Great power. Greater responsibility.
AI is game-changing on the upside and on the risk. We build for the upside without pretending the risk isn't real — because a fast agent doing the wrong thing is worse than no agent at all.
SFM governs every build with four controls: scoped permissions so an agent can only do what its task needs, red-team testing against prompt injection before you rely on it, human approval gates on any action that spends, sends, or deletes, and complete audit trails of every agent action. Governance is how the speed stays safe.
The failures that made 2026 were fast agents doing the wrong thing.
How every SFM build is governed.
- Scoped permissions — an agent gets only the access its task needs, never more. Excessive permission is how one bad input becomes one bad action.
- Red-team testing — we attack the agent with injection payloads and edge cases before you ever rely on it.
- Human oversight — approval gates on any action that spends, sends, deletes, or otherwise carries real blast radius.
- Complete audit trails — every decision and tool call logged, timestamped, and explainable. You can prove what your agent did, when, and why.
SFM vs. a weekend no-code agent.
| Weekend no-code agent | SFM build | |
|---|---|---|
| Time to live | An afternoon | ~2 weeks, tested |
| When it's wrong | Wires money to a stranger to "keep the customer happy" | Blocked at a gate, logged, caught in red-team first |
| Permissions | Whatever was easiest to connect | Scoped to the task, nothing more |
| Proof of what it did | None | Complete audit trail |
| Who owns it | The platform it's built on | You — shipped into your environment |
Let's build what your business actually needs.
One working session on the highest-value problem, and a straight read on whether an agent is even the right answer.
Don't gamble on the flash. Rely on the experience.
FAQ
What is prompt injection?
Prompt injection is when an attacker hides instructions in content an agent reads, an email, a web page, a document, and the agent follows them, taking an action it was never meant to take. It is the top-ranked risk for LLM applications.
How do you test an agent before it goes live?
We red-team it: we attack it with injection payloads and edge cases, score its actions against expected behavior, and confirm the guardrails hold before you rely on it.
What is in an audit trail?
Every decision the agent makes and every tool it calls, timestamped and explainable, so you can prove exactly what the agent did, when, and why, after the fact.
Doesn't all this governance slow the build down?
It's why the build is safe to move fast. We ship a first agent in about two weeks with the controls built in from day one, rather than bolting safety on after something goes wrong.