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Certiv vs AI Proxies

Network proxies see HTTP, not intent. They don't see local models. And they don't follow users off-network.

In short

AI proxies sit in the network path and inspect API calls between agents and cloud services. That model breaks for local-model inference, off-network users, and any decision that needs reasoning-chain context. Certiv runs at the point of intent on the endpoint instead.

Capability
Certiv
AI Proxies
Visibility into agent reasoning + intent
Yes - semantic visibility at the source
No - sees HTTP requests after the decision
Sees on-device / local-model traffic
Yes - endpoint-native
No - local inference never hits the proxy
Pre-execution policy enforcement
Yes - blocks before the action runs
Yes, at the network layer (only)
Works for users off-network / on personal Wi-Fi
Yes - endpoint follows the user
No - proxy depends on routing
Deployment model
Single endpoint install
Network reconfiguration + cert pinning + maintenance
TLS inspection for general web traffic
No - different problem
Yes - its core function

AI proxies and gateways borrow the network-control playbook that worked for web security: sit in the path, decrypt, inspect, apply policy. The pattern is familiar and has its place, especially for organizations that already operate a SASE/SSE stack.

Two things break the pattern for agents. The first is location: a growing share of agent work runs on the endpoint with local models and never produces traffic the proxy can see. The second is context: when the proxy does see traffic, it sees one HTTP call in a multi-step reasoning chain, with no way to know what the chain was for. That forces policy to be coarse: block this domain, allow that one.

Certiv replaces network-layer guesswork with endpoint-layer truth. It sees the agent's reasoning chain in full, attaches it to every tool call, and enforces policy semantically: "this agent must not combine private data access with an outbound call", not just "this IP must not be reached". Proxies still have value at the network boundary; they're not the right tool for agent governance.

FAQ

Frequently Asked Questions

Expand to view common questions.

Why not just put a proxy in front of every AI API?
Three problems. First, on-device models (Claude Desktop with local inference, LM Studio, Ollama, embedded agents) never make external calls, so they're invisible to a proxy. Second, when agents do call cloud APIs, the proxy sees the API call but not the reasoning chain that led to it, so policy decisions are coarse. Third, proxies don't follow the user: when they're at home on personal Wi-Fi, the proxy isn't in the path.
Doesn't a proxy enforce policy at runtime too?
A proxy can block traffic. That's runtime enforcement at the network layer. Certiv enforces at the agent layer, with semantic context. A proxy can say "no traffic to api.openai.com from this user"; Certiv can say "no agent action that combines reading our CRM with calling out to an external domain". Different granularity.
How do they fit together?
Many enterprises will run both. Proxies for general web governance, DLP, and known-egress policy. Certiv for AI agent governance: discovery, semantic monitoring, and pre-execution enforcement. Certiv adds context to proxy alerts (which agent triggered this call and why), and proxies add a coarse fallback when an endpoint agent isn't installed.

Keep reading

AI gateway vs runtime assurance

How gateway-layer controls compare with endpoint enforcement, and where each layer stops seeing agent activity.

Our approach

Why the endpoint, not the network, is the right place to enforce agent policy.

The Lethal Trifecta

A pattern that needs reasoning-chain context to detect, which a proxy cannot do.

See the product

How Certiv ships: one install, no proxy, no network reconfig.