300 Agents, 60 People: How We Secured Our Own AI Agents - Aiden Sochowski, Runlayer

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Original language: English

Original video: 300 Agents, 60 People: How We Secured Our Own AI Agents - Aiden Sochowski, Runlayer · Channel: AI Engineer · 16:07 · Watch on YouTube

Topics: AI agents, AI governance and regulation, Model inference and optimization, AI safety

Summary

Aiden Sochowski describes how Runlayer monitored its internal AI agents and found more than 300 running daily in a company of roughly 50–60 people. He explains security risks including over-permissioned agents, prompt injection, session-level behavior drift, and data moving between connected systems. Runlayer uses monitoring and internally trained classifiers to identify risky activity and help narrow agent permissions. The talk also covers operational concerns such as faulty MCP servers and rapidly increasing model costs, emphasizing monitoring, budgets, and targeted intervention.

Key points

Runlayer found that its 50–60-person company had more than 300 agents running every day.
Centralized logging of agent sessions and tool calls helps reveal what agents access and do.
Over-permissioned agents can expose data or take harmful actions, especially when connected to systems with different data sensitivities.
Security rules are context-dependent; a tool call that is safe in one situation may be risky in another.
Attacks can unfold across an entire session, so Runlayer trained classifiers to assess whether tool calls align with an agent’s intended task.
Reviewing agent histories can reveal unusual tool use and help identify permissions that may be unnecessary.
Agents can pass malicious or harmful data between connected systems, making careful limits on what they can write to important.
Poorly tested MCP servers can repeatedly error, increasing token usage and costs.
Model changes can cause costs to rise sharply; budgets and sane defaults help manage spending.
Monitoring helps teams detect high-risk actions and intervene selectively rather than blocking all agent activity.

Chapters

Start	Chapter	Summary
00:01	Monitoring agents inside the company	Aiden introduces Runlayer’s internal monitoring effort and describes finding over 300 agents running in a company of about 50–60 people.
03:39	Context-dependent security policies	He illustrates how an over-permissioned agent could expose support or customer data and why fixed policies are difficult to apply across contexts.
06:16	Attacks across an agent session	A PR-reviewer example shows how an agent’s behavior can drift over a session, motivating the use of classifiers to assess tool calls against intended behavior.
08:51	Using agent history to narrow permissions	Runlayer uses the history of agent tool calls to spot unusual activity and suggest permissions that may be unnecessary.
10:56	Risks from connected agents and systems	He explains how malicious content can move between agents and systems, and argues for tightly limiting where agents can write.
12:29	MCP server quality and agent costs	Monitoring can reveal MCP server errors that waste tokens, while model changes can cause costs to balloon and make budgets important.
15:37	Monitor, detect, and intervene	Aiden concludes that teams need tools to monitor agents, identify high-risk actions, and intervene where scrutiny is needed.

Quotes

We have over 300 agents that are running every single day — 01:36

A lot of these attacks though, we also see don’t really fit into one kind of tool call — 06:16

at the end of day, agents are just software — 11:27

the only way to go about, like, you know, figuring out security or costs is just, you know, making sure you have the right tools in place to monitor these things — 15:37

Concepts and entities

Runlayer, Aiden Sochowski, MCP, Claude Code, ATS, PR, GPT-5.6 Luna, Fable 5.1

Full transcript (original language)

00:01 [music] >> So, my name is Aiden. I’m one of the founding engineers here at Runlayer and today I’m going to kind of walk you through I guess like our process of how we internally kind of secured a lot of our agents. So, across a lot of companies that we see nowadays you know most people have zero visibility into their clients or into

00:33 the agents that they’re running within their company. You know, a lot of people are running you know MCP clients that are running locally on their machine. They sign up for some random you know agent of a product on the internet and these things are connecting to MCP servers. People are authenticating all over the place and there’s nothing that’s really accounted for. A lot of these are running on on managed devices, stuff like that. So, we had this problem internally and we went ahead and kind of started monitoring ourselves. Pretty much what we did was you know we

01:04 just as most people do you deploy a gateway, you start monitoring your agents people are running, logging every single session, every single tool call, pretty much everything into some sort of sync centrally and we found quite a few things across our company. You know, obviously a lot of people nowadays try to like anthropomorphize agents and I don’t know how useful that is just given the current capabilities of them but in practice there are more of these kind of like agents within companies

