Your agents are already making outbound calls you cannot see. The moment an LLM gets tools (shell access, HTTP clients, MCP connections, messaging webhooks), it can reach any host its network path allows. The question for platform and DevSecOps teams is no longer whether to control that egress, but whether to build the control plane yourself or buy it. This is an honest breakdown of the total cost of ownership and the coverage gaps on both sides, written for the engineer who will actually own the on-call rotation.
What an AI agent egress firewall actually has to do
Before you can compare build versus buy, you need an accurate spec. An effective agent egress firewall is not a single feature; it is a stack of capabilities that have to work together at runtime, inline, on every outbound call. At minimum it must:
- Terminate and inspect TLS so it can read the actual request, not just the destination host and port.
- Enforce a default-deny allowlist of domains, IPs, and routes the agent is permitted to reach.
- Parse application-layer intent (HTTP bodies, MCP tool arguments, webhook payloads), not just L3/L4 metadata.
- Detect and block exfiltration, including encoded secrets, API keys, and PII leaving on the wire.
- Gate risky actions for human approval (human-in-the-loop) without killing throughput.
- Emit tamper-evident audit logs of every action, suitable for SIEM ingestion and compliance evidence.
- Do all of this with sub-millisecond-to-low-millisecond overhead so production teams do not rip it out.
If you build, you own every one of these. If you buy, you own integration and policy. That distinction is the whole decision. For the conceptual grounding on why egress is the right boundary, see our explainer on egress filtering for LLM applications.
The DIY stack: Squid, Envoy, and OPA
Most teams who build start from familiar open-source primitives. The typical assembly looks like this:
Squid or Envoy as the forward proxy
A forward proxy gives you a chokepoint and a place to enforce a domain allowlist. Squid is battle-tested for HTTP egress and ACL-based filtering; Envoy is more programmable and integrates with service mesh. Either can do CONNECT-based tunneling and host allowlisting out of the box. The trouble starts when you need to see inside the request. That means configuring TLS interception with a managed internal CA, distributing trust to every agent runtime, and handling the long tail of services that pin certificates or break under interception. None of that is exotic, but all of it is yours to maintain forever.
OPA for policy
Open Policy Agent lets you express allow/deny logic as code, which is the right instinct: you want policy-as-code guardrails, not a hand-edited config file. But OPA evaluates the request you give it. Feeding it richly parsed agent intent (the actual MCP tool name, the arguments, the decoded body) is work you have to do upstream. OPA does not parse an MCP tools/call frame for you. It does not normalize a base64-wrapped secret. It evaluates structured input, and producing that structured input from raw agent traffic is the hard 80%.
The glue nobody scopes
Then come the pieces that never make it into the initial estimate: a control plane to push policy changes safely, a way to do human-in-the-loop approvals (a queue, a UI, a notification path, a state machine for pending actions), log shipping with integrity guarantees, secret-detection patterns that stay current, DNS-layer monitoring to catch DNS exfiltration, and metadata-endpoint lockdown to stop SSRF against 169.254.169.254. Each is a small project. Together they are a product.
The honest TCO of build
Engineering teams routinely underestimate build cost by anchoring on the proof-of-concept. A Squid container with an allowlist can be standing up in an afternoon. That demo is not the deliverable. The deliverable is a production control plane that other teams trust on the critical path of every agent request. Price it across a realistic horizon:
- Initial build: 2 to 4 engineer-months to reach a defensible v1 covering TLS interception, allowlisting, basic body inspection, and logging. HITL and encoded-secret detection push this further.
- Ongoing maintenance: Plan for a meaningful fraction of an engineer indefinitely. Proxies need upgrades, CA rotation, allowlist curation, and rule tuning as agent frameworks shift. MCP, A2A, and new transports keep arriving.
- On-call cost: This sits inline on production traffic. A bad policy push or a proxy crash is now an outage for every agent workload. That is a new tier-one dependency your team carries.
- Coverage debt: The features you defer (encoded-secret normalization, WebSocket frame inspection, tamper-evident receipts) are exactly the ones that matter during an incident. Deferring them is a silent risk, not a saving.
The build path is viable. It is just rarely as cheap as the first sprint suggests, and the cost is recurring, not one-time.
The honest TCO of buy
Buying is not free of work either, and pretending otherwise is how procurement decisions go wrong. With Agent G you still own:
- Deployment and topology: placing the proxy in the right network position so agent traffic actually routes through it. Our deployment docs cover drop-in patterns, but you own your VPC, mesh, and DNS.
- Policy authorship: deciding what to auto-allow, flag, block, or escalate. Agent G enforces the policy; your team still decides what “good” looks like.
- Integration: wiring approval notifications into your existing tooling and streaming logs into your SIEM.
What you do not own is the hard engineering underneath: TLS interception that does not break tool calls, deep tool-argument inspection, encoded-secret normalization, a built-in HITL approval workflow, and out-of-band tamper-evident logging, delivered with benchmarked low-millisecond overhead. You also do not own keeping pace with new transports and frameworks; that is the vendor's roadmap, not your backlog. Pricing for those capabilities is transparent on the pricing page, and the relevant comparison is not “license fee versus zero”; it is license fee versus the fully loaded cost of the engineers, on-call, and coverage debt you would otherwise carry.
A decision framework
Use these questions to make the call without ego or sunk-cost bias.
Is agent egress security your core product?
If you are building a security product, build. The control plane is your differentiator. For everyone else (fintechs, healthcare platforms, SaaS teams shipping agents), it is undifferentiated heavy lifting that sits on your critical path. Owning a proxy stack does not make your product better; it makes your on-call worse.
How fast do you need coverage?
A credible DIY build to the spec above is a multi-quarter effort before it is trustworthy. If your agents are in production now and you need default-deny egress this quarter, the build timeline is itself a risk.
Can you maintain it at 3am?
This is the question that decides most honest evaluations. An inline egress control that you cannot debug under incident pressure is a liability. If your team cannot confidently own proxy upgrades, CA rotation, rule drift, and the HITL state machine indefinitely, you are not really choosing build; you are choosing future technical debt.
Do you need compliance evidence?
If auditors will ask you to prove human oversight and reconstruct what an agent did, you need tamper-evident, out-of-band logging from day one. Bolting that onto a DIY proxy after the fact is painful. If this is a near-term requirement, it tilts hard toward buy.
The hybrid reality
Build versus buy is rarely binary. A common and sensible pattern: keep your existing network firewall and service mesh for coarse L3/L4 controls, and add a dedicated agent egress firewall for the application-aware enforcement they cannot do. Your mesh sees hosts and ports; it does not see agent intent or tool arguments. Agent G layers on top of that infrastructure rather than replacing it. For the architectural distinction, see what an AI agent firewall is.
The bottom line
Building a v1 egress proxy is genuinely easy. Building and maintaining the full spec (TLS interception, deep tool-argument inspection, encoded-secret detection, human-in-the-loop approval, and tamper-evident logging, all inline at production latency) is a product, not a project. For teams whose core competency is not agent security, the fully loaded cost of build almost always exceeds buy once you price the engineers, the on-call burden, and the coverage debt honestly. If you are scoping this decision, request access to Agent G and compare against your own build estimate. The framework above is the same one we would use.