# Harness Engineering: Building the Production Cage for Powerful Domain Agents — Mike Chambers — session 2026-07-02T19:05:00.000Z → 2026-07-02T19:25:00.000Z

_82 transcript lines · 0 slides · source: full recording_

## Transcript

We just copied the homework. Okay. So the big question. Why? Hello, everybody. Hello, AI engineers. Are we all having a good time still? Woo. I'm having a good time. I mean, look at me. I'm up here. I'm in loving this. So yeah. Thanks so much for joining me. I want to come and talk to you all about harness engineering and all that kind of stuff. Let me tell you who I am. In case you've not met me before, my name is Mike Chambers, and I'm a senior AI specialist developer ad Cuts, and I work at Amazon at AWS. A little bit about, like, how I managed to get to stand here, which is a very exciting time for me. So quite a while ago, in terms of generative AI anyway back in 2023, I had the amazing awesome privilege, to work with Antia, my colleague at the time, and now she works for Amazon AGI. You've probably seen her on this stage before, and the amazing Doctor Andrew Ng on on a course about generative AI with LLMs. Sort of can I say that we're approaching half a million enrollments with that? It looks like that's the case. And on a three week course, that's pretty cool. If you can't tell in that image, I'm playing transformers with Andrew. That seemed like a really funny thing to do at the time. In 2025, I created an MCP Lambda handler that's downloaded still to this day at about 35,000 times a month, to help people in some of the simplest ways of getting serverless m c P p serving happening. I'm gonna talk about other things in relation to that at this time. So we've moved on from that. And in 2026, so the AWS is actually one of the founding members of the Agentic AI Foundation, part of the Linux, Foundation, I'm doing a little bit of work behind the scenes on that. Hope to do a lot more of that as well. So a little bit about me. So as I've been preparing for this oh, and by the way, I did reread the abstract for this session and realized I said I'd be doing some live coding and so I will. So all combined fingers crossed, please, that all works for us. But as I've been sort of traveling around a little bit as I do and I was at the AI engineers, session, summit conference in Melbourne, and took a lot of it in and and also from the beginning of this week as well. I just wanted to to to summarize some of the things that I'm seeing and I'm thinking and I really want to get across and and what really matters to me. And that's this. There are Two different types of agents. So we talk about agents all the time, but I see two distinct types of agents. And as I say this, it's gonna become really obvious. But they're the agents that we use And so, you know, this is Claude Code and Cursor and Kiro and all of those types of things and also things that don't just generate code, things that we use for productivity and the like as well. And so those agents we use in a certain type of way. There's the agents that we use. And then on the other Side of it. We've got the agents that we build, and that's actually more to do with me. And and, actually, it's more to do with this presentation as well. It's agents that we build and how we think about agents that we build. And and so I do really think that these two things are quite separate and they chain together as well. I might build an agent that you use and this still holds true. So token maxing, all that kind of stuff, go for it if that's what you want to do with an agent that you use. But with an agent that you build, think about it carefully. Make sure that you're putting it together in a way that's going to work for the audience who's going to use that. So I promised that we were talking about harnesses and harness engineering, so let's define harness. I'm sure I'm not the only person to have put something like this up. I don't usually do this kind of thing and apologize if it makes your skin crawl. This is a dictionary definition of harness. A harness is a set of straps and fastenings used to control an animal. But if we took animal out of here and put model in there, then actually, it's pretty right. Right? That that's kind of what a Harness is. Other people have done a much better job than just the basic dictionary definition of what a harness is. So Lang Cheyne has got a article out. You've probably seen stuff like that. Martinfowler.com, although Martin didn't write it, it was Brigitte who wrote this harness engineering for coding agents, agents that we use. Right? So there are other ways of looking at harnesses. What about Amazon then? How do we see, harnesses? Well, no. Okay. This is the wrong kind of harness. Sorry. We do have strong opinions on harnesses. I'm gonna show. All of that. But we sell all kinds of things. So in a nutshell and if you read those articles, and if you've had the conversations around here at this event, of course, a harness. If you take an agent, remove the model part from it and everything that you have left, that's the harness. Okay. So let's think about that in context of an agent that we use. And so it's pretty, I