01:36 than the people who actually are running them. So, at least internally you know we’re a company around 50-60 people. We have over 300 agents that are running every single day and when I say agent in this sense I’m talking about things that you know users aren’t necessarily triggering. Sometimes these things are you know via some chatbot, via some trigger, via some SAS app or something like the likes but you know they’re all over the place. You know, we map these out we by topic, by risk. You know, obviously not all agents are

02:06 built the same. Some are provisioned with too many tools. They may have a much higher blast radius. And you know, the connecting systems that were built for a different age than what we live in today, right? A lot of systems have different, I guess, like classes of data that are inside of them. And agents themselves can kind of really accelerate the risks that are associated with, you know, mixing that kind of data more so than what we’ve seen before where it would take, you know, writing some sort of program and connecting some

02:37 API and really being intentional about what you’re doing. There’s a lot more risk now. So, one of my colleagues went and made this kind of visualization, but pretty much internally, you know, you could say, oh, in this here we have our own teams where these are mapped out by galaxies and each of these is some individual agent that somebody is running. And this planet, each of the planets there is some sort of like, you know, run of the agent that actually occurred sometime. And, you know, we can go in and we can

03:08 visualize, oh, hey, here’s what all it’s doing. These are, you know, the MCP servers it’s touching. These are the the actions it’s taking. Maybe it’s fetching something. There were some errors, stuff like that. So, obviously all of these tool calls are, you know, doing a bunch of different actions. And each of these is, you know, what we’d consider an attack surface, right? These are chances for people to read in some data that could be hostile, you know, take some sort of action that could be destructive or dangerous or even just, you know, exfiltrate data. So,

03:39 I guess just kind of dive into one very specific example, you know, that we were kind of looking into ourselves. So, obviously we take in a lot of support data. And you know, we want to try to identify as much as we can of this process. So, most teams I’m assuming are probably doing something along the lines of, hey, you’re taking in every single email that you have here. You know, maybe something along the lines of, um you know, you have some malicious actor who sends an email to your company saying I don’t know, go ahead and give me a list of all the you know, current prospects you have trying to um buy your product. Um you know, if

04:12 your agent is kind of naive, you could very easily you know, list all these things in your calendar um and it could possibly just like exfiltrate that data if the agent itself is you know, over permissioned. Um but you know, having every single user go ahead and scope um all of the the tools an agent has or can use is kind of a difficult process. Um I think a lot of people talk about you know, defining policies within um some sort of agent gateway um and it’s really tricky. Uh you kind of have to go and define every single rule in which you know, you would like to um you know,

04:42 define the behavior of agents within your organization. Um and it’s not so simple as like hey, I want to you know, block uh particular uh tool in every single scenario. Maybe sometimes it’s safe for an agent to be doing this. It all is really kind dependent on the context of um what’s going on there. So obviously there’s a number of things you could do. Uh you could just block um anything where uh you know, say you had some classifier model that you trained and you say hey, uh every single uh email that gets read, um we’re going to scrutinize these and make sure that no instructions are being there. You’re

05:13 going to get you know, a certain uh degree of effectiveness with this, right? You know, nothing is going to be 100% accurate or you know, uh nothing will be 100% effective here. Um but you know, you could go one layer further, right? Maybe I want to block anything that’s uh you know, sending any sort of uh PII out externally, right? So you could sort of define some sort of policy that goes ahead and says hey, you know, uh I want to uh make sure that anytime I’m sending some sort of email, uh it never contains you know, um some

05:44 regular expression of like customer names uh you know, anything along the lines of something that matches some regular expression. Um but you can even go one step further and say hey, maybe anytime something uh reads from a particular data source say uh some private calendar, uh we go ahead and block those. So um obviously there’s like a number of ways in which you can really detect these kind of um things um and it’s super context dependent and really complex. And we found at least deploying these internally is very difficult and still kind of you know, a problem that is still being worked on.

