.redact catches data leaks before your customers doRequests, responses, and tool calls are analyzed in real time, with sensitive data removed before it leaves your environment.
.guard stops attacks before they executePrompt injections, backdoors, and jailbreaks are intercepted as they happen, blocking malicious input at runtime.
.verify keeps every output aligned with your truthModel responses are continuously checked against trusted sources to ensure accuracy and compliance before delivery.
Add capabilities to any system with a single HTTP request. Language-agnostic and framework-agnostic. Works with existing infrastructure without code changes.
Native Python and TypeScript libraries for seamless integration. Embed security checks directly into your application with typed responses and async support.
Command-line tool for testing and automation. Validate prompts locally, integrate with CI/CD pipelines, or batch-process data in your workflow.
Everything you need to know about Superagent
Over the past weeks, we've spoken with dozens of developers who are building AI agents and LLM-powered products. The notes below come directly from those conversations and transcripts.
The gap between a working demo and a reliable product is vast. Andrej Karpathy calls this the 'march of nines' — when every increase in reliability takes as much work as all the previous ones combined. This is the hidden engineering challenge behind every production AI system.
Most agents today rely on large, general-purpose models built to do everything. If your agent has a single, well-defined job, it should also have a model designed for that job. This is the case for small language models: models that handle one task, run locally, and can be retrained as your data evolves.
You've enabled Azure Content Safety or Llama Guard. Your AI agent still isn't secure. Here's why content filtering isn't enough when your AI takes actions.