The RIGHT Way to Build AI Guardrails in 2026

If you're running AI agents across your business, probably tried to set up guardrails for them. For example, a rule that won't let an agent approve a big discount without a human signing off. Guardrails are surprisingly complex and the way agents interact with them is sometimes unexpected. And this is a blocker to organizations rolling out agents at scale. Now look, I've spent 20 years in enterprise software and now I help organizations set up their AI agents to make sure companies stay in control while they deploy automation and are able to make impact and deliver outcomes from their AI investment. So in this video, I'll show you the right way to set up guardrails that covers all your agents at once so you can stop mistakes before they happen. I'll also review the new technology infrastructure you need to deploy to ensure this works in your business. Let's get to it. All right, so the reason an agent or a system of multiple agents in a workflow can make a mistake in the first place is simple. AI agents run inside big platforms you already use like Salesforce, Microsoft, or ServiceNow and other SaaS applications. And each agent built in and deployed in those systems only follows the rules assigned to it inside the platform where it's built. Your team has probably also built custom agents for jobs those platforms don't handle, and those will follow their own separate rules that you assign to them. Each agent looks at the guardrails that are assigned to it when it runs, but it doesn't see the guardrails assigned to the other agents or in other platforms. And that becomes a problem because your agents don't work alone. They're constantly giving each other work. So the moment one agent finishes its part and gives the work to the next one, the first agent's guardrail has no idea what happens after that. No guardrail that you've deployed is watching the whole path that a decision takes as it moves through a workflow in your business so 2 of your agents can each pass their own check while 2 decisions combined don't match your intent. The risk. So let me show you exactly how that plays out using an example you've probably run into some version of in your business. Say one of your agents, Agent 1, is a pricing agent. Its guardrail is never discount more than 15% without a human approval. And another agent handles retention inside Microsoft, Agent 2. It's a retention agent. And its guardrail is never issue more than 10% $10,000 in service credits per account per quarter without approval. Both these guardrails work perfectly independently and correctly guide each agent. Now let's say a customer comes in to do a renewal. Agent 1, the pricing agent, notices a churn risk in an enterprise account is flagged and the agent applies a 12% loyalty discount to retain them under their threshold. Guardrail passes, no escalation. Everything for that agent runs correctly. Agent 2, the retention agent, the same account had a support SLA breach last month. The agent issues an $8,000 credit under the threshold. Its guardrail passes, no escalation. It handled that situation correctly. So both agents did exactly what they were supposed to do. What actually happened was that the account now has a stacked 12% discount and an $8K credit in the same quarter. A combined concession that doesn't match the intent. It blows through the company's max margin erosion policy for that account tier. Neither agent did anything wrong, neither guardrail misfired. Each one only had visibility into its own action and against its own threshold. But why nothing caught it? The pricing agent has no idea the retention agent touched this account. The retention agent has no idea a discount was already live. The problem with how everyone is deploying guardrails to agents creates this problem, and you can't solve it without new purpose-built technology. The issue is that there's No set guardrails whose job is working at a higher level above a group of agents that are running independently and at a higher level is able to govern across a group to ensure the combined work across the agents matches intent. The failure is in the space between them, and agent-level guardrails are structurally blind to that space because they were never designed and deployed to look across multiple agents, only within one agent at a time. Why this matters. This isn't a rule that failed. It's a rule that was never written because you'd have to know in advance which combinations of independently fine actions add up to a problem. You can't hardcode that. What you need is something watching for the pattern. Same account, multiple agents, concession stacking in a window and escalating and routing that up before it becomes a quarter-end surprise in the margin report. Now look, that example is just one version of a much bigger issue because guardrails fail in 3 different ways commonly in almost Everyone is only thinking about the first one. The first way a guardrail fails is that an agent just breaks a rule or does something that was clearly told not to. But well-designed controls inside each agent already catch that one on their own. So that's one that is pretty straightforward to address. The second way is the way we just talked about where two agents each stay inside their own rules, but the two decisions combined break a policy and nothing catches it because no guardrail is looking at both agents at the same time. And this second