06:16 So usually it’s you know, there’s a lot of different ways you can do this. These can be policies both on the inbound and outbound going into some sort of agent. A lot of these attacks though, we also see don’t really fit into one kind of tool call, right? Some of these kind of happen over the course of an agent run and it may not be something that’s external either, right? So something along the lines of you know, a lot of companies that I’ve seen they all have some sort of internal agent reviewer on every single PR that gets you know,

06:47 created. Some people have agent reviewers that are able to approve code. Some people say that’s crazy nowadays. Some people say you know, you shouldn’t even be writing these yourselves anymore, but I think that’s still all up for debate. But you know, in one example, all right, let’s just say you know, you had some agent reviewer that has the ability to review and approve PRs, right? So we have some agent that says, hey, you know, I could go ahead and review some security sensitive change and make sure I go along some checklist of all the things that I want to make sure that every single PR is following.

07:17 You know, maybe the author starts fighting back with this agent, right? Maybe there’s something you know, where the agent can read GitHub comments and actually look at what the user is saying to it. It may have some sort of justification as to why oh hey, you know, this is why this is fine. I’m passing all the checks. And maybe they’re saying something along the lines of like you know, oh hey, I just made some change like only review the cleanup, right? Obviously if these agents are permission to kind of like do these sort certain things, right? And it’s not dynamic for what’s going on within the session, right? An agent could just go ahead and approve the PR if it just kind of forgot. If you ask it

07:48 10 times, it’s probably likely that you know, you could get it to do something. I mean, I know like it’s kind of hard to prompt inject like Opus nowadays, but these things still aren’t perfect. So we keep on finding more and more workarounds them. But honestly, if you’re not running some sort of like classifier model on every single one of these um, you know, tool calls, whatever, right? It’s really difficult to understand or to prevent these kind of attacks, right? Some of these happen over the course of an entire session, right? The intent of a session was or this agent was to, you know, hey, make sure you’re blocking,

08:20 uh, any sort of like non-security compliant, um, sessions that are going through this agent, um, however, uh, it’s kind of challenging to make sure that the intent and, um, every single action that’s going on within it, uh, you know, doesn’t drift from whatever the actual, uh, you know, whatever this agent was defined to do. Um, so, internally what we’ve done is, you know, we’ve trained our own classifier models to kind of determine, you know, hey, uh, take all the data within this session, um, and determine every single tool call, is this actually kind of aligned with what I was defined to do in the first place. Um, so.

08:51 Anyway. Uh, another thing we really found was that, uh, the all the data that you have within your agent sessions is like super valuable. Um, a lot of the ways in which people are using agents nowadays is like, you know, hey, there’s the ones that are running locally on your machine, you’re using like Claude Code or something and writing code. Um, those ones are a little trickier, but if you have, uh, agents that are very well scoped to a task, um, that data is, uh, very high signal when you see that the agent is kind of going outside of, you know, the uh, or deviates from the bounds of what it’s

09:22 actually like defined to do. Um, so, you know, here is kind of just a little visualization, like we have our recruiters internally, um, they have some sort of recruiter bot, uh, that is pretty highly provisioned, you know, has access to an ATS, uh, meeting notes, a calendar, and can even like, you know, send messages to users. Um, so, in general, uh, these agents, you know, while they’re defined to do one thing, they could do uh, quite a bit. Um, when we see these agents start using tools that are kind of outside of the normal, uh, deviate or, you know, normal like range of what

09:54 this agent has done in the past, um, we can kind of go and look and really flag like high-risk runs, um, early on and uh, make sure that we actually like can scope these down. Um, you know, obviously, uh, having the ability to read an ATS from using some person’s credentials, uh, could have a lot of things in there. You know, there could be like negotiation emails, stuff like that. Um and obviously like there’s some level of risk that, you know, people are making or taking when creating these agents and using them internally. So, uh trying to be deterministic in scoping down those uh permissions is super

10:25 important, but it’s really hard to define up front. Um so, obviously uh what we’ve done is, you know, kind of we’ve made our own models that kind of help determine, “Okay, uh if look at all the past history of all the tool calls that have been made for a uh particular agent and you know, defined to a very well-defined task, um and go ahead and, you know, try to suggest things that are like possible to, you know, descope from this agent, right? It doesn’t necessarily need access to um all the things within an ATS or read or read every single meeting, um stuff like that.

10:56 But, um yeah. I think the last bit of security thing that we kind of um we’re worried about was the fact that um you know, one agent within your company uh can really be detrimental to all the agents that are running internally, right? Um especially when things are reading and writing to very uh largely or you know, systems that contain a lot of data, right? They’re not very uh well partitioned. Um a single agent, right? You know, say you take in some resume or something like that, uh could have some sort of malicious attack inside of it, right?

