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title: Slot Resolvers
excerpt: Resolving natural language into business objects
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# What are slot resolvers?

Slot resolvers convert natural language into data types. The way that users will reference your business objects will seldom match how they're stored in business systems.

![](https://files.readme.io/24b4e8795e72b5c671a9f536ecd7e4ec1cbe92559915381fdcefd98135e71658-CleanShot_2024-10-24_at_18.18.212x.png)

For example...

* "Tomorrow's Standup" is a `GoogleCalendarEvent` with ID = `4s567d8s908f87654sa678ds`
* "Jamie" is a `User` with ID = `9e107d9d-372b-4ac9-b4e9-0fbccd3029ab`

We purpose-built Slot Resolvers to solve this problem. **[Plugins](/docs/plugins) built using slot resolvers will perform substantially better when deployed to production.**

# How do they work?

## Candidate Retrieval

Then, the AI agent retrieves possible values that might satisfy the constraints of your slot. These are retrieved using the [resolver strategy](/docs/resolver-strategies).

![](https://files.readme.io/3d741695b6e7dbf01a267e14f57d29e9d3d0c0303ece9a23d9c4ba7e6fe2cd3a-CleanShot_2025-02-05_at_12.17.08.png)

<br />

## Disambiguation

Then, when the AI agent finds multiple possible matches, it presents them (with [citations](/docs/citations-1)) so the user can pick the right one.

![](https://files.readme.io/b760cb0254cda14f5fadf33ea042da84213e6e7dad75264af606eff348d1d096-CleanShot_2024-10-24_at_18.29.322x.png)

## Stable Memory & State Management

Slot Resolvers use a **symbolic working memory architecture** to keep track of both

1. the list of possible values possible values &
2. the selected value

As a result you can be confident the AI agent won't accidentally change IDs or make up new ones when providing them to your actions.