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

# Actions & runs

> How Agent Sentinel models agent execution: runs contain actions.

## Actions

An **action** is a single instrumented unit of work in your agent (a tool call, API request, DB query, etc.).

In the Python SDK, actions are created by decorating functions with `@guarded_action(...)`.

Each action records:

* **Name**: `name` argument (or the function name)
* **Cost**: `cost_usd` (you supply the estimate for that call)
* **Duration**: measured automatically
* **Outcome**: `"success"`, `"error"`, or `"replayed"`
* **Inputs/outputs**: captured from the call
* **Tags**: optional list of strings

## Runs

A **run** represents one execution of your agent (one “session” you want to analyze end-to-end).

On the platform, a run:

* Owns many actions
* Aggregates `total_cost`
* Has a `status` such as `"running"` / `"completed"`

## Run IDs

SDK 0.2.0 stamps a `run_id` onto every ledger entry, taken from the active `ExecutionContext`. To group actions into a logical run:

```python theme={null}
from agent_sentinel import ExecutionContext

with ExecutionContext(run_id="my-run-id", agent_id="my-agent"):
    do_thing()      # ledger entry includes run_id="my-run-id"
    do_other()
```

When using remote sync, the same `run_id` is sent alongside the batched entries (see <a href="/sdk/python/remote-sync">Remote sync</a>) so platform-side run grouping is consistent with local ledger filtering.

<Tip>
  If you launch agents from a parent process, set `ExecutionContext` once per logical run rather than per call so every action lands in the same run on the platform.
</Tip>