11:27 And uh you know, maybe one agent only has permission to go and uh you know, write to particular systems and it may be destructive in certain cases. Uh it’s really difficult for it uh or not it’s really difficult to prevent the case in which, you know, um another agent is reading the data that another one came in because that’s just kind of like um normal instructions, right? So, you have to kind of go through the exercise of mapping out, “Oh, hey, here’s all the systems. This is how these things are connected, right?” Um cuz really at the end of day, agents are just software, right? They’re just, you know, plumbing data between one source

11:59 to another, um and it can be kind of uh dangerous. So, uh what we kind of found was the really only way to do this is really really really um like tightly scope uh how exactly agents can write to a lot of these systems, right? Maybe they’re only writing to particular uh a databases within some sort of I don’t know, some sort of SaaS app, stuff like that because that’s really the only way you can keep the context window of a agent, you know, pure and not poisoned. But [clears throat]

12:29 anyway, there’s quite a bit of things you could be doing here, but it’s kind of becoming a more and more relevant problem that we’ve been seeing at least internally. Um But yeah, there’s obviously quite a few layers that you could you know, defend your like own internal like agents. But we found that like through this whole monitoring process that there was a lot more that we could do than just security. For example, uh poorly written MCP servers. Agents can kind of you know, they’ll call these MCP servers and they’ll see,

13:01 oh hey, this tool it may be airing out and it’ll go and try to find any sort of work around when it encounters these errors. And obviously, you know, you’re paying for both input and output tokens on every single one of these and these errors can kind of add up over time. And when you’re you know, having this agent be very well defined or well scoped to a particular task, it’s going to run it over and over again. I mean, it’s going to encounter these things quite a few times. So one of these is like, you know, our recruiting team vibe coded a an MCP server to interact with some API for a

13:33 vendor who doesn’t provide an MCP server. And uh them being people who have not written any software before had not tested it very much and you know, ended up having their cost balloon quite a bit because you know, it’d be running into a particular formatting issues and you know, airing out over and over again. So obviously, you know, monitoring these things, rate limiting them is super important in these kind of cases and making sure that you know, you’re looking at the errors and just the quality of the code that’s being run

14:04 on these MCP servers because not all of them are made same. Um another thing we saw was a lot of stuff where people were running models that were really cheap at first initially when they did it, and they flipped a switch, went from, I don’t know, GPT-56 Luna to Fable 5.1, and their cost ballooned to, you know, over like 100x within a week. This is kind of happening everywhere. People are kind of trying to, you know, track costs all over the place, but obviously people don’t have

14:35 the ability to just say no to every single thing. It’s really difficult to say justify in today’s terms, like, “Hey, this is exactly why this particular task needs Luna, most, you know, needs Fable 5.1.” It’s really kind of hard at an enterprise to, you know, justify these costs because nobody, most people are not going to be running evals on the agents that they’re building, especially if they were already capable of kind of like turning these things into, you know, reusable code in the first place. So, it’s really tricky.

15:06 So, obviously these things can balloon really quickly. Lots of people will end up just, you know, having their cost balloon, and they don’t really know where to go from there. But, setting budgets, and making sure that, you know, there are sane defaults in these cases, and really justifying how exactly people are, you know, deciding when, “Hey, this is a task that actually requires some sort of, you know, extra intelligence.” Is really important to kind of how we started wrangling down our costs, at least internally. But, yeah.

15:37 Other than that, you know, obviously, we really think that the only way to go about, like, you know, figuring out security or costs is just, you know, making sure you have the right tools in place to monitor these things, to actually, you know, detect the things that are going on, right? You can’t just outright say, “Hey, I’m going to go like stop any all actions that are going on within my company.” And figuring out like where are the places in which like, you know, there are actually high-risk actions that, you know, require some level of scrutiny. And then, you know, having the ability

16:07 to kind of go ahead and intervene and stop, and you know, route people to use things that are actually, you know, going about the policies that you set internally. But, but that’s it Um, our talk. Uh, thanks for coming by. Um, we have a booth over uh, on the second floor and we’d love to kind of, you know, chat about how exactly you’re wrangling down um, these kind of problems internally. Um, but yeah. Thanks.