Question 1

What is static analysis for AI agents?

Accepted Answer

Static analysis for AI agents examines agent code and configuration without executing it, detecting logic flaws like infinite loops, prompt injection paths, and missing guardrails. Unlike runtime monitoring or observability, static analysis catches issues before deployment—acting as a pre-flight check for your agents.

Question 2

What is agent readiness?

Accepted Answer

Agent readiness refers to verifying that AI agents are production-ready before deployment. This includes checking for logic flaws (infinite loops, recursion risks), security issues (prompt injection, unconstrained tools), and compliance requirements. Inkog provides the pre-flight check that answers: is this agent ready to ship?

Question 3

How does Inkog detect infinite loops in AI agents?

Accepted Answer

Inkog uses static analysis to trace control flow through your agent code, identifying cycles and recursion patterns that could cause infinite loops. It detects missing exit conditions, unbounded retries, and circular tool chains—before you ship to production.

Question 4

How does Inkog detect prompt injection?

Accepted Answer

Inkog uses static analysis to trace data flow from user inputs to LLM prompts, identifying injection points where untrusted data enters prompt templates without proper sanitization. It detects both direct and indirect prompt injection patterns in LangChain, CrewAI, and other frameworks.

Question 5

What AI agent frameworks does Inkog support?

Accepted Answer

Inkog supports 15+ AI agent frameworks including LangChain, LangGraph, CrewAI, AutoGen, PydanticAI, Google ADK, OpenAI Agents, and visual builders like n8n, Flowise, and Langflow. One scanner works across all frameworks.

Question 6

What is MCP server auditing?

Accepted Answer

MCP (Model Context Protocol) server auditing examines the tools and servers that provide capabilities to AI agents. Inkog scans MCP servers for logic flaws, missing authorization checks, and security risks before you install them—ensuring your agent's tools are production-ready.

Question 7

Is Inkog compliant with EU AI Act?

Accepted Answer

Yes, Inkog generates compliance reports aligned with EU AI Act requirements including Article 14 (human oversight), Article 15 (accuracy and robustness), and Article 12 (record-keeping). It also maps findings to NIST AI RMF and OWASP LLM Top 10.

Question 8

How is Inkog different from observability tools?

Accepted Answer

Observability tools like LangSmith monitor agents after deployment, showing you what went wrong. Inkog is a pre-flight check—it finds logic flaws before you ship. We're pre-commit, not post-mortem. We're complementary to observability, not a replacement.

Question 9

How is Inkog different from guardrails?

Accepted Answer

Guardrails add runtime checks that can block or modify agent behavior in production. Inkog analyzes your code statically—we fix the code itself, not add runtime overhead. We help you ship agents that don't need guardrails to catch infinite loops.

Integrations

LangChain

LangGraph

CrewAI

AutoGen

OpenAI Agents SDK

Semantic Kernel

LlamaIndex

Haystack

DSPy

Phidata

Smolagents

Vellum

PydanticAI

Google ADK

SuperAGI

BabyAGI

AgentGPT

n8n

Flowise

Langflow

Dify

Microsoft Copilot Studio

Salesforce Agentforce

GitHub Actions

GitLab CI

Pre-commit Hooks

Jenkins

CircleCI

npx / npm

Docker

Homebrew

Go CLI

REST API

Python SDK

JavaScript SDK

MCP Server

OWASP LLM Top 10

EU AI Act

NIST AI RMF

ISO/IEC 42001

AWS

Google Cloud

Azure AI Foundry

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