Whitepaper Companion Podcast - Introduction to Agents

Welcome to the deep dive. Today we're

really getting into something exciting.

The architecture for AI agents.

>> That's right. We're focusing on the day

one white paper from that 5day of AI

agents intensive course by Google X

Kaggle.

>> Exactly. This feels like the guide for

anyone building with generative AI

moving you know beyond just simple

demos.

>> Absolutely. It's about building robust

productionready systems.

>> So let's talk about that shift. What's

the big picture change here? Well, it's

fundamental. Think about AI.

Historically, it was mostly passive,

right? Answering questions, translating,

>> responding to a prompt,

>> precisely. Now, we're talking about

autonomous, goaloriented AI agents.

These things don't just talk. They plan,

they act, they solve complex problems

over multiple steps

>> without someone holding their hand the

whole time.

>> Exactly. That's the autonomy part. They

execute actions in the world to hit a

goal.

>> Okay. So, let's unpack that. The white

paper breaks down the agent anatomy into

three core parts. What are they?

>> Right. You've got the model, which is

like the brain. Then the tools. Think of

those as the hands.

>> And the third piece,

>> the orchestration layer. That's the

conductor pulling it all together.

>> Let's start with the model, the brain.

It's the LLM, right? But what's its

specific job in an agent?

>> So yes, it's your core language model,

your reasoning engine. But its key

function here is really about managing

the context window.

>> Managing context. How? So

>> it's constantly deciding what's

important right now. Information comes

from the mission itself, from memory,

from what the tools just did. The model

curates all that. It decides what input

matters for the next thought process.

>> Okay? So the model thinks, but it needs

the tools to actually do anything.

>> That's it. The tools are the connection

to the outside world or even internal

systems. They could be APIs, specific

code functions, ways to access

databases, vector stores. So the agent

can like look up customer data or check

inventory.

>> Exactly. And crucially, the model

reasons about which tool is needed for

the current step in its plan. Then the

orchestration layer actually calls that

tool

>> and the result from the tool.

>> That result, the observation gets fed

straight back into the model's context,

ready for the next cycle of thought.

>> Which brings us to that orchestration

layer. You called it the conductor. It

sounds like more than just running code.

>> Oh, much more. It's the governor of the

whole process. It manages that

operational loop we mentioned. the

planning, keeping track of the memory or

state and executing the reasoning

strategy.

>> Reasoning strategy, like chain of

thought.

>> Yeah. Or react, which is really common

for agents. React blends reasoning and

acting. The agent thinks, okay, based on

my goal and current info, I should do X.

It acts using a tool. It observes the

result. Then it reasons again based on

that new info.

>> So, it's not just blindly following a

script. It's constantly thinking,

acting, observing, thinking again.

That's the loop. That's what makes it

agentic. It's transforming the LLM from

just a text generator into something

that can actually accomplish complex

tasks.

>> Can you walk us through that loop? Say

for a simple task, what are the key

stages?

>> Sure. Let's use the white paper example.

Organize my team's travel. Step one is

obvious. Get the mission. The agent gets

the BLE goal. Step two, scan the scene.

It looks around virtually speaking. What

tools does it have? Calendar access.

Booking APIs. what's relevant in its

memory.

>> Then step three,

>> think it through. This is the planning

stage.

>> The model says, okay, for travel, first

I need the team list. I should use the

get team roster tool.

>> Makes sense.

>> Step four, take action. The

orchestration layer actually calls that

get team roster tool.

>> And finally,

>> step five, observe and iterate. The tool

runs, maybe returns a list of names.

that list, the observation gets added to

the agents context, its working memory,

and bam, it loops right back to step

three, thinking, "Okay, I have the

roster. What's next? Check

availability."

>> And that cycle just repeats

>> until the overall mission, organizing

the travel, is complete. It's the same

process for handling something like a

customer support query. Where's my

order? The agent needs to plan, find

order, get tracking, query, carrier,

report back. Each step involves that

think, act, observe cycle.

