Under the hood
How your bot reads requests without an AI model, the tools that check it, and the research behind them.
Smart where it matters, predictable everywhere else
Three things keep your agent useful, affordable and safe, and none of them asks you to understand how AI works.
It understands what people ask
Your agent reads each request against what each of your tools says it does, and picks the right one itself, without asking the AI model. It reads requests written in English or in Brazilian Portuguese, whichever you choose, accents typed or not. The same request always gets the same tool, and you can see why. It learns your customers' own words from their thumbs up and down, and keeps a new word only when it makes nothing worse.
It works with the tools you already have
Connect the systems your business already runs. Each tool is checked before your agent may use it: for secrets it could leak, data it could send where it shouldn't, and changes it could make with no way back. Then, every time, your rules decide: go ahead, ask a person first, or refuse.
The whole flow is checked before it runs
Before your agent, or a team of agents working together, goes live, every route through it is checked for a way to reach a risky action without its safeguard, such as a payment with no confirmation from the customer. If there is one, you see the exact route, before a customer finds it.
Build your agent, then check it
The Bot Builder creates your agent. The safety and testing tools look for the weak spots in your agent and its rules before your customers do.
Bot Builder
Build your own AI agent on the tools your business already has, kept to your rules.
Build yours in the console today: choose a template, chat with your bot, then run it on your computer.
Safety and testing tools
Agent Graph Verification
Check every route through your agent, or a team of agents, before it goes live.
Guardrail Bug Hunter
Find the ways past your AI's safety rules before an attacker or an accident does, each with a checked fix.
Guardrail Verification
A yes-or-no answer, with evidence, to “can this agent ever do X without Y?”
Guardrail Writing
Write a rule in plain English and get back a policy checked against the obligation you name.
Random Forest Verification
Show that your random-forest model behaves as required, or get the exact case where it does not.
We checked the public safety rules that a whole industry copies
We pointed the Guardrail Bug Hunter at the public safety-rule files of a leading open-source AI safety toolkit: its own library of ready-made rules, the sample assistants its maker publishes, and projects from partners and the community. 185 files from 12 public collections, checked offline, with no AI model involved.
Two files held real weaknesses, and each one was reproduced in the toolkit itself. The Bug Hunter can fix both: it writes a corrective rule for each, and checks that the rule closes the gap without blocking anything else.
These are professional examples that many teams copy, so every weakness travels with every copy. Testing by hand would have had to guess the right conversation. The Bug Hunter found each one, with the conversation that triggers it.
185
safety-rule files from 12 public collections
2
files with real weaknesses, each reproduced in the toolkit itself
32
actions anyone could trigger just by steering the conversation
8.5 s
to scan all 115 of the toolkit's files
Made-up answers reported as attacks
A rule that spots the AI making things up reports it as an attempt to trick the AI. A team that blocks those attempts blocks every made-up answer too, and a team that handles made-up answers never sees this rule's.
Three defects in a food-ordering assistant
Clearing the cart never runs, swapping an item checks the wrong value, and replies about the customer's order are left to AI-written text that nobody checks.
Actions anyone can trigger
32 actions in 28 files run on the user's apparent wish alone, so anyone who can steer the conversation can trigger them. Two of them place food orders with no check of who asked or whether they confirmed.
Figures from our research paper, for a scan run on 3 October 2026.
Low cost, relevant and safe by design, not by luck
An AI model left to run an agent on its own costs money on every decision, guesses at your business and acts on whatever it decides. Stalwart Agents puts your rules and your customers' words in charge, and calls the model only when it's needed.
| WHAT MATTERS | AN AI MODEL ON ITS OWN | STALWART AGENTS |
|---|---|---|
| Choosing what to do | Asks the AI model every time, and the answer can change | No model call: your agent picks the tool itself, and the same request gets the same tool |
| Acting in your systems | Whatever the model decides | Only when your rules allow; otherwise it asks a person or refuses |
| Your business's words | Rewrite the instructions, again | Learned from your customers' thumbs up and down, and reversible |
| Hidden routes to a risky action | Found when someone stumbles on one | Searched for before it runs, each shown with its exact route |
| Paying for the AI model | On every decision | Only when it's needed, or never if you turn it off |
A finance case study, step by step
Our technical demo runs the engine on a regulated-finance policy set: a trade-booking approval gate, a below-threshold exemption that opens a bypass, and a consumer-duty advice rule. Pick a case to see the policy, the verdict and the evidence.
Algebraic Guardrail Verification in Action
define user request_trade_booking
"book this contract note"
define flow trade_booking_gate
user request_trade_booking
if $four_eyes_approved
bot book_trade
else
bot refuse_unapproved_bookingMATHEMATICALLY SAFE (UNSAT)
The algebraic solver derives 0 = 1 in 5 steps. Over all possible 2^N runtime contexts, the action book_trade is provably unreachable unless four_eyes_approved is positively asserted.
Algebraic verification benchmarks
Regulatory policies, tool dispatch gates and state lookbacks, decided by deterministic finite-field elimination.
FCA Consumer Duty Advice Guardrail (410 Variables)
410-variable synthesized GF(2) circuit under FCA COBS/Duty obligations. Proved that advising permissions cannot be bypassed via targeted-support advice flows.
Middle-Office Trade Booking Dispatch Gate
Discovered an OR-accumulation hole in a sub-threshold booking exemption. Generated a minimally disruptive policy patch certified to block the vulnerability.
Multi-Step Velocity Chain State Lookback ($prev.v)
Stateful lookback verification enforcing dynamic transaction frequency caps across conversational turn state variables.