think, fairly straightforward. I have this coding assistant. It's probably on my machine. Has access to my files, and I create a harness or the place I work at has created a harness for me, which contains, how it's gonna use memory, the skills that I want it to use, tools and MCP servers to allow it to go to be able to go and connect to documentation servers and the like. And and well set up engineering teams have got their standards that they've had. They've had coding standards for decades, but now they have basically harness standards, the things that they want to deploy to Everybody's coding assistance. So in a nutshell, that's what it is. I'm not going to talk too too much more about that. But I do want to share one QR code with you, and I'll try and give you a little bit of warning before I bring QR codes out. This is the agent toolkit for AWS. This is, available on GitHub. Of course, it's free. You can install it, and it helps you if this is what you're doing and you're deploying code. If you're deploying on AWS or you're thinking about deploying on AWS or or maybe you will one day grab this toolkit and Enable your, agent to to help you in the right direction. It's instructions for how to install it on pretty much everything. And the reason why I get passionate about this is because I don't wanna see any more slop ops. So we always used to push back against click ops in, you know, in the professional cloud development space. Clicking around on the console is great for being able to figure out what's going on, but it's not how you deploy things into production. We can ask an agent what's going on, but we don't ask the agent to spin An s three bucket, get me an e c two instance, whatever it might be. We want the agent to build up our infrastructure as code, which is going to go and do that so that we still own our deployments in the cloud. So no more swap ops. Okay. So that's the agent that we use. Now let's go and talk about the agent that we're gonna build. I'm gonna get into the code as quickly as I can, and we'll do as much as we have time for. So how do we think about a harness in relation to the agent that we're building? Exactly the same? To a point, yes. We still wanna have how we're going to manage the memory, how we're going to manage the skills. And tools in MCP, by the way, that that belies a lot of stuff. Right? Because you can pretty much extend an agent to do almost anything you want with a whole bunch of different types of tools, which could be via MCP. But with an agent that I am building, I need to think about a lot more than just that, especially if, like at Amazon and like a cloud scale, I'm deploying my Agent out to the masses. So how do I actually manage the loop? How do I manage, scaling payments, memory, identity skills, runtime, context management, the rest of it? And I have left it to the last thing, but it should be the first thing that I say, observability and evaluations. Super, super important. How do we actually deal with this? Do I write all of this code down into one container and just deploy it and scale that? Not really if I want to be scaling to thousands of users. I need to think about each. Individual of these components and how I'm going to scale them out individually. And that, to me, is harness engineering. This is the serious side of stuff. This is the big stuff that we want to get harnesses working at real scale. Okay. Let's see if this works. I can feel your combined goodwill being sent my way that we're gonna try and make some code work. So I'm here in Kiro. This is my IDE of choice here, and I've got a few different samples that we're just gonna race through watching that clock count down fast. So, just just make sure that we're all on the same page here and hopefully you can all see this. Of the code, which I'm about to show you, by the way, one piece of code, not this one, has been generated by Kiro. Everything else is either a tool or this one. I actually wrote it myself. I didn't use an agent for this. I know. I deserve a round of applause, but it's okay. So this is, this is a Strand's agent. So I've just taken the Strand's agent's SDK. And, hopefully, this kind of thing is kind of familiar. I brought in an agent. I brought in the tool decorator, and I'm creating myself an agent. The tool definition is down here, and so I just pass in my system prompt. Things are pretty simple at this stage, and I've passed in a couple of tools. Calculator is something that's a library I can install, and get time is the one that we always use because I don't tend to use agents to book flights, certainly not ones like this. And so I can say something simple here like, what is the time? I'm not gonna run this because you know The time, but you can see generally how this works. Is this a harness? Sort of. There's not an awful lot to it. Right? We've got the tools in there. Our loop is being managed for us by the framework. This is pretty cool, so that's good. But obviously, if I was to run this, this is running on my laptop. It's not running at any particular scale, and we're missing some of the attributes that I want from the agents that I'm gonna deploy. Let me move on to my