docs/features.md

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.