No Memory, No Harness: Why the Database Is the Last Line of Defense — Kay Malcolm, Oracle

Everyone, are we having fun?

>> Oh, you've got to give me way more than

that. So, let me tell you, um, my name

is Kay Malcolm. I am a retired hiphop

instructor. So, if I don't get more

energy than that, we will start. We'll

start. Are you having fun? [cheering]

>> Okay. All right. So, here's what we're

going to talk about today. Now, you guys

have heard a lot about two letters. Does

anyone want to guess what those two

letters are that I'm going to talk about

today?

>> Data. That was pretty good. DB. I'm

going to talk about AI, but I'm

specifically going to talk about agent

harnesses. But before I do that, I want

to introduce you all to a few people. Is

that okay?

Yes or yes. Is that okay?

>> I gave choices. Yes. Anyway. All right.

Okay. All right. This is my team.

I run an outbound database product

management team at Oracle. I've been at

Oracle a really long time, 20 years.

Funny story, I started when I was 12.

So, don't do the math and don't start

start adding in your head. Um, and we've

got a problem. That problem is I've got

one group

that does platform development and then

I have another group that does content

development for live labs, a platform

that I wrote myself. So yeah, I'm an

engineer but I'm kind of a developer

poser too. And then I've got another

group who does QA

and then I have another group who does

my front-end development

with AI. Here's what I found out as a

leader. Because in the token maxing era

of 2025, because you know, we're not

token maxing anymore, right?

We are responsible AI now. But in the

token maxing era, the thing that I found

out was while AI was making the

individuals on my team faster, there was

another problem it was creating.

It wasn't making my team more

productive.

And the reason was when one team from

the Netherlands checked in code at my

4:00 a.m. in the morning because I've

got half of my team that's in AMIA and I

have half of my team who that are here

in the United States. They checked in

the code but they didn't check in their

context from Codeex. We use Codeex at

Oracle.

So then when the US team woke up,

they got the code but no information

about the context. So we used AI to

solve a problem that AI created. And

here's what we did. Oh well, let me talk

about this first. So some of the issues,

the context, like I said, wasn't shared.

GitHub wasn't tracking that. I had

repositories that were diverging and I

was asking the managers who work for me,

what's happening to your teams? Why why

are we not going faster? We're spending

all of this money on tokens. We're

spending all this money on AI, yet

something is missing because we're still

spending time doing testing and

validation. So our netn net wasn't

really wasn't really working for us

because git records the code and not

human intent.

So it's a problem

and

even though code creation

was no longer our problem

still had a bottleneck.

We needed a collaboration layer. Now, I

do have members of my team in the

audience, so don't judge me, and you

know who you are. I'm not saying that

you all didn't collaborate. But now,

we've got a new team member, and that

new team member is AI.

So,

we needed to figure out how to track our

progress in our next steps. how to

rationalize decisions

that the

agent was making.

We needed to figure out how to resolve

questions in conflicts.

Okay,

hold my problem. Will you all hold my

problem for me right here? We're going

to just tuck that in a little box. Let

me define what a enterprise agent

actually is. Now, most people think that

an enterprise agent is the model and

workflow. How many people agree with me?

Man, this tough crowd you. Okay, one

person. Okay, the rest of you think it's

a little bit more. Okay, let's see what

could it be that a real enterprise agent

has tools.

Tools are how it does things.

Context.

The context. That's a context window.

That's what's in the actual prompt.

Memory.

Huh?

And if you're thinking, "But wait, K,

memory, you just said that the model is

kind of like the brain of the

operation." Hold tight. We're going to

talk a little bit more about memory

retrieval because you don't want to get

everything back. So that's being able to

to retrieve the right information back.

And then I know that there are a lot of

developers here and you all don't care

about security.

I care about security because I work for

the most secure database company and I

used to work for um agency that has no

name. But guard rails is also important.

This is the harness. I speak in

analogies and I and I speak in stories

because if I tell you this and Marvel,

you know exactly what I'm talking about.

So the agent, think of it as the model,

little brain floating a in a glass jar

plus this harness. This harness is the

body.

So it's how the agent can actually do

things and get things done. That memory,

that's the part of the central nervous

system. And you remember the central

nervous system connects the brain to the

rest of the body, legs, arms. That's the

part of the central nervous system that

carries context. So you remember my

problem with Git.

What I needed was memory. Okay, so there

are a number of memory types. I chose

five, the five most common ones that

people talk about and these are the ones

that I want you to remember. The first

one is short-term memory. That's the

session, right? And so if you're storing

memory of an AI uh process, that is the

short-term memory is if you're with

chat, cloud code, right? Codeex, pick

your poison. The long-term memory is

what persists across sessions.