type is the one that costs you the most because everything looks completely normal right up until someone finds that unapproved deal weeks later. The third way a guardrail fails is when an agent runs into a situation no one wrote a rule for, like a type of customer it's never seen or a request that doesn't match any policy you have. The agent doesn't break any rule and the agent makes its best guess and keeps going. And you probably don't find out it guessed wrong until that decision has already caused damage, like a refund. The agent should never have issued or promised to a customer you just can't keep. So what are the new requirements to get guardrails right and keep agents safe? So if a guardrail on each individual agent can't catch this, then what can? The answer is you need architecture that assigns guardrails at multiple levels and spans your entire agent estate. It needs to be system agnostic, able to control all your agents no matter where they run, be it in Salesforce, Microsoft, or any other platform or custom agent. One place to control and manage guardrails. This enables your ability to control guardrails assigned at the agent level, guardrails assigned for a group of agents, guardrails assigned for a higher level of an organization. Your architecture needs to span to and connect to all your agents, whether they are custom made in your SaaS platforms, so you can control all of them from a single place. By implementing this architecture, all your guardrails are in one place. They cover every agent at once. No matter where the agents actually run. So one system is watching all of your agents individually and also watching over them collectively simultaneously. That way, when your Salesforce agent approves a discount and your contract agent Microsoft approves a change, your guardrails govern both of those decisions individually while also seeing both those decisions collectively. And it can stop the action the moment the two of them add up and impact. That way, when your Salesforce agent approves a discount and your contract agent in Microsoft approves a change. Your guardrails govern both of these decisions individually while also seeing both of these decisions collectively, and it can stop the action the moment the two of them add up and impact a higher-level rule. This is actually what I built my platform FourSequels to do. It connects all of your agents across every platform they run on and watches every single decision they make from one place. It enables you to manage guardrails at multiple levels across individual agents, groups of agents, and across the organizations, and Since it sees every interaction, it also provides a single place to manage escalations when you need to get humans in the loop. Let me show you what that looks like. You can click into any agent and it'll show you every decision your agents are making across Salesforce, Microsoft, and your own custom tools all in one place. So you're not logging into 20 different systems just to see what your agents did today. And you can also open any decision and see every agent that worked on it and exactly what each one did right there in one view. Now let's send that same discounted deal we talked about earlier through again and watch what Foresee Force catches. The discount comes in from your sales agent. The credit comes in from your retention agent. And because both are being watched at the same time, it catches that the combination of the two of them together should result in a human weighing in. So Force holds that deal there and makes sure the right human sees it and takes action on it before any of it becoming final. On top of catching the problem, you also get a complete record of every decision without anyone having to write anything down. So if anyone ever asks why a certain decision got made 6 months ago, you can pull up exactly exactly what each agent did and who signed off on any escalation in just a few seconds. By the way, if you want to try Force Equals yourself, I'll leave a link to it in the top of the description. Anyway, let's keep going. Now, even with all the right guardrails in place at the right levels across all your agents and systems, some decisions are still going to require a human to sign off on. When you get a guardrail collision and it triggers a need to get a human to make a decision, before it goes any further. The question then becomes who that decision goes to and how quickly can you resolve it. And the way a lot of companies deal with this today is super inefficient and just won't work for asynchronous running agents at any scale. Right now, most companies set themselves up so escalations fire in a place that requires individual queue management for every agent or that a ticket gets created and dropped into a ticket queue or an email thread or a Slack channel. Huge problem is that ticket shows up with limited context, especially when it's a higher-level guardrail that triggered the escalation and there's no ability directly from that to take action. So whoever ends up dealing with the issue has to figure out what the agents were even doing in the first place, what went wrong, and go searching through a few different systems just to make sense of it. And usually all that has to happen prior to deciding who actually needs to handle the decision and it gets reassigned and someone now has to go through this complex cognitive load all over again. Over again. These are all hard things. Remember, all