next agent quickly. So this is also a Strand's agent, but this one I We asked Kiro to write it for me for this session, because I wanted to include some more stuff. And so inside of this agent, the one main thing that I want to point out is that I am, I've included a session manager. So my session manager is helping me to maintain session state between invocations. So this is a sort of memory. It's a kind of medium term, short term memory kind of thing. It's not proper long term memory, but it is there. And actually, it does store long term memories in files. Which are down the side here that it's, included for us. So if I just scroll down here, you can see, yeah, here's the agent definition itself. And we've got a bit more of a system prompt because Kiro couldn't help itself. And we've got some tools here defined, including a remember tool that the agent can decide to use to remember stuff about me. And then I've got my, session manager down there, and that session manager is gonna rehydrate the conversation history when I come back to chat to it the next Time. And maybe the next time is now. So let's see if we can get this working. Now, again, this is running on my local machine, and this is a demo here. So let's just type in hello because I'm scared of typing too much and spelling it wrong. And it says, yeah. Keep testing me. Bring it on. Excellent. Who will win the World Cup? So, obviously, I need to know this. And, what does it say? Yeah. So while you could just Say Australia. It knows I want Australia to win. It's where I'm currently living. I'm I'm Australia. So, obviously, Australia is gonna win the World Cup. But why has it got that? It's because of previous conversations that we've had, and, obviously, it's being honest that it has no clue because that's coming from the large language model, of course. So okay. Looks at a couple of different agents there, blasted through this. This is, it's running on my machine, so this isn't really getting me to cloud scale, of course. And I'm I'm picking up I'm including various pieces in this like memory. So let's go next. How do we get to the point where I can deploy something like this, if not this actual agent, out at cloud scale and take things like memory and deploy that separately so we can scale separately, taking our loop out so it can scale separately, and we can then bolt in all kinds of other things as well. So in order to do that, I'm going to use something called an agent core. And so we have Bedrock Agent core is part of the stack that we have at AWS, and that's how I'm doing this and how I'm deploying. So I've done that already, but I wanna show you how to start out with that and how we do this. So if I go to here, Yeah. I'm ready to go. So I have a command line tool on my machine, the agent core command line. And so there's a QR code at the end as you might imagine so that you can get hold of this. But I can use this to help me deploy my agent. Now this steps me through like many of these Types of tools do, and it sort of steps me through what do you wanna do. So this is my woohoo agent. And it's going to ask me a bunch of stuff. And I wanted to show you some of this as we step through. Now, strangely, I'm not gonna select harness, and we'll come back to why I'm not selecting harness in a second. But I'm saying I wanted to deploy an agent. And what's gonna happen here is this command line tool is actually going to step me through and actually write an entire agent. It's basically a hello world agent that I can then go and customize myself. So using this command line is an easy way to get started with agent core. So I'm gonna keep the default name. In fact, I'm probably gonna keep all the defaults here just so we can see what's the option. Of course, I can bring code if I want, but I'm gonna ask it to create some code for me. So it says, well, what do you want? Python or TypeScript? And back in the day, I used to do things like activate functions and back propagation in the machine learning space. So Python, it is for me. So I will choose that and and there's some deployment options. There's also this. I just wanna Point this out. Like, how can we actually go and connect into our agent? So our agent that's running at scale in the cloud, HTTP is probably the obvious one, but we might want to have it being served behind MCP. We might want to use awesome little things like AGUI so we can make nice interactive chat agents. But I'm gonna say HTTP. We can use any, framework we want. I happening to use Strand's agents SDK, but anything you could write your own framework if you want to, or your own own base base. Code. Any model is supported by this as well, so we don't just have to use the Amazon models, and we don't have to use the ones from Amazon Bedrock, but we can use any model. I'm using the one here. I'm using Sonnet 4.5 just because that's offered to me at default. And here's memory. So this is the one thing I wanted to show you. So I can come in here and ask for long term and short term memory to be deployed. And we'll see what this means in just a second, but it's basically going to create for us cloud infrastructure, which is going to manage