Episodic memory. Hm.

What happened the last time

I interacted with fill in the blank?

That's your episodic memory. Procedural

memory

tools, steps that were taken.

And then finally, semantic memory. And

semantic memory because we're talking

enterprise agents. We're not talking the

agent that I built, Sasha Fierce,

because remember I told you guys that I

was a I'm a a dancer. So, of course, my

my chief of staff is going to be called

Sasha Fierce because that was Beyonce.

Any Beyonce fans?

Okay, I'm sorry. All right, we got to

focus. Okay, so these are the memory

types. Now, when you're defining this

real enterprise agent in this memory,

there's something you need to consider

where to store it. And so, I'm going to

tell you guys a story. But when I tell

you the story, you have to promise me

that you're not going to judge me. Do

you promise?

Do you promise

>> you're not recording me, right? Because

this doesn't paint me in a good light.

Okay. All right. The world of data was

one simple. I've been at Oracle a long

time, but I came from a customer. That

customer's name was Southern Company. It

was a power company. I'm based out of

Atlanta. And I was hired at Southern

Company because I was a rockar

performance tuner. You had a SQL query.

I mean, I'm dating myself, but whatever.

You had a SQL query. I knew all of the

innit.org parameters. Even the ones when

you called support and they said, "Don't

remember these. Don't write them down."

I wrote them down in my little notebook.

I could tune a query within one inch of

its life. Then one of you came to my

desk because I mean the world the world

was rows and columns. It was a great

time back in my Albundy days um and said

hey I need to store data unstructured.

Why I need to do that?

And so me being K the the diligent DBA,

I was like, "Let me figure it out and

get back to you."

Did I get back to him?

I didn't get back to him. Now, the thing

you have to know about Southern Company

was for every database system that a DBA

managed, I had to attend two meetings.

Today, when I hear Sarbain Oxley, I

still throw up a little bit in the back

of my throat. So I had to attend a

security meeting and a patching meeting

every week. Never failed. Now because

this developer installed a database that

was specialized for unstructured. Okay,

there are really smart people in the

room. How many meetings am I going to

now?

Four. Okay, I'm a little annoyed, but

I'm like, okay, we can we can we can do

this. Then they said, "Okay, since

you're such a good tuner,

I need you to figure out this

relationship." Now, the way that

Southern Company worked, there was this

people could die application, um, and it

was a it was like a Nokia phone that

people who were climbing the towers,

right? So, you guys have have been in a

storm and the power goes out, right? And

then you're pretty sure that within

maybe an hour or two the power will go

on. Well, that system that would tell

the people who were climbing those trees

and risking their lives to turn the

power back on sometimes would have false

positives or false negatives. So, they

wanted to look at all of the other um uh

polls in the area

to try to get away from the false

positive or the false negative. And so I

did that in a SQL query and it was it

was amazing. It was a five nested union

all statement. It was some of my best

work. Now it might have taken like 20

minutes to work but it was like a

predecessor to graph.

Yeah, they they install Neo4j.

So now how many meetings am I going to?

Six. That's a problem.

So I um Oh, let me I got ahead of

myself. So you know what I did? I quit.

I left and I came to Oracle because I

was like this is a problem and maybe I

can go to Oracle to help solve it. So

then Joe Mundy called me and he said hey

um we are installing Reddus. Oracle is

late to the game. We've got a vector

database.

Okay.

But here's your problem, Joe.

Agents now need access to all of this

data. So if data is in an Oracle

database, if then it's also in an

unstructured JSON database, if it's in a

graph database and it's in a vector

database, where is your single source of

the truth?

The agent has to figure that out.

Sometimes it'll get it right.

Most times it'll get it wrong and it's

going to burn up a whole bunch of

tokens. And so now if you want to store

your memory somewhere, you can store it

in a file system.

You can store it in

clawed or chatgpt because we all know

about the memory.md file.

But that's going to be a problem. Now I

want to illustrate this. I need four

volunteers. I can see you raise your

hand. One, two. Okay, I can't. Maybe I

can. Three.

I need a fourth. Ah, fourth in the back.

Okay. Fourth in the back. You are going

to be our old reliable. You're going to

be a relational database. Yes or yes.

You got your So, you have your

assignment. Okay. And then there was

someone here. You're going to be my

unstructured database. Okay. And then

where was my other? Ah, very good.

You're going to be my graph database.

You good? Relationship guy. You look

like a relationship guy. All right. Very

good. Fourth. Where was my fourth?

