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

# Agents

> Detailed guide on configuring and deploying agents within your agentic platform environment.

## Understanding Agents

Agents are the atomic units of execution within the platform. An agent is a specialized entity designed to perform a specific task or set of operations. While a single agent can solve a discrete problem, the platform's power lies in combining multiple agents to frame a comprehensive **[Workflow](/v1.0.0/agentic-platform/workflows)**.

Individually, agents constitute work by receiving inputs, applying specific logic (ranging from natural language reasoning to hard-coded scripts), and producing a deterministic or structured output for the next stage of a process.

***

## Agent Classifications

Our platform provides six distinct agent types, categorized by their execution environment and the nature of the task they perform.

### 1. No-Code Agent

The No-Code agent allows users to define operational logic using plain, natural language. It is designed for accessibility, requiring zero programming knowledge.

* **Mechanism:** Interprets plain language instructions to execute tasks.
* **Best for:** General reasoning, text summarization, creative drafting etc.

Example:

```json theme={null}
Tell me a joke using the month of January in about 5 words.
```

### 2. No-Code File Search Agent

Simlar to No-Code agent, file search agent acts as a retrieval specialist. It utilizes natural language processing to browse through uploaded documents to find specific answers.

* **Mechanism:** Semantic search and information extraction from uploaded file repositories.
* **Best for:** Legal discovery, technical manual queries, HR policy lookups etc.

Example:

```json theme={null}
Extract all the important key points and summarise the document in about 2 lines.
```

### 3. Simple Python Agent

For tasks requiring custom logic that natural language cannot reliably handle, the Simple Python agent allows for direct scripting.

* **Mechanism:** Executes standard Python 3.x code snippets.
* **Best for:** Data transformation, string manipulation, and custom business logic.

Python agents support advanced orchestration and system integration, including:

* **Agent Interactivity**: Enabling agents to call and coordinate with other specialized agents.
* **Data Persistence**: Providing direct access to storage engines and internal databases.
* **Resource Management**: Creating snippet documents and accessing or modifying internal ERP resources.
* **Security & Auth**: Programmatically retrieving credential tokens for secure API communication.

<Note>
  For implementation guidance or technical assistance on using advanced features, please [contact our support team](mailto:support@sarasfinance.com).
</Note>

Example:

```python theme={null}
location = {{location}}
textInput = "{{textInput}}"

charAtLocation = textInput[location]
```

### 4. UI Automation Agent

The UI Automation agent is built for "code-based automation" of user interfaces, mimicking human interaction with software applications.

* **Mechanism:** Executes scripts to navigate, click, and extract data from web or desktop interfaces.
* **Best for:** Web scraping and automating legacy software without APIs.

<Note>
  This is a very complex and a premium feature, please [contact our support team](mailto:support@sarasfinance.com).
</Note>

### 5. External ERP Agent

This agent serves as a bridge between the platform and enterprise resource planning systems. It facilitates high-level data operations.

* **Operations supported:** `Fetch`, `Update`, and `Create`.
* **Best for:** Real-time inventory updates, fetching client records, or creating invoices in systems like SAP or Oracle.

<Note>
  Please note that as of today, we support only JSON based response from the external ERP system. To consume non JSON response, we recommend using Simple Python agents.
</Note>

### 6. Calculation Engine Agent

To eliminate the risk of LLM-based mathematical hallucinations, the Calculation Engine ensures 100% accuracy for heavy computations.

* **Mechanism:** Deterministic execution of mathematical formulas and heavy data processing.
* **Best for:** Financial auditing, tax calculations, and scientific data analysis.

<Note>
  This is a very complex and a premium feature, please [contact our support team](mailto:support@sarasfinance.com).
</Note>

***

## Slot Resolution

Agents are made dynamic through **Slot Resolution**. This feature allows you to inject variables into an agent's instructions or scripts at runtime.

To specify a slot, use the double-curly brace syntax: `{{variable_name}}`.

<Note>
  Ensure that the variable names used in your slots match the keys provided in the workflow's global context.
</Note>

***

## Structured Response

To maintain data integrity across workflows, agents can be configured to return a **Structured Response**. By providing a JSON example of the expected output, you force the agent to bypass conversational filler and return machine-readable data.

### Configuration Example

When setting up your agent, define the response schema like so:

```json theme={null}
{
  "summary": "some string",
  "total_amount": 1",
  "items_found": [1, 2, 3],
  "is_urgent": true
}
```