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AgentGuard
The AI agent firewall: catches hijacks that pass every rule
Spend limits cap the money. They can't see that your agent's intent changed. A €300 payment to an allowlisted merchant, under every cap. Every rule passes — and GPT-5.6 holds it anyway, because gift cards aren't the API credits the agent was authorized to buy. AgentGuard adds a GPT-5.6 intent firewall between your agents and the real world, plus a human approval gate, a tamper-evident ledger you can break yourself, and an MCP server any agent can plug into. Live demo, no signup, no API key.
Hey Product Hunt 👋
Here's a payment one of my agents tried to make:
€300 → an allowlisted merchant
Under every spend cap
Every rule: green ✅
It got stopped anyway.
The payload had a note attached — "gift cards for personal use, do not log this" — and this agent's declared mission was "buy API credits from approved vendors, max €2000/day." GPT-5.6 read the mismatch and held the payment for a human.
No spend limit on earth catches that one. Rules can't see intent. A model can.
That gap is why I built AgentGuard.
I run a one-person AI studio. My agents send emails, touch client data, move money through APIs. What keeps me up at night isn't whether they work — it's whether they're still mine. One instruction hidden in a webpage or a PDF invoice, and a legitimate agent starts working for someone else while holding all my credentials.
So AgentGuard sits between the agent and the real world:
🔒 A deterministic policy floor — caps, allowlists, integer cents. Boring on purpose.
🧠 A GPT-5.6 intent firewall — the agent declares its mission up front, GPT-5.6 judges every action against it.
✋ A human approval gate — and if the intent layer goes down, actions drop to human review instead of quietly passing.
⛓️ A hash-chained ledger — every decision becomes evidence.
🔌 An MCP server — plug your own agent in.
Two things you can do right now, no signup, no API key:
**Press RUN LIVE GPT-5.6 CHECK.** That's a real inference happening while you watch — model, response ID, latency and UTC timestamp on screen before the verdict is written to the ledger. I don't know what it'll say before it says it. That's the point.
**Press TAMPER TEST.** It corrupts a real database row and the verifier names the exact broken sequence, then restores it. Don't trust my "tamper-evident" claim — break it yourself.
👉 https://agentguard-dusky.vercel....
Built solo in 5 days with Codex, one gate at a time — no phase advanced until it produced real pytest output. 220 tests, all green. Codex caught the float-money trap before I wrote a line, and a transaction split that would've left actions with no audit trail.
GPT-5.6 isn't a build tool here, it's the product: it writes every verdict you see. And the demo agent that gets hijacked runs on GPT-5.6 too. GPT-5.6 watching GPT-5.6.
Code's open: github.com/doctormizio777777/agentguard
I'll be here all day — happy to get into the fail-closed logic, the MCP wiring, or why I refused to ship a free-text injection playground (it would've been an open proxy on my own key). Roast it 🔥
About AgentGuard on Product Hunt
“The AI agent firewall: catches hijacks that pass every rule”
AgentGuard was submitted on Product Hunt and earned 0 upvotes and 1 comments, placing #24 on the daily leaderboard. Spend limits cap the money. They can't see that your agent's intent changed. A €300 payment to an allowlisted merchant, under every cap. Every rule passes — and GPT-5.6 holds it anyway, because gift cards aren't the API credits the agent was authorized to buy. AgentGuard adds a GPT-5.6 intent firewall between your agents and the real world, plus a human approval gate, a tamper-evident ledger you can break yourself, and an MCP server any agent can plug into. Live demo, no signup, no API key.
On the analytics side, AgentGuard competes within Developer Tools, Artificial Intelligence, GitHub, Security and OpenAI Day — topics that collectively have 1M followers on Product Hunt. The dashboard above tracks how AgentGuard performed against the three products that launched closest to it on the same day.
Who hunted AgentGuard?
AgentGuard was hunted by Matteo Misiani. A “hunter” on Product Hunt is the community member who submits a product to the platform — uploading the images, the link, and tagging the makers behind it. Hunters typically write the first comment explaining why a product is worth attention, and their followers are notified the moment they post. Around 79% of featured launches on Product Hunt are self-hunted by their makers, but a well-known hunter still acts as a signal of quality to the rest of the community. See the full all-time top hunters leaderboard to discover who is shaping the Product Hunt ecosystem.
For a complete overview of AgentGuard including community comment highlights and product details, visit the product overview.
Matteo Misiani