>> Got it. That loop is fundamental. Now

the white paper also talks about a

taxonomy different levels of agent

capability. Why is that important?

>> Ah yeah this is crucial for actually

designing and scoping your agent. You

need to decide how complex it needs to

be. Level zero is the baseline.

>> What's level zero?

>> That's just the language model on its

own. No tools. It only knows what it was

trained on. It can tell you about

history. Maybe explain a concept.

>> But it can't tell me the score of last

night's game.

>> Exactly. It's cut off from the present.

So level one is the first real step up,

the connected problem solver.

>> This is where the tools come in,

>> right? You connect the reasoning engine

to tools. Now it has those hands. It can

use a search API, check a database. It

can answer the score question because it

could look it up. It has real-time

awareness.

>> Okay, so level one connects to the

world. What's level two?

>> Level two is the strategic problem

solver. Now we're moving beyond single

simple tasks to more complex multi-art

goals. The key skill here is something

called context engineering. Context

engineering.

>> Yeah, it means the agent gets smart

about crafting the input for each step.

Take the example, find a good coffee

shop halfway between two addresses.

>> Okay, that's definitely multi-step,

>> right? A level two agent would first

use, say, a maps tool to calculate the

actual halfway point coordinates. Then

it takes that specific result, the

coordinates or maybe the neighborhood

name, and uses it to craft a very

focused query for another tool like a

place search API, maybe asking for

coffee shops near those coordinates with

a rating above 4.0.

>> Ah, so it's using the output of one step

to intelligently shape the input for the

next step.

>> Exactly. It's actively managing its own

context to get better, more relevant

results and avoid noise.

>> That's clever. What about level three?

Level three is the collaborative multi-

aent system. This is a big jump. Now

you're talking about a team of

specialists. Agents start treating other

agents as tools

>> like a little company of AIS

>> sort of. Imagine a project manager

agent. It gets a complex goal like

analyze competitor pricing. It doesn't

do all the work itself. It delegates

tasks to specialized agents, maybe a

market research agent, maybe a data

analysis agent.

>> So it calls another agent's API

essentially. How is that different from

just calling a complex function?

>> Good question. The difference is often

the autonomy of the agent being called.

The project manager delegates the goal,

analyze pricing. The market research

agent receives that goal and might

execute its own multi-step plan using

its own specialized tools and knowledge

before returning a synthesized result.

It's not just a simple request response.

It's agent-to- agent goal delegation.

>> Okay, that's starting to sound seriously

complex, which leads us to level four.

>> Level four, the self-evolving system.

This is uh pretty much the frontier

right now. Here the system can actually

identify gaps in its own capabilities.

>> It knows what it doesn't know or what it

can't do.

>> Exactly. And it can take steps to fix

it. So if that project manager agent

realizes it needs say real time social

media sentiment analysis for the

competitor research and no existing

agent or tool can do it.

>> What then

>> it might invoke an agent creator tool to

actually build a new sentiment analysis

agent on the fly. Maybe configure its

access permissions. everything. It's

adapting and expanding its own toolkit.

>> Wow. Okay, that's a powerful vision.

Let's shift gears a bit. If we want to

build these, especially beyond level one

or two, how do we make them work

reliably in production? That

non-determinism seems tricky.

>> It is tricky and it starts with model

selection. You need to move beyond just

looking at generic benchmark scores.

>> So, the biggest model isn't always the

best.

>> Not for an agent. You need the model

that shows the best reasoning and

crucially the most reliable tool use for

your specific tasks. This often leads to

a strategy called model routing.

>> Routing like sending different tasks to

different models.

>> Precisely. You might use a really

powerful maybe more expensive model like

Gemini 1.5 Pro for the complex planning

steps or high stakes decisions. But for

simpler highvolume tasks within the

agents workflow like summarizing text or

extracting a simple piece of data you

might route that to a faster cheaper

model like Gemini 1.5 flash. It's about

optimizing performance and cost.

