How To Manage AI Agents At Scale (Safely)

It's 10 PM. Do you know where your agents are? Do you know what all your AI agents are doing across every single platform across your entire business? If you're old enough like me, you probably remember the public service announcement prior to the 10 PM news every night. It's 10 PM. Do you know where your children are? And if you're younger, there's thousands of memes on this on TikTok and Instagram. So are agents today a little like children? The corollary is that agents, like children, when they get out into the real world, they start confronting situations they've never seen before. Never before, some no one even thought of. They have to take in new data, sometimes incomplete data or different data than they expect in real time. They may start to interact with new agents added to their environment, agents that were built by different teams, trained in different ways for different reasons and with different rules. And sometimes they can get confused, do unpredictable things, or misinterpret rules, all while trying to make decisions. To keep them safe, they need guidance and rules that keep them in control. You need visibility so you know when to step in, and sometimes you need to be there when they need help. So back to the question. At any moment's notice, like right now, or even if it's 10:00 PM on a Saturday night, do you know what all your AI agents are doing across every single platform they run on in your business? When I ask this question to CFOs, CIOs, heads of AI, almost none of them can answer it. And that's a problem. Because these agents are out doing things, sometimes things you don't anticipate them to do, improving things, sometimes in workflows, sometimes with your customers. And if you can't see them, what are they doing? You won't know one or several of them made a mistake until it's potentially too late. So in this video, I'm going to show you how to safely manage your agents at scale so you have visibility and control into what every single one of them's doing at all times and be able to catch errors, manage escalations, and make critical human-in-the-loop decisions to keep your deployment safe. Let's get into it. There are 4 critical areas of consideration that you need to deploy in your business to enable you to safely scale agents, and 99% of you don't have the infrastructure in your place yet to do this. So let me walk you through it. The first area is agent visibility. Now, there's several reasons almost nobody can answer the question of what all their agents are doing right now. The first is because their agents aren't all in one place. Some run inside the big platforms you already use, like Salesforce, Microsoft, and ServiceNow. And your own team has probably built several custom agents in other systems for the jobs that the platforms don't handle or for workflows that cross between platforms, like a pricing agent or a contract quality agent or warranty replacement and parts inventory agents. All built around the way your business actually works. And because of this, there's no single place to have visibility into every agent across all your workflows. And you probably have to log into every agent individually to see what each one did. And in the case of agent-to-agent workflows, determining which agents did what gets complicated fast because agents make decisions by weighing probabilities in how they make their decisions. They don't make binary decisions like software. What context existed at the time the decision was made becomes complex right out the gate. So do you know if you had a rogue agent last night? How many escalations happened in this business process this week and why? What did your team do in those instances? Did everything go right? Is there a design problem you need to fix? You have no way to answer these questions across a multi-agent system because you never needed this infrastructure before. The second requirement is control. And again, There's nothing in your infrastructure to manage governance and guardrails across multi-agent arrangements and workflows across multiple platforms, multiple applications, multiple workflows, and custom agent environments. There's just no system for operational governance that spans your ecosystem. No system does this in your infrastructure today. No system manages guardrails across multiple levels at the agent level, across groups of multiple agents, and at the organization level, that's all required to act and control transactions in real time. Sometimes individual agents will be doing exactly what you want them to do, but as a group, they, they step on each other or they do something that doesn't match your intent. The only way to ensure this doesn't happen is to have a multi-layer control system connected to every agent, every group of agents, and across your organization. The third requirement is agent escalation management. You don't have a single place to manage agent escalations. This is how you get humans in the loop when you need the humans. Your team members can't and won't look across dozens of queues every day. They won't log into dozens of individual interfaces and agents to get the context or to take required action. They'll get lost in this noise and miss the signals. Agent escalations happen asynchronously because you need to intercede in the real time within the agent's decision process, not after the fact when the damage has been done. So you need to be able to intelligently route each escalation to where value is created fast, where decisions can be made fast. That means where expertise and authority merge. And every escalation for each agent in each workflow, and each guardrail collision will require different people for each escalation. It'll require unique context that will require different sets of decisions and actions that will need to be triggered to resolve the escalation. Sometimes escalations will cross multiple agents, and therefore probably more than one individual, possibly from more than one team, will be required to make the right decision. That means different context for different humans in the loop need to be provided to them in that escalation event. So there's no system in your infrastructure today that brings all of this together. And without a single place to manage