Akto is increasingly relevant in API security, discovery, MCP exposure, and agentic posture conversations. RAGuard focuses on the runtime question: what an AI agent is allowed to do with those APIs and tools in production.
Maps APIs, agentic surface area, and MCP exposure so AppSec teams can see what AI systems can reach.
Determines whether the agent should actually call that API, in this tenant, with this context, and for this workflow.
API discovery, API security testing, exposure mapping, and emerging agentic and MCP posture conversations for AppSec and platform teams.
Runtime permissions, AI tool governance, tenant-aware execution policies, instruction provenance, and action-level control once agents start using APIs.
| Question | Akto | RAGuard |
|---|---|---|
| Is the primary lens API and exposure management? | Yes | No, runtime governance is primary |
| Does it help discover agentic or MCP exposure? | Yes | Indirectly through runtime control context |
| Does it govern AI agent permissions at runtime? | Limited | Yes |
| Does it mediate tool and workflow execution? | Limited to emerging | Yes |
| Does it enforce tenant-aware AI policy? | Limited | Yes |
| Does it track instruction provenance? | Limited | Yes |
| Does it retain governance evidence for AI behaviour? | Limited | Yes |
Akto is increasingly relevant when buyers are worried about how APIs, MCP endpoints, and internal services are being surfaced to AI systems. That makes it a useful comparison in AppSec and posture-led conversations.
RAGuard takes over at runtime. Once an agent can see an API, the next question is whether it should call that API in this tenant, with this context, for this workflow, and with what evidence trail.
You need API discovery, API testing, posture insights, or an AppSec-led view of where agentic systems can reach inside your environment.
You need to control how AI agents use those APIs and tools in production, with tenant-aware permissions and runtime evidence.