Inkog vs Invariant Labs
Both built for agents. Different approaches.
Invariant Labs focuses on runtime guardrails and agent monitoring — enforcing policies on running agents. Inkog focuses on static analysis — finding vulnerabilities in agent code before deployment. Both are purpose-built for AI agent security but work at different stages of the development lifecycle.
Feature Comparison
| Feature | Inkog | Invariant Labs |
|---|---|---|
| Static code analysis (pre-deployment) | ||
| Runtime guardrails (production) | ||
| Agent trace analysis | ||
| AI agent loop detection | ||
| MCP server auditing | ||
| EU AI Act compliance reports | ||
| CI/CD integration | ||
| SARIF output | ||
| Agent framework adapters (15+) | ||
| AGENTS.md governance verification | ||
| Multi-agent delegation graph | ||
| Policy enforcement | Pre-deploy | Runtime |
When to Use Each Tool
Use Invariant Labs when...
Use Invariant Labs when you need runtime monitoring and guardrails for agents already in production. Their approach works well for enforcing policies on agent behavior during execution.
Use Inkog when...
Use Inkog in your development pipeline to catch vulnerabilities before they reach production. Static analysis finds structural issues — missing loop bounds, unsafe data flows, compliance gaps — that no amount of runtime monitoring can fix retroactively.
Frequently Asked Questions
How do Inkog and Invariant Labs differ?
Inkog is shift-left static analysis — scan code during development and CI/CD. Invariant Labs provides runtime guardrails — enforce policies on running agents. Inkog finds the bugs, Invariant monitors the deployment. They're complementary.
Which should I use first?
Start with Inkog. Finding and fixing vulnerabilities in code is cheaper and faster than catching them in production. Add runtime monitoring as your agent deployment matures.