this, you will be performing a science project for each and every escalation and chasing people, trying to get them to collaborate and act all after the fact. And if you don't have one place to manage all of this, you also can't learn how to become more efficient and what changes you need to make to improve. And if you can't learn, your escalations repeat, or worse, over time, get ignored. And then where do you go when you need to know how decisions were made? Where can you investigate the system and get real answers? No one will be able to stitch the audit logs together across multiple agents and systems after the fact and get it right. The only way is to have infrastructure that plugs in for visibility and control, and you don't have this in your infrastructure yet. You have no visibility across all your agents, no reliable no governance control, no escalation and routing system that's bulletproof, and now you're being asked to add more agents. With 1 or 2 agents, most companies can fight through all this and handle the chaos manually. But that brings us to the fourth concept: scale. Building and rolling out agents was never the hard part. What's hard is everything happening once you start to scale the number of agents in your ecosystem. Your agents don't work on their own. One agent's decision may affect what another one does next, or several agents working together within a single workflow impact each other's decisions. So what you actually have to watch is not just if agents follow their own rules and individual guardrails, but also what happens between every pair of agents that can potentially affect each other. And there are far more of those pairs than there are agents. Here's the math nobody puts in the boardroom deck. With just 2 agents, you have 1 potential connection between agents to keep track of and control. Pretty straightforward. At 10 agents, you have 45 potential connections to control. At 50 agents, you're at 1,225 potential connections. And at 100 agents, you have 4,950 potential connections. This means Once you're past a few agents, no team can track all of those interactions manually. Everybody worries about one agent doing something crazy on its own, but what actually goes wrong is companies lose track of what their agents are doing together across all these interactions. That's the whole challenge once you start to scale. Governance for any individual agent, governance across a group of agents, and governance across the organization. All at the same time. You need to be able to see every agent at once in one place to do this. You need governance that acts against every agent and against any group of agents in real time. You need to be able to identify when something's wrong. You need to be able to act on it prior to agents making the wrong decisions. There's one more I forgot to include above, but it's a big one. Change. Guess what? All the agents you launch are not going to work perfectly. Every agent signal and escalation is critical to understand. Does the agent, the system of agents need to be updated, improved, or is there a new idea or approach to add and adopt that will provide breakthrough results? You need a system that automatically captures this, all the context, so you can decide if it's a good idea. Should we prioritize it? Do we have the right plan of attack? Is the change approved? Can we ship it? Why your current tools can't help. I know your first instinct is probably that you already have some of the tools for this, and that's a fair instinct, but not one of them actually does the job. There are 3 tools you'll probably think of right off the bat that may solve for part of this, and each one misses the exact thing you need. Let me explain. The first tool is your ticketing system, the one your IT team already runs everything through. It was built to handle one request at a time in a queue, usually triggered and started by a human where a person picks up a ticket, deals with it, and closes it. If there was uncertainty, humans were involved from the get-go, so you could go back to the person that submitted the ticket and ask for more context and collaborate. And most importantly, the human could usually bypass the issue one way or another and move stuck processes forward manually. What if The guardrail collision was triggered by an anomaly between agents in a workflow. Agent escalations are completely different. No human to go back to for additional context, who has authority and the right expertise to make a decision. What was the rule that caused the escalation? What was the individual agent that was involved? What if the guardrail collision was triggered by anomaly between agents in a workflow? And what action do we tell the agents to take As you can see, break-fix support was never designed to watch a bunch of agents all making operational decisions, potentially compounding decisions, all at the same time asynchronously, which is exactly what's happening now when you get multiple connected agents and multiple agents across your business systems and workflows. The second tool is the monitoring that comes built into each platform, like the dashboards inside Salesforce, Microsoft, or ServiceNow or other BI tools. The first problem is each of those systems was designed for a different reality. They show history. When a change in a value of a field triggers something, it shows up in a dashboard. The human on the front end always made the decisions that resulted in the changes. These were binary changes, so pretty straightforward to understand and recreate. But now, agents are making probabilistic decisions in the workflows before data changes. Therefore, governance needs to occur within the decision itself prior to the result, or it's too late. And understanding probabilistic decisions as they compound across the workflow is almost impossible in a dashboard. Nothing in how binary software works was designed for this. And this is a disability all legacy systems have and will have to deal with before they're anywhere ready to address this. The dashboard is a history lesson. Governance happens in real time between when the results show up on the dashboard and the guardrails break during the decisions within individual agents and in the interaction and decisions between these agents. You, you'll never