>> Smart resource allocation. What about

the tools themselves? You mentioned

retrieval and action

>> right for retrieving information.

Grounding the agent in facts is key. So

you use arg retrieval augmented

generation often with vector databases

for searching unstructured documents or

NL2SQL tools so the agent can query your

structured databases using natural

language

>> and for taking action

>> that's typically done using APIs wrapped

as tools scheduling a meeting via a

calendar API updating a CRM record maybe

even executing code some agents can

write and run Python scripts in a secure

sandbox environment to handle really

dynamic tasks

But for the model to use these tools

reliably, it needs to know how to call

them right?

>> Absolutely critical. This is where

function calling comes in. The tools

need clear descriptions like an open API

spec. This tells the model exactly what

the tool does, what parameters it needs

like order it or custom email, and what

format the output will be in.

>> So the model can generate the correct

API call.

>> Yes. And just as importantly, it can

accurately understand the response from

the tool. Without that structured

communication, the whole loop can break

down.

>> Okay. Let's swing back to the

orchestration layer. We know it runs the

loop, but what else does it handle?

>> It's also responsible for defining the

agents persona and its operating rules.

This is often done via a system prompt

or a constitution,

>> like telling it you are a helpful

support agent for Acme Corp. Never give

financial advice.

>> Exactly. Setting the boundaries, the

personality. And the other big job is

managing memory.

>> Memory.

>> Yeah. You typically distinguish between

short-term memory and long-term memory.

Short-term is like the agent's scratch

pad for the current task. The running

history of action observation pairs in

the current loop

>> and long-term

>> long-term memory persists across

sessions. It's how the agent remembers

preferences, past interactions or

knowledge it gained previously.

>> Architecturally, this is often

implemented as just another tool,

usually a rag system talking to a vector

database where memories are stored and

retrieved.

>> Okay, so we have the model tools

orchestration memory. But these things

are still unpredictable sometimes. How

do you handle testing and debugging what

the white paper calls agent ops?

>> Yeah, this is a huge shift from

traditional software testing. You can't

just assert output expected output

because the output might be perfectly

valid even if it's phrased slightly

differently each time.

>> So what do you do instead?

>> You evaluate quality. A common technique

is using an LM as judge. You use another

powerful language model. give it a

detailed rubric and have it assess the

agents output.

>> You use an AI to check the AI

>> essentially. Yes. Does the response meet

the requirements?

>> Is it factually grounded? Did it follow

the negative constraints? You run these

evaluations automatically against a

golden data set of test scenarios.

>> And when it fails, how do you figure out

why?

>> Debugging is tough. That's where

observability tools, specifically open

telemetry traces, are incredibly

valuable. A trace gives you a detailed

step-by-step log of the agents entire

thought process, its trajectory,

>> like a flight recorder.

>> Exactly. It shows the prompt at each

step, the reasoning, which tool was

chosen, the exact parameters sent to the

tool, the observation received back,

everything. It lets you pinpoint where

things went wrong in that complex loop.

>> That sounds essential. What about

feedback from actual users?

>> Human feedback is gold dust. The process

should be. When a user reports a failure

or a weird behavior, you don't just fix

it. You capture that scenario, reproduce

it, and turn it into a new permanent

test case in your golden data set.

>> So, you vaccinate the system against

that specific error recurring.

>> Precisely. It drives continuous

improvement and makes the agent more

robust over time.

>> Let's talk security and scaling. Giving

these agents the power to act using

tools. That sounds potentially risky.

>> It is. There's a fundamental tension,

the trust trade-off. More utility often

means more potential risk. Security

needs multiple layers, what's called

defense in depth.

>> Like what?

>> You need hard-coded guard rails, simple

rules enforced by code, like a policy

engine blocking any API call that tries

to spend over a certain limit.

>> Yeah.

