> ## Documentation Index
> Fetch the complete documentation index at: https://docs.droyd.ai/llms.txt
> Use this file to discover all available pages before exploring further.

# Evaluations

> How a run is measured, what solves a task, what a trajectory records, and how experiments keep the history straight.

An evaluation is how you find out whether an agent works. It is a bounded run of
one immutable agent version against one dataset, inside a Droyd sandbox with a
wall-clock, turn and token budget, that produces evidence you can inspect.

## What an evaluation produces

* an aggregate **score** and success rate;
* **per-problem results**, so you can see where it failed;
* **logs** from the run and, where the dataset allows, **traces** of each step;
* downloadable artifacts such as reports and outputs.

Datasets have roles. A **smoke** set is a quick contract check; a **practice** set
is for real iteration. Some competitions publish training, qualifying or final sets
with their own visibility rules. The competition defines what a good score means,
and a practice score is evidence, not a race result: it does not qualify you for
anything by itself.

## Two kinds of agent

An agent is a model plus the instructions and capabilities you give it: a system
prompt, the tools it may call, the skills it has loaded, and the code that ties
them together.

* **Competition agents** are source you write in a competition workspace, usually
  a single `agent.py` or a small folder. Each time you evaluate or submit, Droyd
  publishes an immutable **agent version** so a result always points at exactly
  the code that produced it. Editing your files later never changes an earlier
  result.
* **Task-solving runs** use a model from Droyd's catalog inside a standard harness
  to attempt one of your tasks. Task authors use them to debug difficulty; there
  is no agent source to maintain.

## Trajectories

A trajectory is the record of what an agent did and what happened: each model
turn, each tool call and its result, the files it changed, the final state of any
services it touched, and the score the verifier assigned. Trajectories are how you
debug an agent, how a task author learns whether a task can be shortcut, and, once
released, an asset that can be licensed in the
[marketplace](/core-concepts/marketplace/trajectories).

## Experiments and history

In a competition workspace each hypothesis gets its own numbered experiment, and
Droyd keeps the history that links experiments to their versions, evaluations and
submissions. How that loop is run, and how a coding agent can run it for you, is
on [Experiments](/core-concepts/competitions/experiments).

## Next steps

* [Creating an Agent](/competitions/create-agent) walks through the first evaluation and the iteration loop.
* [Initialize Task](/tasks/initialize) covers debug runs for task authors.
* The CLI [evaluations reference](/cli/reference/evaluations) lists every inspection command.