Your AI Agent Has No Nervous System — Matt Gibiec, Dynatrace

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Original language: English

Original video: Your AI Agent Has No Nervous System — Matt Gibiec, Dynatrace · Channel: AI Engineer · 15:34 · Watch on YouTube

Topics: AI agents, Task and workflow automation, Software engineering with AI

Summary

Matt Gibiec argues that AI agents need context about the environments they operate in to reason effectively, avoid repetitive failures, and deliver measurable value. He presents Dynatrace Blue Box as a way to provide that context across the software development lifecycle, including production dependencies, historical behavior, and code changes. The tool is described as helping teams test changes, detect and investigate issues, plan features, and suggest fixes while keeping humans in control of deployment decisions. The stated goal is to help agents ship code that teams trust in production.

Key points

Agents can get stuck repeating actions when they lack the information needed to change their approach.
Teams should consider operational concerns, security, and how to measure the value their agents deliver.
Agents act quickly, but their decisions depend on the quality and completeness of the data and context available to them.
The speaker describes context as the connective tissue that helps agents understand an organization’s applications and processes.
Blue Box is presented as a way to provide context to AI coding agents and other agents throughout the development lifecycle.
Production service dependencies can inform local testing and help developers identify changes that might affect other services.
Historical behavior can help detect deviations, such as slower services or new errors, before they become larger issues.
Repository and change information can help connect operational problems to the code changes that may have caused them.
Context can support feature planning before developers prompt agents or begin building.
The proposed workflow includes observing, suggesting fixes, and leaving the final decision to deploy with a human.

Chapters

Start	Chapter	Summary
00:01	Why AI agents need context	Gibiec introduces the idea that agents replacing human work need sufficient context to reason, avoid loops, and deliver value.
04:11	Agents operate fast, but lack sight	He explains that missing data, details, or permissions can lead agents to make poorly grounded decisions, and frames context as essential.
06:57	Blue Box and production-aware testing	Gibiec presents Blue Box as a way to bring service dependencies and production context into AI-assisted development and local testing.
09:03	Detecting and fixing issues with context	He describes using historical behavior and code-change information to identify emerging issues and guide fixes without causing new problems.
12:12	Planning features and keeping humans in control	The talk covers briefing agents before development, using context to drive automation, and preserving human control over deployment.
14:19	Closing and preview sign-up	Gibiec summarizes the goal of helping agents ship trusted production code and invites attendees to sign up for a Blue Box preview.

Quotes

why am I stuck in a loop with my agent? — 02:07

The agents operate at speed. But without sight. — 04:11

context is king. — 06:24

plan before you prompt. — 12:43

we want to help agents ship code you trust to production. — 14:19

Concepts and entities

Dynatrace, Matt Gibiec, Bernd, Blue Box, Claude, observabilidade, Contexto de produção, human-in-the-loop, SDLC

Full transcript (original language)

00:01 [music] >> All right, it is 11:10 so officially time for our session. Thank you so much everybody for joining. I know this is the last day of the conference so I really appreciate for for all of you that that actually wanted to come on Thursday and and listen to our topic. My name is Matt Gibiits and I’m the

00:33 regional director of solutions engineering for AI native at Dynatrace. With a show of hands, anybody knows what Dynatrace is? All right, we got a we got a couple hands, okay. We are an observability platform, AI powered observability platform, but we’re not going to be talking much about Dynatrace today. Because what we want to address today is a little bit different topic and the topic that I want to address is, as you see the title of the session, that your AI agent has no nervous system. Probably an intriguing title and you’re

01:04 thinking, well, of course it doesn’t have a nervous system, it’s it’s not a person. Well, but if you really think how AI agents are being used today, we are using these agents to replace things that people used to do. And a lot of times, in order to do that, we need the reasoning. And when we really think about it, if we need reasoning, we need these agents to be able to actually not necessarily behave like humans, but have enough input to understand what they need to do to actually help us in our organization and not just automate things that aren’t

01:36 necessarily working very well today. So, with that said, let’s let’s break this down. With a show of hands again, how many developers do I have in the room in here? Okay, perfect. Any team leads? Okay. And okay, the last one might be a little bit tougher. Do I have a head of engineering in here? Would would you call yourself a head of engineering? All right, perfect. I got one. So, if you take a look at these questions, we’re not going to go through all of them. We only have 18 minutes left in here. But, if you take a look at these

02:07 questions, has any of you ever ask yourself a question or been asked a question, why am I stuck in a loop with my agent? How many of you have used, for example, Claude, and it just keeps repetitively doing the same thing over and over, consuming tokens, not giving you the result that you want? Anybody experience that? Everybody has, right? Now, if you think about it from a human perspective now, if you as a human do research, and you get results that that are not what you want, you will change your approach. You approach it from a different angle.