the easy work doesn't get escalated because agents handle them. Purely oversight approvals are very straightforward, but most of the escalations are firing when something isn't going right. We're talking about when guardrail rules break or do not define how to make a decision or fire off from a higher level. These are also usually business decisions, so it's usually someone in the line of business or compliance or legal or a combination of multiple departments that brings both the experience and the authority to make the call. It's not IT. By the time somebody finally gets around to making a decision, hours or even days have gone by, and that whole process that the agent is supposed to automate has just been on hold the entire time waiting for an answer. Hopefully the agents don't just proceed or make their own call in this void. Hopefully this isn't holding up thousands of transactions. So the right way to handle this is to send that escalation straight to the exact person who can make the call with everything that happened already attached, with all of the context from all the different agents and workflows that participated prior to the escalation, with the ability to collaborate on decisions built into the interface, with the ability to capture decisions capturing who was involved, and with the ability to directly trigger actions as a result of making the decision so that you can be really efficient. Every time for every escalation all in one place. This requires a new technology that does not exist yet in your IT stack. The UI needs to be hyper-intelligent. That way the humans in the loop get everything in one place, what every agent did, the exact decision that needs to be made, and the ability to action what happens next depending upon what they decide. They can approve it, reject it right from where they are without wasting hours trying to figure out what happened or having to track down the other system or the associated systems to apply the action. This is critical as escalations need to be efficient and personalized for every human in the loop. And if you're one of those humans and you don't need to be involved in one escalation, spare you the noise. But if you need to be involved, give you everything in one place every time with all the tools you need to make the call and activate the right actions. Actions may be different for every type of escalation and every agent. So The whole system has to be more intelligent at the UI level than any prior business application you've ever used. It needs to adjust to the escalation system and agent automatically and the individual it's sending the escalation to. It needs to be easy to use if you're in the office or in the middle of your commute. And this is exactly what we built in FourSquads. And the whole system gets better the longer you run it. Since every one of these decisions runs through the same place, it starts to learn who handles what, where expertise meets authority, and it sends each new decision to the right person faster and faster. But what's even better is when the same kinds of escalations keep coming back to a human over and over, that's a signal, that's a sign something in your setup needs to change. Escalations often highlight the gaps in your process and point to where updates, changes, and additions can create value. Normally a fix like that gets mentioned once in a Slack message and completely forgotten and the same problem just keeps happening week after week. But with Force Equals, that same problem gets identified and added straight to your team's project portfolio queue of potential projects with all the context. So it doesn't just get forgotten. This could be an idea to change an agent's capability, rules, guardrails at any level, workflow, or adding another agent into the system to tackle a newly identified task. From there, those ideas get combined with your business strategy to determine how to prioritize them amongst all the other projects and ideas in your queue that you're evaluating. It then helps you plan these projects so you get great stakeholder alignment, including governance and guardrail design if required for the new agents or the changes to the agents that you're recommending, proved and built, and the problem finally gets solved and your agents stop making the same mistakes over and over and your business gets better step by step. So if you want the the right architecture and technology to set your guardrails up just like what we just discussed, where one system watches all of your agents together and stops mistakes and escalates when required to the right people at the right time, with the ability to intelligently flex to every type of agent and escalation, every type of workflow, and every type of human in the loop for the decisions and sign-offs, all in one system. That makes it easy. If you want that, you can try ForceEquals for free by clicking the link in the description. You get agent visibility, agent control, intelligent escalation management all in one place. And when you have visibility and control and every escalation gets to the right person at the right time, you can finally roll out AI agents everywhere you want and trust them to run no matter how many you add. So again, you can try ForceEquals for free by clicking the first link below this video. And if you want to see how to safely scale hundreds of AI agents to work across your whole company, then check out this next video.