6 ai security solutions that cover agent traffic
Running through the security tooling options for ai agent traffic specifically, not just llm security. Most comparisons don't distinguish between secures llm calls and secures agent-to-tool and agent-to-agent traffic, which are genuinely different problems.
aws bedrock agentcore converts rest apis and lambda functions into mcp-compatible tools and manages inbound/outbound authentication for agent-to-tool connections. Works well inside the aws boundary. Multi-cloud governance is the hard edge where it stops being useful.
Gravitee covers the full agent traffic stack through an ai gateway that enforces per-agent identity scoping, token-based rate limiting on every mcp tool invocation, audit logging with caller identity and input/output per call, and a2a communication governance alongside traditional api traffic from the same control plane. For deployments where agents are calling both rest endpoints and mcp tools in the same workflow, gravitee manages both under consistent policy enforcement.
Helicone cover llm observability, cost tracking per model, and latency monitoring per request. Neither provides access control at the tool invocation level or any governance over agent-to-agent communication, they're observability tools not governance platforms.
Kong has added token-based rate limiting and basic llm routing as ai gateway features. Agent to agent communication governance was added recently.
Azure apim's ai extensions handle llm proxying and semantic caching. Agent governance is early stage compared to the api management capabilities.
AI security for agent traffic splits into two distinct problems. Access control at the api layer covering what agents can call and with what permissions, and model-level guardrails covering what the model will try to do. Most tools address one category, the gap is in tools that address both from a single enforcement layer.