## Purpose

Define the autonomous agent-driven extraction pipeline — structured field extraction from natural language, schema-based validation via tool calling, autonomous retry logic, and human-in-the-loop clarification.

## Requirements

### Requirement: Structured field extraction from natural language

The agent SHALL extract a predefined set of key-value pairs from user-provided natural language text (e.g., email content) and return them as a structured JSON object.

#### Scenario: All fields extracted successfully

- **WHEN** the user sends a message containing natural language with all required information
- **THEN** the agent returns a JSON object with all predefined fields populated from the text

#### Scenario: Partial extraction

- **WHEN** the user sends a message that contains some but not all required fields
- **THEN** the agent extracts available fields and leaves missing fields as null

### Requirement: Predefined extraction schema

The system SHALL define a fixed set of known field names and types as a strongly-typed C# class. All extraction output MUST conform to this schema.

#### Scenario: Output conforms to schema

- **WHEN** the agent produces extracted fields
- **THEN** every key in the output matches a field defined in the schema and values match expected types

### Requirement: Autonomous validation via tool calling

The agent SHALL validate extracted fields by calling a validation tool function. The validation tool checks that all required fields are present and correctly typed.

#### Scenario: Validation passes

- **WHEN** the agent calls the validation tool with a complete and correct extraction
- **THEN** the tool returns a success result and the agent returns the final output to the user

#### Scenario: Validation fails with fixable errors

- **WHEN** the validation tool returns errors for missing or malformed fields
- **THEN** the agent re-reads the source text and attempts to fix the extraction without user intervention

### Requirement: Autonomous retry with iteration cap

The agent SHALL retry extraction autonomously up to 3 times when validation fails. After exhausting retries, the agent MUST escalate to the user.

#### Scenario: Agent retries and succeeds

- **WHEN** validation fails on the first attempt but the error is recoverable
- **THEN** the agent retries extraction and calls validation again, up to 3 total attempts

#### Scenario: Agent exhausts retries and escalates

- **WHEN** validation fails after 3 attempts
- **THEN** the agent sends a natural language message to the user identifying the specific fields it could not resolve and asking for clarification

### Requirement: Human-in-the-loop clarification

When the agent escalates to the user, the user SHALL be able to provide the missing information in natural language, and the agent SHALL incorporate the clarification and re-attempt extraction.

#### Scenario: User provides clarification

- **WHEN** the agent asks for clarification about missing fields and the user responds
- **THEN** the agent incorporates the user's response into the conversation context and produces an updated extraction

#### Scenario: Clarification via normal chat

- **WHEN** the agent escalates for clarification
- **THEN** the clarification request appears as a regular assistant message in the chat UI, and the user responds via the normal chat input