If you are shopping for a Zenity alternative, you are usually looking for one thing that governance and posture platforms do not do: inline enforcement of the agent's outbound network call. Discovery, inventory, and policy reporting tell you what happened. Runtime agent egress control decides, per request, whether the call is allowed, denied, or held for human approval.
What buyers actually mean by a Zenity alternative
Zenity sits in the agent security governance category: it inventories copilots and agents, maps their permissions and data reach, and reports on risky configurations. That is genuinely useful for AI governance programs. The gap teams keep hitting is that governance is an out-of-band judgment about an action that already executed.
When a platform team asks for a Zenity alternative, the requirement list is almost always some combination of these:
- A policy enforcement point that sits on the agent's egress path, not beside it.
- Default-deny domain and destination allowlisting per agent identity.
- Inspection of tool-call arguments and responses, not just the destination host.
- Human-in-the-loop approval for irreversible operations (payments, deletes, production writes).
- Wire-level logs that can be replayed as audit evidence.
That is a different architectural layer. It is the difference between an inventory of agents and a proxy that can return a 403 before an agent posts your customer table to an unapproved webhook. We break the layering down in detail in inference-boundary vs network-boundary AI security.
Zenity vs agent firewall: the enforcement question
The cleanest way to evaluate every option on your shortlist is to ask a single question about each candidate: can it stop the request in flight, with the agent still waiting on the socket? Everything else (dashboards, risk scores, posture findings) is downstream of that capability.
| Approach | What it sees | Blocks a call in flight? | Best fit |
|---|---|---|---|
| Agent governance and posture platforms (Zenity class) | Agent inventory, permissions, connector reach, config risk | No (mostly detect and report, some platform-native actions) | Copilot and low-code agent sprawl visibility |
| Agent G egress firewall | Full outbound request: host, path, headers, body, tool arguments, responses | Yes: allow, deny, redact, or escalate to a human | Custom and framework-built agents in production |
| Prompt classifiers | Model input and output text | Only the model call, not the tool action | Jailbreak and injection detection |
| AI-SPM | Cloud config, model inventory, data exposure | No | Posture reporting and drift |
| LLM gateways | LLM API traffic, tokens, cost, routing | Yes, but only for model endpoints | Model routing, metering, caching |
| DIY proxy plus policy engine | Whatever you write parsers for | Yes, with sustained engineering investment | Teams with a dedicated network platform crew |
| Service mesh egress | Host, port, SNI, mTLS identity | Yes at L3/L4, blind to arguments | Coarse network segmentation |
Zenity alternative shortlist: six options for runtime agent egress control
1. Agent G: zero-trust egress proxy with human-in-the-loop approval
Agent G is a drop-in egress proxy and AI agent firewall. Point the agent's HTTP client, MCP client, or container network at it and every outbound call is evaluated against policy-as-code before it leaves. It resolves the destination, inspects tool-call arguments and MCP responses, runs DLP normalization on bodies (base64, URL encoding, homoglyph folding) to catch encoded secrets, and then applies one of four verdicts: allow, log, block, or escalate.
The escalate path is the piece governance tools structurally cannot provide. The request is parked, a human sees the exact rendered action (method, host, path, arguments), and approves or denies while the agent blocks on the response. See engineering human-in-the-loop approval for risky agent actions for the throughput design, and default-deny egress allowlisting for the policy starting point. MCP-specific enforcement lives in the MCP gateway.
Choose it when: your agents are code (LangChain, LangGraph, CrewAI, OpenAI Agents SDK, coding agents in CI) and you need deterministic prevention plus audit evidence, not a risk report.
2. DIY: Envoy or Squid plus OPA
The honest build option. An egress proxy with TLS interception, an OPA sidecar for decisions, and a log pipeline gets you host-level default-deny in a sprint. What takes quarters is the agent-aware part: parsing JSON tool arguments across frameworks, understanding MCP framing, normalizing encoded payloads, wiring an approval UI with timeouts and audit records, and keeping certificate trust working inside every agent runtime.
Choose it when: you have a network platform team with headroom and a small, stable set of agent egress patterns.
3. Service mesh egress policy (Istio, Envoy, Cilium)
Mesh egress rules are excellent at the layer they operate on: which workload identity may reach which host on which port. They are the right coarse boundary and they cost you almost nothing if the mesh is already deployed. They cannot tell a benign POST /messages from one whose body contains your production database dump, because the argument payload is not part of the policy model.