02:41 But, our AI agents today, they’re not smart enough to do that. All right, let’s let’s shift to to another question in here. I like the one under team lead in the middle. How can I handle operational issues? Are you thinking about that yet today, or right now are you still in the boom of let’s just get things deployed, let’s just get get things going, and we’ll figure out everything else later? Anybody in here having operational issues with your agents? Here and there, maybe. Okay, uh let me throw a twist in here.

03:11 How about security concerns? That That was a layup, right? I think everybody Everybody raised their hand in here. And so, again, a a very important questions that we have to ask ourselves. And the last one that I want to try with you guys, let’s look at the the head of engineering in here. I love the middle one again. Does our app or does the agent bring value to our users? I think we we jumped in so quickly to the AI world and at the AI era that a

03:41 lot of the times I see customers and and spending 3 days in here talking to folks, everybody has that AI buzz, this AI agent buzz, but how do you measure value? Do you have instructions already today to how to measure value of your AI agents? No, right? And and I almost think that as much as you want to run fast and get there, we have to take a little pause and understand what are we really trying to accomplish and how can we measure the success of our agents? Because if we’re spending our engineering resources, time, effort,

04:11 money on creating these agents and and and utilizing them in a way that we believe helps our organization, we need to come up with measures that’ll help us do it. Now, with all these questions, hopefully I I I I set up a picture a little bit in here to to to really jump into the next topic that I want to discuss, which is led in here by a quote from my CTO, Bernd. And I’m not going to read the whole thing, but just highlight what we see in blue in here. The agents operate at speed. Would everybody agree with that? That they’re pretty fast? But without sight.

04:46 And what what we mean by that is these agents today are only as good as the data that is available to them to make the reason to make the reasoning to make a decision. If the data that is provided to our agent is missing context, is missing details, is missing the right permissions to pull the data from some of the tools in your environment, you start entering the the very buzzy word out there like hallucinations. You start doing processes and automating things in

05:16 your environment that now probably don’t bring in value because there’s not enough context in your environment. So, what if I told you that essentially what we want to do in here is we actually don’t need your agent to necessarily have nervous system or sight to be efficient and be productive. There’s obviously a little twist on the title of the session in here. But what they need is actual context of your environment. You can pick any agent that that that’s

05:51 out there. You can just pick the the easy scenario of Claude and trying to automate things with um Claude inside of your um SDLC life cycle. And as you do that, if Claude doesn’t have access to the context of your environment, if it is not able to understand the dependencies of your services, if it is not able to understand how your applications are being used by your users, it will start coming up with decisions and advices that might seem right at that point,

06:24 but they won’t be right in the long term because there’s not enough context for that agent to understand how your environment really looks like. So, what I want to sum up this slide with is this one sentence in here, context is king. And what context is, it is that nervous system that we that you can provide to your agents. So, as they’re communicating, as you’re automating things, as you’re creating new processes, that connective tissue of a context is driving all of the processes that you’re automating with your agents.

06:57 So, let’s take a let’s take a look at a couple examples. So, keeping that in mind, what what what I want to present to you is Blue Box by Dynatrace. And what a Blue Box is is essentially an agent that will now be able to be integrated in the middle of any of your life development life cycle to provide context to any of your AI coding agents or other proprietary agents that you’re developing in your environment. I have a couple slides that we’re going to go through in here. I’m not going to go through every single scenario in here, but I’m going to point out the most important things that that I

07:27 believe are helpful in here. Let me Let me run this scenario by you. How many times you as a developer develop a feature, maybe even develop a feature or skill for an agent, and you test it locally, and it works great. Locally, everything works great, right? We’re happy, it’s awesome. And then what we do is we merge to to a main branch, we push it out there, and all of a sudden three other services are broken. Has that happened to any of you? Where your impact, even though locally it worked fine, once it was merged with

08:00 other agents, with other applications, now it broke a process in your environment. That is one of the most important things that Blue Box by Dynatrace can provide in here. We will be able to bring that essentially services and dependency mapping for every single one of your agents, every single one of your AI workloads. So, as you’re developing and as you’re making changes, you’re not just talking about testing things locally. We’ll actually be able to now use Blue Box to bring the production context into your local testing. So, now you know whether the feature that you develop not only works locally

08:33 for you and what you’re trying to automate, what you’re trying to achieve, but also that it won’t break and impact negatively anything else that that your agent that your workload is connecting to. Let’s take a look Let’s take a look at another um example in here. I This is honestly one of my favorites just in the entire world of observability. I’ve been doing observability now for 8 years, and I think if there’s one goal that I ask any of my customers, like, what would you like to achieve with your observability

