They focus on securing the agent runtime and detecting threats. Trustabl focuses on hardening the tools agents use so they are production-safe, policy-compliant, and resilient by design. We prevent problems at the source rather than only detecting them at runtime.
Yes. Trustabl generates audit-ready metadata, including structured logging schemas, data lineage, retention policies, and SLSA attestations that make compliance evidence much easier to produce.
No. Skills (SKILL.md) teach the agent how to perform a task or workflow. Trustabl Agent Analyzer (TAA) makes the tools themselves reliable, safe, and production-ready. Skills focus on process. TAA focuses on resilience, validation, policy, observability, and supply-chain trust. The two are highly complementary — great skills need hardened tools underneath them.
Yes. Trustabl is essentially a specialized linter for AI agents. While traditional linters (like ESLint or Ruff) focus on code style, syntax, and general bugs, Trustabl analyzes your AI agents, tools, prompts, and SDK configurations for reliability, safety, and production readiness — flagging patterns that expose you to prompt injection, missing timeouts, tool misconfigurations, and guardrail gaps that standard linters miss. Think of it as "ESLint for AI agents" — it runs in CI/CD, gives clear explanations and fix suggestions, and helps you ship safer, more robust agentic systems.
