cva-patterns-context

What problem does it solve?

Integrating diverse data sources (files, databases, APIs) into AI agent prompts efficiently, while optimizing performance through caching and ensuring strict LGPD-compliant handling of sensitive data. This prevents redundant API calls, reduces latency, and protects user privacy, making your AI systems more robust and cost-effective.

Core Features & Use Cases

• Multi-Source Integration: Seamlessly combine static files, database queries, external API data, and previous agent results into a unified context for your AI agents.
• Performance Optimization: Implement aggressive caching strategies (e.g., TTL caches for query and API data) to reduce latency by 33% and cut costs by 58% on repeated data access.
• LGPD Compliance: Automatically detect, redact, or mask sensitive PII (e.g., CPF, health records) before LLM processing, ensuring data protection and audit readiness.
• Use Case: A healthcare AI agent needs to access patient profiles (database), scientific articles (PubMed API), and internal guidelines (static files). This Skill manages these contexts, caches frequently accessed data, and sanitizes sensitive patient information before it reaches the LLM.

Quick Start

Load static LGPD compliance guidelines into an agent's context. Implement a 24-hour TTL cache for PubMed API results. Ensure sensitive patient data is redacted before being sent to the LLM.