Stop finding out your AI had a problem after something goes wrong.
Know exactly what your AI agents are allowed to do, and where the risks are, before it affects your customers, your reputation, or your revenue.
of enterprise apps will embed AI agents by end of 2026, up from under 5% in 2025.
Gartner / BCG
of enterprises have mature governance models for autonomous agents.
McKinsey / Gartner
rise in AI-related incidents from 2024 to 2025, and the trajectory is accelerating.
AI Incidents Database
Your engineers built the AI. Nobody designed how it behaves when things go wrong.
Most companies deploying AI agents know what their agents are supposed to do. Very few know what they're actually allowed to do.
That gap, between what AI is designed to do and what it's permitted to do, is where things go wrong. Not because the model fails, but because nobody made the operational design decisions consciously.
Nobody defined what actions need human approval. Nobody mapped the escalation path. Nobody set the threshold for when a human takes over. The agent just does what it's allowed to do, and nobody checked what that was.
"Air Canada's chatbot fabricated a bereavement discount policy and promised it to a grieving customer. The tribunal ruled the airline liable. The chatbot wasn't broken, it did exactly what it was allowed to do."
No visibility into what they can do
You know what the agent is supposed to do. You probably don't know every system it can touch, every action it can take, or every edge case it might hit.
No approval layer on high-risk actions
Your AI SDR can send pricing emails. Your support agent can issue refunds. Nobody explicitly decided what needs a human in the loop before it happens.
No escalation path when things get ambiguous
When the agent hits a situation it can't resolve, what does it do? For most deployments, the honest answer is: nobody knows.
No plan for when it goes wrong at scale
One bad interaction is a support ticket. The same failure happening 500 times before anyone notices is a crisis. Most teams have no early warning system.
We help you see exactly what your AI agents are doing, find where that's risky, and put the right controls in place, before something forces your hand.
Not another observability tool
Tracing and monitoring tools answer "what happened?" Nodal answers the question your team isn't asking: "should this have been possible at all?"
Tell your engineers what the agent did, traces, token usage, latency, errors.
Tells you, the founder, whether it should have been allowed to, and what happens if it does it wrong at scale.
Six questions it answers
What is this agent actually designed to do?
We document the full purpose, scope, and business impact of every agent in your stack, often revealing agents nobody had fully mapped.
What is it allowed to do?
We map every action the agent can take, every system it can access, and every decision it can make without human involvement. Most teams are surprised by how much authority their agents have by default.
How do humans stay in control?
We identify every point where a human should be in the loop, and whether those checkpoints actually exist. Most deployments have fewer than anyone assumed.
What happens when the agent gets stuck?
We map the escalation path: what triggers a handoff, what happens when confidence drops, what the agent does when it hits an ambiguous situation. For most deployments: nobody knows.
What does failure actually look like?
We build specific, tangible failure scenarios, not abstract risk categories. Real situations, real consequences, written in plain language so every stakeholder can understand them.
Can it recover when something goes wrong?
We assess whether your agents have the controls, rollback procedures, and fallback processes needed to contain and recover from an incident, before it becomes a crisis.
From "we think it's fine" to we know exactly where we're exposed.
Before working with Nodal, most founders are in the same position: they know AI is creating value, they suspect there are gaps, and they don't have a clear picture of either.
You can answer the hard questions.
Board asking about AI risk? Investor doing due diligence? Enterprise client requesting documentation? You have a clear, documented answer, not "we think it's fine."
You can scale AI usage without scaling the risk.
Every new agent you add sits on top of a control framework that already exists. You're not starting from scratch each time and hoping for the best.
You have a response plan if something goes wrong.
Not just a report about what could happen, a blueprint of exactly what's controlled, what needs approval, and what escalates to a human. The difference between a recoverable incident and a damaging one is usually whether that infrastructure was in place.
One AI support agent with uncapped refund authority running for a week before anyone notices isn't a £5,000 problem. It's a £50,000 problem, or more, depending on your customer volume. The assessment costs less than one prevented failure at scale.
Start with an assessment. Scale into ongoing trust.
Every engagement begins with the Trust Assessment, it's the map everything else is built on.
Honest about who we're for.
Book a call if you
- +Have AI agents in production and can't fully document what they're authorised to do
- +Can't answer questions from your board, investors, or enterprise clients about AI risk
- +Have scaled AI usage quickly and know the operational design hasn't kept pace
- +Are preparing to scale AI usage and want controls in place before you grow the problem
- +Something has already gone slightly wrong and you want the full picture
Look elsewhere if you
- –Are still evaluating whether to use AI, come back when you're deploying
- –Need a cybersecurity review or penetration test, different specialism
- –Want a generic compliance report, we produce operational intelligence, not paperwork
- –Won't act on the findings, the report is only valuable if the controls get implemented
Questions founders ask
We already use LangSmith / Arize / an observability tool.+
Those tools tell your engineers what the agent did technically, traces, token usage, latency, errors. We map what your agent is allowed to do, what systems it can touch, and what happens if it gets bad input, then identify where controls are missing. It's less "what happened" and more "should this have been possible?", a question most teams using observability tools still haven't answered.
We're a small team, we know what our agents are doing.+
That's usually true at the start, with two agents and one person who built both. The gaps emerge as you add agents, connect systems, or the person who built it moves on. Most gaps I find aren't surprising in isolation, they're obvious once mapped. The problem is no one had mapped them. Could you confidently walk your board through exactly what each agent is authorised to do right now?
What's the ROI on this?+
Depends what your agents do. A support agent handling 500 tickets a week that mis-resolves 3% because of an escalation gap is 15 bad outcomes a week, compounding. An outbound agent sending wrong pricing to 50 prospects costs more than the error, it's sales-cycle disruption and reputational damage. The real question is what one operational failure in your AI stack would cost.
How does the free risk review work?+
It's a 30-minute call. We ask six questions about your AI setup. By the end, you'll know whether you have gaps worth addressing, and we'll both know whether the full assessment is the right fit.
Book a free 30-minute risk review
We'll ask you six questions about your AI setup. By the end, you'll know whether you have gaps worth addressing, and we'll know whether the full assessment is the right fit.
Not ready for a call?
Start with the free AI Agent Exposure Report, six questions that tell you exactly where your agents are exposed. Takes 10 minutes. Instant result.
Get your free Exposure Report →