Harness Engineering: Building the Production Cage for Powerful Domain Agents — Mike Chambers, AWS

Hello everybody. Hello AI engineers. Are

we all having a good time still?

>> I'm having a good time. I mean, look at

me. I'm up here. I'm loving this. Um, so

yeah, thanks so much for joining me. Um,

I want to come and talk to you all about

harness engineering and all that kind of

stuff. Um, let me tell you who I am in

case you've not met me before. Um, my

name is Mike Chambers and I'm a senior

AI specialist developer advocate. Um,

and I work at Amazon at AWS. Um, little

bit about like uh how I managed to get

to stand here which is a very exciting

time for me. Um so um quite a while ago

in terms of generative AI anyway back in

2023 um I had the amazing awesome

privilege uh 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 um and the amazing

Dr. Andrew Ing on on a course about

generative AI with LLMs um 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, um,

I'm playing Transformers with Android.

That seemed like a really funny thing to

do at the time. Um, in 2025, I created

an MCP Lambda handler. It's downloaded

still to this day about 35,000 times a

month. Um, to help people in some of the

simplest ways of getting serverless MCP

serving happening. Um, I'm going to talk

about other things in relation to that

this time. So, we've moved on from that.

Um and in 2026, so the AWS is actually

one of the founding members of the

Aentka Foundation, part of the Linux

Foundation. Um I'm doing a little bit of

work behind the scenes on that. Hope to

do a lot more of that as well. So little

bit about me. Um so as I've been um

preparing for this, oh 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 that all

works for us. Um but as I've been sort

of traveling around a little bit as I do

and I was at the AI engineers uh session

uh summit uh uh conference in Melbourne

um and took a lot of it in 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. Um, so we talk about

agents all the time, but I see two

distinct types of agents. And as I say

this, it's going to become really

obvious, but they're the agents that we

use. And so, you know, this is claude

code and cursor and kirao 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. Um and

and so I do really think that these

don't 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 Chain has got

a article out. You've probably seen

stuff like that. Martinfowler.com

although Martin didn't write it. It was

Pitta 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 um harnesses? Well,

um no, okay, this is the wrong kind of

harness. Sorry, we do have strong

opinions on harnesses. I'm going to show

you all of that, but we sell all kinds

of things. So, in a nutshell, and if you

read those articles, um and if you've

had the conversations around here at

this event, of course, um a harness, 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. It has access to my files.

And I create a harness or the place I

work at has created a harness for me

which contains um how it's going to 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 to too

much more about that. Um, 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 u

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, enable

your um uh 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

want to see any more slop ops. Um, 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 want to ask the

agent to spin up an S3 bucket, get me an

EC2 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 going to build. And I'm

going to get into the code as quickly as

I can and we'll do as much as it has

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 want to have how

are we going to manage the memory? How

are we 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 um like at Amazon

and like at cloud scale I'm deploying my

agent out to the masses. So how do I

actually manage the loop? How do I

manage um 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

going to try and make some code work.

So, I'm here in Kira. This is my IDE of

choice here. And I've got a few

different samples that we're just going

to race through watching that clock

countdown fast. So, um just just make

sure that we're all on the same page

here and hopefully you can all see this.

Um of the code which I'm about to show

you, by the way, one piece of code, not

this one, has been generated by Curo.

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.

[laughter]

So, this is uh this is a Strand agent.

So, I've just taken the Strands agents

SDK. Um, and hopefully this kind of

thing is kind of familiar. I've brought

in an agent. I brought in the tool

decorator and I'm creating myself an

agent. The tool definition is down here.

Um, 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. Um and so

I can say something simple here like

what is the time? I'm not going to 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 going to

deploy. Let me move on to my next agent

quickly. So this is also a strand agent,

but this one I actually asked Kira to

write it for me for this session um

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 uh 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 uh included

for us. So, if I just scroll down here,

you can see uh yeah, here's the agent

definition itself. Um, and we've got a

bit more of a system prompt because Kira

couldn't help itself. Um, and we've got

some tools here defined. Um, including a

remember tool that the agent can decide

to use to remember stuff about me. Um,

and then I've got my uh session manager

down there. And that session manager is

going to 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. Um, 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. Um, and it says,

uh, yeah, keep testing me. Bring it on.

Excellent. Um, um, who will win the

World Cup? So, obviously I need to know

this. And, um, 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

Australia. So, obviously Australia is

going to 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 um running on my machine. So this

isn't really getting me to clouds scale

of course and I'm I'm picking up and 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 cloudscale and take things

like memory and deploy that separately

so it 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 um

agent core um and so we have bedrock

agent core. It's 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 want to show you how

to start out with that and how we do

this. So if I go to here uh yeah I'm

ready to go. So, I have a command line

tool on my machine, the agent core

command line. Um, and so there's a QR

code at the end, as you might imagine,

so that you can get hold of this. Um,

but I can use this to help me deploy my

agent. Now, this steps me through like

many of these types of tools do. Um, and

it sort of steps me through what do you

want to do? So, this is my woohoo agent.

Um, 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 going to 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

going to 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. Um, and so using this command

line is an easy way to get started with

agent core. So I'm going to keep the

default name. In fact, I'm probably

going to 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 going to 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. Um, and there's some deployment

options. There's also this I just want

to 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 going to say HTTP. We can use

any um framework we want. I happen to

use Strand's agents SDK, but anything

you could write your own framework if

you want to um or your own own base

code. Any model is supported by this as

well. So, we don't just have to use the

Amazon models. Um, 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 Sonic 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. Um, 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 strands

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-tenented

code. It's a massive saver and from a

security and identity perspective, it's

makes it so much simpler. It's um 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. Um, and we've got the uh

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 um which is

there. Um, 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 um uh 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

agentcore dev. And what that's going to

do for me um Wi-Fi permitting is it will

spin up for us a web uh 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 press

now. Um it knows that I'm coming to do a

presentation, but I think it does

anyway. Um and so yeah, you can interact

with the agent here. You can make

adjustments to the um to the code and

you'll see it update live here. Um but

you can also use this to switch over to

the live um deployed version. So with

agent core deploy it will use

infrastructure as co 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 stored

look at all that stuff so that you can

debug and see what's going on. Now, when

we stepped through the um the the the

console just a second ago through 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 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 um agentic use cases 80% of

agent development is 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

prompt do I want to use. Can't get much

more simpler than that system prompt.

Um, 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. 18

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. Um, 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, probably should

have 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 Strands agents,

which I happen to be using for this,

it's obviously it's it's free because

it's open source. Um, and it's a um a

model first framework for putting

together agents. It's super fast, it's

super powerful, and it's what I use all

the time. Thank you so much for being

with me in this presentation. Please

feel free to connect with me on

LinkedIn. I'd love to carry on the

conversation with you. have a fantastic

rest of show and have a safe travel as

you go home after the event. Thank you

so much.