Was it you? Yes. Yeah. You are my vector

database. Okay. Now, everybody be

really, really quiet.

For my four volunteers, I need you all.

I'm going to say something to you. And I

need you all to decide how you're going

to store it and who's going to have the

single source of the truth. You can't

get up from your seats and you have to

whisper because if you talk loud that's

5x the tokens for you. Yes.

Okay. Are we ready? All right.

The cow jumped over the moon.

Go.

Doesn't really work, does it? That's a

problem. Okay,

Oracle. And if you don't forget one, if

you forget everything I say and you

remember one thing,

Oracle is not the Oracle that you think

that is why I am here today. How many of

you knew that Oracle could natively in

the same table down to the same

partition store JSON graph vector my

vector friend over there my JSON friend

spatial

you want your memory to be immutable

blockchain in the same database raise

your hand yeah

we have a marketing problem

so any data type can be stored in a 26AI

database any workload anywhere AWS GCP

Azure OCI on prem choice and flexibility

so now when we take this and we talk

about the agent I want to be able to

store my long-term and procedural memory

in relational on in JSON I want to store

my short-term and my long-term memory

graph I want to store procedural because

procedural that's how I figure out the

relationships right the steps

my episodic and semantic memory. I need

to do some vector and then store it also

as text. Now, if I have four different

databases, you all saw they can't talk

to each other. It's going to be a

problem. And so, what I'm saying to you

today is the Oracle AI database is the

best place to store this agent memory

that's going to power your harness.

Remember your harness is your body

[music] and that memory is your central

nervous system. Okay, back to PY. So the

problem that I had, we solved it with a

memory broker named Py. We used agent

memory. We got out of that automatic

continuity.

So with my team, they were able to share

not just their code, but PY also kept

track of the context. So if one context

window was had procedural memory,

episodic memory, information about the

long-term memory, that was then shared

with the other folks on the team. You

could call them agents, if you will.

They're just human agents shared across

forks.

The developers on the team remained in

control while Polly was able to create

the context, figure out which fork and

branch it belonged to and which commit

it belonged to. Now this is a very

simplistic example but when you take

this to the enterprise here's what

happens.

Memory is the thing that becomes

non-negotiable in an agent's harness.

Now these are three papers that that I

read um on the airplane. This first one

is from open AI and it's about its

in-house data agent and the thing that

it says is it is saying that its

in-house data agent actually needs

memory. Memory was crucially important

to ensure that its agent was able to

filter correctly instead of trying to

string match.

Harrison Chase said, "Your harness, your

memory. And if you don't own your

harness, you don't own your memory."

Which is key. And then I'm sure you all

are wondering, "Well, Claude has memory.

Why can't I use that?" Well, it's kind

of like file system memory and it works

with one, but just like in my example,

when you scale past one, and you're

going to scale past one in the

enterprise, it creates a problem. So

Oracle has a Oracle agent memory pap

package. PIP install Oracle agent

memory. You get access to it. And this

memory is this SDK that we have is the

thing that will hold your live

conversations, your memories, your facts

and figure out what is worth keeping. So

if we look at Py now, Kevin

can share his context with Py, our

memory broker. We use the Oracle agent

memory SDK. It's stored in an Oracle

autonomous database.

We can use the LLM of our choice or we

can use a local model through the Oracle

private AI services container.

And then Linda, who's actually sitting

right here, can interact and work with

Kevin, no issues.

So yes, AI makes individuals faster.

Shared memory on an Oracle AI database

makes teams faster. So I don't want you

all to compromise. In the age of AI,

what 26AI does is you can choose

and pick what's best for agent memory

file system stored in a database file

system or in the database. If you need

to do data modeling, you've got JSON,

you've got relational. We've got choice.

Okay, I've got some goodies for you. The

Oracle AI developer hub. That's where

you can guys you guys can get coding

materials, the applications, what I

talked about today. Livelabs.oracle.com.

If you've done any of our workshops

today, that happens to be something that

I wrote myself about six years ago and

40 million users um ago. Spend my OCI

tenency money.

kick the tires on any Oracle technology

um for six hours, 12 hours, however long

you need. Um and then I'm giving you all

all a Mac Mini. No, I'm just kidding.

I'm giving you an OCI Mini. So, I don't

know if you knew, but there is an always

free OCI. It is the most generous of any

of the hyperscalers where you can get a

free Oracle database, free compute, you

can send 3,000 emails a month, 200 gig

in storage, and if you click on that,

you can get access to it. Or just search

Google for Oracle Cloud, always free.

Connect with me. If you build something,

will you all message me and let me know?

Yes or yes?

>> Thank you.

>> [music]