applies graders, and aggregates the results into scores.
Eval Harness sits in the Agents part of the AI stack. Understanding it helps you make better decisions when building, debugging, and shipping AI features.
Developers Digest publishes tutorials and videos that cover Agents topics including Eval Harness. Check the blog and YouTube channel for hands-on walkthroughs.
Related
A way of building AI systems where you write evals for a capability before the agent can do it, then iterate on prompts, tools, and harness until the evals pass.
An agent workflow where one LLM call writes a response and a second call checks it against explicit criteria and sends back feedback.
Vercel's framework for building AI agents where you define an agent as a folder of files - model config, system prompt, and one typed tool per file - and eve compiles it into an app on Vercel Functions.

New tutorials, open-source projects, and deep dives on coding agents - delivered weekly.