Choose it when: you want segmentation underneath an application-aware layer, not instead of it.
4. Prompt classifiers and guardrail libraries
Lakera Guard, NeMo Guardrails, LLM Guard, and similar tools score prompts and completions for injection, toxicity, and leakage. They reduce the probability that the model decides to do something stupid. They do not sit on the socket, so a successful indirect injection still produces a real HTTP request. We map that boundary in Agent G vs Lakera Guard.
Choose it when: you want detection at the inference boundary, paired with enforcement at the network boundary.
5. AI-SPM and cloud posture platforms
Posture tooling finds the over-permissioned role, the exposed vector store, the unmanaged model endpoint. It answers questions about configuration state. It is a peer to Zenity rather than a replacement for enforcement, and it is worth keeping in the stack for exactly what it is good at.
Choose it when: your immediate problem is unknown inventory and misconfiguration, not runaway agent actions.
6. LLM gateways (LiteLLM, Portkey, Cloudflare AI Gateway)
Gateways are inline, which makes them feel like enforcement. The scope is the model call: routing, retries, key management, caching, token accounting. The agent's tool calls to Slack, Stripe, an internal admin API, or a third-party MCP server do not traverse the gateway at all. That is the traffic that causes incidents.
Choose it when: you already run a gateway for the LLM leg and add an egress firewall for the action leg. A fuller side-by-side lives on the alternatives page.
An agent security governance alternative you can pilot in five steps
- Observe first. Deploy the proxy in log-only mode for every agent workload. You now have a real destination inventory instead of an assumed one, including shadow MCP servers and hallucinated package registries.
- Derive the allowlist. Promote observed, sanctioned destinations into a per-agent-identity allowlist. Everything else becomes deny by default, including the cloud metadata endpoint at 169.254.169.254.
- Tier the actions. Classify tool calls into auto-allow, log, block, and escalate. Writes, deletes, money movement, and outbound messaging with attachments belong in escalate.
- Wire the approval path. Route escalations to Slack or a webhook with a hard timeout and a default-deny fallback so a missed approval fails closed, not open.
- Ship the evidence. Stream verdict logs (agent identity, destination, arguments, verdict, approver) into your SIEM so auditors get artifacts and detection engineers get signal.
Most teams move from log-only to enforcing on their top three destinations inside a week, because the policy is derived from observed traffic rather than guessed in advance.
How to decide
Keep governance if your problem is that nobody knows how many copilots exist. Add runtime egress enforcement if your problem is that one of them can reach the internet with production credentials and a poisoned context window. Those are not competing purchases, but only one of them can say no at the moment it matters.
The practical test for any Zenity alternative on your list: hand the vendor a scenario where an indirect prompt injection tells the agent to POST a customer record to https://collector.example.net/ingest, then ask what their product does in the 200 milliseconds before that request completes. Deterministic block or human approval is enforcement. An alert is a report.
Frequently Asked Questions
Is Agent G a direct replacement for Zenity?
Not identical in scope. Zenity focuses on agent discovery, permissions, and governance reporting across copilot platforms. Agent G enforces the outbound call inline with default-deny egress, tool-argument inspection, and human approval. Many teams keep governance for inventory and add Agent G for prevention and audit evidence.
What is the difference between Zenity vs agent firewall approaches?
Governance platforms observe and report on agent configuration and activity, generally after the fact. An agent firewall is a policy enforcement point on the egress path that returns a verdict per request, so it can block a destructive or exfiltrating action before the target system ever receives it.
Do I still need prompt injection detection with egress enforcement?
Yes, as defense in depth. Classifiers lower the rate of malicious model decisions; egress enforcement contains the ones that get through. Injection detection is probabilistic at the inference boundary, while allowlists and approval gates are deterministic at the network boundary.
How much latency does inline runtime agent egress control add?
Policy evaluation on an inline proxy is a local decision against compiled rules, typically low single-digit milliseconds, which is negligible next to LLM inference and third-party API round trips. Escalated actions are the exception by design, since they wait on a human.
See it on your own traffic
The fastest way to compare a Zenity alternative against real requirements is to watch your agents' actual egress for a day. Agent G runs in log-only mode first, shows you every destination and tool argument on the wire, then lets you enforce. Request access to the Agent G private beta or start with the product overview and deep tool-argument inspection guide.