see the breaks. You'll just see the state before a decision is made or the state after a decision is made in any dashboard. Software dashboards and traditional BI have an evolution to go through before being anywhere near ready for the realities of AI agents. I'll do a whole video on this topic later in the series. And the third tool is the AI policies your company wrote up. The document that lays out what your agents are and aren't allowed to do. The thing is, a policy is just words on page. It says what's supposed to happen, but it has no way to show you what your agents are actually doing. You can have the best policy in the world and still have no idea whether your agents are following it because you have no visibility or solution designed to operationalize guardrail management. And because of that, you're blind. Also, each new agent you build likely is making decisions on something new. So each guardrail needs to be designed at the agent level, a level your globalized policies will be too generic or vague to provide proper operational decision guidance. And finally, you don't have a system to help you plan at this agent level while you're designing the workflows, the unique workflows and the unique actions that an agent's allowed to take in your environment. None of your existing tools can show you all your agents together and how they're operating with control and with your team. What you need is one place that connects to every agent, no matter what platform it's running on. And you need a single place to manage operations of the agents. By agent operations, I mean a place to manage the 4 topics I discussed earlier: visibility, control, escalations and scale, and the one I missed above, change. That way, everything your agents are doing operationally shows up and is controlled in one live view. And you don't have to log into 20 different systems to try to check on them. Now, for something like this, I'd recommend using a platform like FourSquads. I built this platform so it connects all your agents across every platform and puts every part of agent operations and decisions in one place. Let me show you how it works. This one screen shows you every decision your agents are making across every platform, so you're not going tool by tool just to see what happened today. Anytime you want to know what your agents are doing, you just look here and you have your answer. It's an intelligent surface, real-time feed of health status and escalations across your entire agent estate. Surfaces only what needs human judgment and routes it to the right human at the right time. It's an intelligent escalation routing system. So guardrail breaches, cross-platform anomalies, semantic Ambiguity and strategic maintenance signals are each routed to the right human automatically at the right time. It's closed loop resolution. Every decision routes back as a real action, a approval, a denial, a rollback, a decision, a change request to agents, or a change to an agent's configuration, or a decision to add a new agent or capability to your workflow. Is also captured so you can start change management. It's one place to ask anything. Ask any question in one place and you have access to all your agents' platforms across all your platforms, decisions they made in collaboration with your human teams and all the history. So what agents triggered guardrail breaches last night and still haven't been resolved? And who approved the guardrail design requirements for those particular breaches? Instant answers. No dashboard required. Every person sees what they need, nothing they don't. The Intelligent Surface isn't a shared inbox. It's a personalized feed to understand your individual role, your team's individual roles, your agents, your controls, your guardrails, your decisions, and your time. It surfaces what's relevant to you today, learns from every decision you make, and routes future escalations faster because it remembers who resolved what and how. It's the polar opposite of a dumb support queue. Force Equals is an intelligence layer purpose-built to operationalize agent and agent-to-human collaboration. By the way, if you want to set this up across your own agents, in the top description, I placed a link where you can set up time with our team and try Force Equals for free. Anyway, let's keep going. What your board and auditors will ask for. Now, if you don't know already, being able to prove what your agents are doing is about to become a hard requirement. And that's because the same question is about to come at you from at least 3 different directions. The first place it'll come from is your board. They're going to start asking hard questions like, who's accountable and what is the process when an agent gets something wrong? And how can you show exactly how a decision got made for any given transaction? The second group of people that are going to start asking you tough questions are your regulators. If you're an industry like financial services, insurance, or healthcare, the people who regulate you aren't waiting around for everyone to police themselves. The rules for how companies have to govern AI are coming fast, and you'll want to be ready to show you're already on top of it. And the third one's audit. Auditors will ask how a transaction happened and how decisions were made across your agent and human workflows. You need to be able to answer these questions in 30 seconds or you're not— in control. Even if everything went perfect, if you can't demonstrate and show exactly how those decisions were made, you're still not in control. Now, the organizations that have the infrastructure in place and can answer these questions confidently are the ones that will trust their AI programs and progressively let their agents run more and more of their businesses, allowing them to make bigger impact. And when you can show exactly what every agent did and you can manage agent operations with control, you can put new agents to work quickly because you're not anxious about what you can't see. So the way to manage your agents safely at scale is to give yourself one place to operationalize visibility, control, escalation, scale, and change management. If you want to deploy this in your business, you can try Force= for free by clicking the first link in the description.