Searching for a Lakera Guard alternative usually means one thing: you have prompt classification in place but the agent can still take actions you never see or approve. Lakera Guard inspects prompts and model outputs to detect injection and unsafe content. Agent G sits on the network egress path and enforces what the agent actually does: every outbound tool call, API request, and data transfer.
Prompt classifier vs egress proxy: two different boundaries
The core distinction is where each product operates. A prompt classifier lives at the inference boundary, scoring text as it enters and leaves the model. An egress proxy lives at the network boundary, inspecting and gating the concrete actions the agent takes after the model decides what to do. These are complementary layers, not competitors, but they stop very different failures.
Lakera Guard is strong at the first mile: catching known jailbreak patterns, flagging PII in prompts, and scoring output toxicity. What it cannot do is observe the HTTP POST to an attacker webhook, the DNS lookup that tunnels data out, or the DROP TABLE that a compromised tool argument carries. By the time the agent forms a network request, the classifier has already handed control to the runtime.
Where the Lakera vs agent firewall gap shows up
Consider an indirect prompt injection buried in a retrieved document. A classifier may or may not flag the injected text depending on training coverage. Even when detection fires, the agent framework often proceeds with the tool call anyway because detection is advisory, not blocking. Agent G takes the opposite posture: default-deny egress, where an outbound call to an unapproved host simply does not leave the box, regardless of what the model was convinced to do.
This is the difference between a probabilistic detector and a deterministic enforcement point. A prompt classifier answers a question (does this text look malicious?). An egress proxy answers a different one (is this specific action allowed by policy, right now, for this agent identity?).
Prompt classifier vs egress proxy: side by side
| Capability | Lakera Guard | Agent G |
|---|---|---|
| Operating boundary | Inference (prompt and output) | Network egress (outbound calls) |
| Primary mechanism | ML classification and scoring | Policy-as-code allow, deny, gate |
| Blocks unauthorized tool calls | No (advisory detection) | Yes (inline, deterministic) |
| Inspects tool arguments on the wire | No | Yes |
| Stops DNS and encoded exfiltration | No | Yes |
| Human-in-the-loop approval gate | No | Yes |
| Signed audit evidence of every action | Limited | Yes (out-of-band receipts) |
| Enforces per-agent identity at egress | No | Yes |
Action-layer prevention that a Lakera alternative needs
The reason platform teams look past prompt-only tooling is coverage of the action layer. Agent G intercepts the agent's traffic and applies deterministic controls before anything leaves your network. Concretely, it enforces:
- Default-deny egress: only allowlisted domains, hosts, and ports are reachable, so a poisoned prompt cannot reach an attacker endpoint.
- Deep tool-argument inspection: the actual payload of a tool call is parsed and matched against policy, catching a wire transfer amount or a destructive SQL statement.
- Human-in-the-loop approval: risky actions pause and route to a human for approve or deny, with the decision recorded.
- Egress DLP: credentials, API keys, and encoded secrets are detected and blocked on the way out.
- Verifiable action receipts: every allowed and denied action is logged out-of-band as tamper-evident audit evidence.
How to combine both layers
The strongest architecture is not either-or. Use these steps to layer detection and enforcement:
- Keep or add a prompt classifier at the inference boundary to reduce obvious injection and unsafe input reaching the model.
- Treat every classifier verdict as advisory input, never as a hard control on the agent's actions.
- Route all agent outbound traffic through Agent G as a drop-in egress proxy with default-deny.
- Write policy-as-code that allowlists exactly the domains and tools the agent needs, and gate high-stakes actions for human approval.
- Stream Agent G's egress logs and action receipts to your SIEM for detection, alerting, and audit evidence.
With this split, the classifier handles the fuzzy text-similarity problem it is good at, and the egress proxy handles the deterministic policy question it is built for. Neither layer has to pretend to do the other's job.
Why egress enforcement is the reliable backstop
Prompt injection defense will keep improving, but it is fundamentally a detection problem with an unbounded input space. Egress enforcement is a containment problem with a bounded, declarable policy. Even if a novel jailbreak slips past every classifier, an agent that can only reach three approved domains, cannot exfiltrate a secret, and must get human sign-off for a wire transfer has a tiny blast radius. That is the property Agent G guarantees and a prompt classifier structurally cannot.
Frequently Asked Questions
Is Agent G a drop-in Lakera Guard alternative?
Not exactly a replacement, but a different and often more critical layer. Lakera Guard classifies prompts and outputs at inference. Agent G enforces actions at network egress. Many teams keep a classifier for input hygiene and add Agent G for deterministic blocking, approval gates, and audit evidence on the wire.
What does egress enforcement catch that a prompt classifier misses?
Egress enforcement catches the concrete outbound action: calls to unapproved domains, DNS tunneling, encoded secret exfiltration, destructive tool arguments, and SSRF to metadata endpoints. A prompt classifier never sees these because they happen after the model decides and the runtime executes.
Does adding an egress proxy slow down my agents?
Agent G is engineered for low overhead inline, so the added latency per call is minimal relative to typical LLM and tool round trips. Because it operates on the network path rather than re-scoring text, the enforcement decision is a fast deterministic policy lookup, not another model inference.
Can I run policy-as-code instead of tuning a classifier?
Yes. Agent G uses versioned, Git-managed policy-as-code to declare allowed domains, tools, arguments, and approval requirements. This gives deterministic, auditable enforcement, unlike a probabilistic classifier whose behavior shifts with model updates and threshold tuning.
To compare the underlying boundaries in depth, read our breakdown of the inference boundary vs the network boundary, see how we handle deep tool-argument inspection, and review the default-deny egress allowlist that anchors the whole model. You can also survey the field on our alternatives page or explore the MCP gateway.
If prompt classification alone is leaving your agents free to act without oversight, Agent G closes the gap at the wire. Request access to the Agent G private beta and start enforcing what your agents actually do.