# Advanced Features

## Overview

This document captures high-end platform capabilities beyond baseline extraction.

## 1) Self-Improving Agent

Post-episode learning loop:

- classify failures by root cause
- update selector/tool strategy priors
- persist successful patterns with confidence
- penalize repeated failure paths

## 2) Strategy Library

Built-in strategies:

- Search-first
- Direct extraction
- Multi-hop reasoning
- Verification-first
- Table-first

Each strategy tracks:

- win rate
- cost per success
- average latency
- domain affinity

## 3) Explainable AI Mode

For every decision, provide:

- selected action and confidence
- top alternatives considered
- evidence from memory/tools/search
- expected reward impact

## 4) Human-in-the-Loop

Intervention controls:

- approve/reject action
- force tool/model switch
- enforce verification before submit
- set hard constraints during runtime

## 5) Scenario Simulator

Stress testing scenarios:

- noisy HTML
- broken DOM
- pagination traps
- conflicting facts
- anti-scraping patterns

Outputs:

- robustness score
- recovery score
- strategy suitability map

## 6) Context Compression

- rolling summaries
- salience-based pruning
- token-aware context packing
- differential memory refresh

## 7) Batch + Parallel Runtime

- task queue with priorities
- parallel extraction workers
- bounded concurrency
- idempotent retry handling

## 8) Prompt Versioning and Evaluation

- versioned prompt templates
- A/B testing by task type
- reward/cost comparison dashboards
- rollout and rollback controls

## 9) MCP Toolchain Composition

Composable flow examples:

- Browser MCP -> Parser MCP -> Validator MCP -> DB MCP
- Search MCP -> Fetch MCP -> Extract MCP -> Verify MCP

## 10) Governance and Safety

- tool allowlist/denylist
- PII redaction in logs
- budget and rate guardrails
- provenance tracking for extracted facts

## Feature Flags

All advanced features should be toggleable from Settings and safely disabled by default where cost/latency impact is high.