09:03 strategy with now AI? And when it comes to issues, I think this is everybody will say that. Find issues before they’re real issues, before they impact my organization, before they impact my users. This is now exactly what the context that Blue Box brings into your agents will be able to do for you. And how do we really get that done? We are able to now again understand end-to-end the interactions of your agents within your application, within your ecosystem, within your organization. And what we are able to do is we are

09:34 able to use the historical data from your environment, the historical the historical loads to understand what regular what normal looks like. So, when things start to deviate from what normal looks like, we are able to now get ahead of it and inform your agents, let’s say in your IDE for your developers, that one of the services that belongs to them now it’s acting slower or all of a sudden it’s throwing errors that it didn’t throw. Now, what Blue Box will also do will be

10:06 also integrated in the middle of your code repository. So, as you do that, we will now also have the context of what was changed in your ecosystem. What was changed in your application? What was changed in your workload? So, now be able to overlay that data and understand that the issue that might be happening in your environment was actually caused by a change that somebody pushed out there. Maybe by a merge that wasn’t successful. Okay? So, as we dive into the next um scenario

10:38 in here, we discovered an issue. We We started with creating context, right? So, we have the context of your environment. We’re able to bring literally production context data to your local testing, to your developers in their IDE. You bake in Blue Box into your AI coding agents. Now, we’re able to detect the issues early. We’re able to start to be product proactive. But, what’s next? We have to talk about fixing things. And here’s yet another example where Blue Box will be able to be directly baked in into your SDLC.

11:11 So, when we detect that there is an issue, when we know and we have the knowledge of your repo, we know who pushed it, we know what changed, we are now able to again once again provide the context to your developers and let them know that the service that is now having an issue, that the agent that is now having an issue, that the workload that is now having an issue can be remediated in the following steps. So, again, why is the context so important in that process? Because when we need to fix a bug that’s causing an issue in our environment, it is it might be changing one line of code

11:41 to fix that particular issue. But after you change that one line of code, what happens to the rest of your environment? What happens to the rest of your application? So, again, that context and the dependency of all the other services in your environment is super, super important. So, as we do that, as we think about fixing things, it’s not only about fixing the the issue that we have at hand, but also making sure that we don’t cause any other issues in our environment. Um and the last um um or or two more suggestions that I want to run through

12:12 is actually briefing your agents before building starts. That’s actually, I think, one of one of my favorites when it comes to directly talking to the developers. I think the way too often we see developers going out there, and somebody came up with a feature, and now the developers just go and develop it. But have we done any research up front to understand what actually that feature needs to deliver to our users? What are we actually trying to accomplish with with

12:43 that specific feature? That’s exactly what we’ll be able to to do in here. We’ll be able to actually plan before you prompt. So, when you’re building your agents, when you’re building your applications, when you’re using your AI coding agents, again, Blue Bags will bring in all that context from your application today, from all the from all the agents that you already have in your environment today, to understand that as you’re planning for a new release, as you’re planning for a new feature, you’ll have enough data to understand what that feature that you’re now focusing on needs to encompass. And the last scenario that I that I want to share with you guys

13:16 is that at the end of the day, providing context to these agents can further drive automation. But when we talk about further driving automation, I’m sure some of you start thinking about the concept of human in the loop. Like at the end of the day, if we just let our agents do everything, I think it’ll be a pretty messy world, right? At some point, we want to be able to have control over what we are doing in our environments. So, obviously, with Bluebox, you’ll be able to do that as well. And I I I’d

13:48 like this picture that we have in here on the right, right? We’ll We’ll first start with observe. Observing your environment, building that context, building that dependency mapping, providing that locally to your developers as they’re testing, given all the production data, given all the production um details that they need to properly test their code. Once you have that, and then we detect issues, we’ll be able to actually suggest fix, but not just fix for the particular issue that’s breaking the agent, but using the context of your environment, using the context of the application that’s monitored, using the context of the agents that are being monitored,

14:19 making sure that the fix actually addresses what’s happening in your environment. Lastly, we’ll be able to drive the the entire SDLC in here by providing the fix, and giving you the control at the end of it, to make the decision whether you want to push the feature out there, whether you want to push the fix fix out there, or not. So, with that said, um that really brings me to the to the end of our um our a little pitch about uh Bluebox. And And what I like to finish with is we want to help agents ship code you trust to production.

14:49 So, if this is interesting to you, um you can go ahead and go to bluebox.ai, and you can actually sign up for a preview of it. Um uh as as soon as you do that, we’ll be reaching out to you to give you access to the platform. I also have my back pocket little um things that you can scan, so I’ll just walk around the room now and give them to you. But, with that said, we have 5 minutes. So, if you there’s any questions that that anybody wants to ask, uh we can absolutely use that time for those as well. No questions?

15:34 >> Mhm.