I've been working in the AI agent governance space for a while and noticed there's no good comparison of the available tools. So I made one.
Here's the landscape as of April 2026:
The Tools
asqav - ML-DSA-65 (quantum-safe) signed audit trails. Hash-chained so you can't omit entries. Policy enforcement blocks actions before execution. Works with LangChain, CrewAI, OpenAI Agents, Haystack, LiteLLM.
Microsoft Agent Governance Toolkit - Policy-as-code with Cedar, SQLite audit logging, multi-language SDKs. No cryptographic signing but the most mature policy engine.
AgentMint - Ed25519 signing with RFC 3161 timestamps. Content scanning for 23 patterns (PII, injection, credentials). Zero external dependencies.
Aira - Ed25519 + RFC 3161. Hosted receipt layer so you don't run your own TSA. Maps to EU AI Act Articles 12, 13, 14, 86.
Guardrails AI / NeMo Guardrails - Output validation and safety rails. No signing or audit trails but great for controlling what agents say.
The Real Difference
The split is between tools that prove what happened (asqav, AgentMint, Aira) and tools that control what happens (MS AGT, Guardrails, NeMo).
For compliance, you need proof. EU AI Act Article 12 requires "tamper-evident" logging. That word matters - a SQLite database isn't tamper-evident. A signed, hash-chained audit trail is.
For safety, you need control. Guardrails and policy engines stop bad things from happening in real-time.
Best setup for regulated industries: both layers together.
When to Pick What
Building for finance/healthcare/government: You need signing. Pick based on whether quantum-safe matters for your retention period (10+ years = ML-DSA, under 5 years = Ed25519 is fine).
Building for general enterprise: MS Agent Governance Toolkit has the broadest language support and the most mature policy engine.
Building a quick proof of concept: Guardrails AI is the fastest to integrate.
Full comparison table: github.com/jagmarques/ai-agent-governance-landscape
Top comments (1)
Great comparison! Governance is becoming critical as AI agents get more autonomous. I work with AI video generation tools and the prompt engineering side alone needs solid guardrails — one poorly structured prompt can produce completely off-brand content at scale. Would love to see how these governance frameworks handle creative AI outputs, not just code-level agents.