those memories for us separately. From our running agent running asynchronously from our agent and connected, of course. So there's obviously other kinds of things we can do. We can hit enter, and it will start to create the configuration of this agent on my machine. Now I'm going to skip over here and come back to the actual code I have because I've already done this, of course. And this is the agent that it would be currently deploying, something like this. So we've built up here. This is a little bit more complex. So this is a Strand's agent. You'll notice that it's got a few more things. Added in. So it's got the linkage into Amazon Bedrock Agent core app. But pretty much apart from that, that's all you need in order to be able to scale this agent out at runtime and do multi tenant isolation. So you can write an agent that works for one user and then scale that out without you having to write all the multi tenant code. It's a massive saver. And from a security and identity perspective, it makes it so much simpler. It's, it's very, very useful. So if I scroll down through here, you can see the rest of it is looking pretty similar. We've got some test tools in here. We've got a connection to MCP so we can see how that is done, and we've got the, connection into our session manager and our memory, which is all built in here. So if I scroll down a bit more, we'll be able to see somewhere where we actually invoke the thing, which is there, and the system prompt is is somewhere at the top. So we can we can scroll through this code. I'm going through it quickly. You can write your own. Code and do this with it as well. If I go back over to my, code here for just one second, I'm in the folder now that has been created with that code locally. It is actually deployed, but let's assume it's not deployed quite yet. I can come back in and type in agent core dev. And what that's gonna do for me, Wi Fi permitting, is it will spin up for us a web browser. And inside of that web browser, we're now connected to that agent running locally. So if I make updates to that code We would see that happen in real time here. So I can say, hello. I am doing the pres now. It knows that I'm coming to do a presentation. I think it does anyway. And so, yeah, you can interact with the agent here. You can make adjustments to the, to the code, and you'll see it update live here. But you can also use this to switch over to the live, deployed version. So with agent core deploy, it will use infrastructure as code code like I talked about before to deploy your agent out at scale with the memory, with the agent, with runtime, and with many other components if you choose to do so. You can use this interface then to go and look at traces, look at memory storage, look at all that stuff so that you can debug and see what's going on. Now when we stepped through the, the the the console just a second ago, sorry, the the CLI app a second ago, we didn't choose harness. I skipped out on that one and I'm just going to show you that quickly now. So one One thing we can do instead is I think we can get to the point you've seen I've deployed agents. I didn't do very much. I just did a system prompt and some tools and go. And there's an argument to be made that essentially if that's possible, then maybe 80% of, agent use cases 80% of agent developments kind of solved already. We don't need to do much more than system prompt connect to some MCP tools and we've got what we want. And if that's the case, then we have harness built into agent core. This is the configuration for an agent. I just have a simple JSON which is showing me which model do I want to use and what system prompts do I want to use. Can't get much more simpler than that system prompt. And then this can also be deployed with agent core deploy. So at this point, we don't have even any agentic code either. We can just deploy it straight out. If you want to know any more about any of this, then please do come and see us down on the booth or see me after this session. I'll be more than happy to talk to you at length about this. Eighteen minutes is such a short amount of time. For me to be able to talk about almost anything. But this is essentially all of these components on here. There's a mapping somewhere into something that agent core has. I apologize for the colors. It seemed like a good idea at the time. But this is an overview of the different capabilities that are composable out of Agent Core. So you can take any of these and use any of them together or separately. If you have an agent that's running in production very happily at the moment but You like the idea of having long term memory managed for you serverless, then you can just take that part and integrate it. That's totally something you can do. Here's a QR code. Sorry to pretty should put that there a second ago. I'm moving this QR code in just a moment. But Amazon Bedrock agent core is that. If you're interested in the Strand's agents, which I happen to be using for this, it's obviously it's it's free because it's open source, and it's a, a model first framework for putting together agents. It's super fast. It's super powerful.

## Slides