>> But you also layer on AI based guard

models.

>> More AI checking AI.

>> Yeah. These models specifically look for

risky steps before execution. Is the

agent about to leak sensitive data? Is

it trying to perform a forbidden action?

the guard model flags it potentially

stopping the action.

>> And a key part of this is giving the

agent its own identity.

>> Absolutely fundamental. An agent isn't

just acting as the user. It's a new

actor in your system. It needs its own

secure, verifiable identity. Think of it

like a digital passport, often using

standards like sexbf.

>> Why is that so important?

>> Because it allows for leech privilege

permissions. You can grant the sales

agent access to the CRM tool, but

explicitly deny it access to the HR

database. The agents identity determines

what it's allowed to touch.

>> That makes sense for individual agents,

but what happens when you scale up to

level three or four with potentially

dozens or hundreds of agents

interacting? Agent sprawl.

>> Agent sprawl is a real risk. Management

becomes key. You need agent governance,

typically through a central control

plane or gateway.

>> A single point of control.

>> Yes. All traffic user to agent, agent to

tool, even agent to agent communication

must go through this gateway. It

enforces policies, handles

authentication, and gives you that

crucial single pane of glass for

monitoring logs, metrics, and traces

across your entire agent fleet.

>> Okay, last couple of points. How do

these agents learn and evolve over time?

Do they get better automatically?

>> They need to adapt. Otherwise, their

performance degrades as the world

changes. Learning comes from analyzing

their runtime experience. those logs and

traces, user feedback, and also from

external signals like updated company

policies. This feedback loop fuels

optimization, maybe by refining the

system prompts, improving the context

engineering, or even optimizing or

creating new tools.

>> And the white paper mentions simulation,

an agent gym.

>> Yeah, that's kind of the next frontier,

especially for complex multi- aent

systems. It's about having a dedicated

safe off-production environment where

you can simulate interactions, use

synthetic data, maybe even involve

domain experts to really stress test and

optimize how agents collaborate or how

they handle novel situations without

impacting real users.

>> Let's make this concrete with some

examples. Google co-scientist,

>> right? Co-scientist is a fascinating

example of a level three or maybe even

pushing level four system for scientific

research. It acts like a virtual

research collaborator. There's a

supervisor agent managing the project,

delegating tasks like formulating

hypotheses, designing experiments,

analyzing data to a whole team of

specialized agents. It iterates,

refineses ideas, basically mirrors a

human research workflow, but potentially

much faster.

>> And Alphavolve, that sounds even more

abstract.

>> Alphavolve is definitely in the level

four space. It's an AI system designed

to discover and optimize algorithms. It

uses the code generation power of LMS to

create potential algorithms, but then

combines that with an automated

evolutionary process to test and improve

them rigorously

>> and it's found useful things

>> reportedly. Yes, things like more

efficient data center operations, even

faster ways to do fundamental math like

matrix multiplication. But the key is

the partnership the AI generates

solutions often as code. But humans

provide the expert guidance, define what

counts as a better algorithm via

evaluation metrics and ensure the

solutions are understandable. So

wrapping this all up, it really feels

like building successful agents isn't

just about having the smartest model.

>> Not at all. That's the core message. The

agent is the combination of the model

for reasoning, the tools for action, and

the orchestration layer managing that

loop. Success really hinges on the

engineering rigor around it. The

architecture, the governance, security,

testing, observability. That's what

makes it production ready.

>> So for you listening, the takeaway is

that your role is evolving. You're

becoming less of just a coder and more

of an architect, a director guiding

these increasingly autonomous systems.

>> Absolutely. These agents aren't just

fancy automations. They have the

potential to be genuinely collaborative,

adaptable partners in tackling complex

work.

>> It's a powerful concept. We really

encourage you to check out the Google X

Kaggle course materials. Dig into that

day one white paper and maybe start

thinking about how you could build your

own production grade agentic systems. It

feels like the future is definitely