You know that immediate mental hurdle when you
start thinking about an AI agent project? It's
almost always complexity. Your mind just leaps
to... Perfect prompts, multi -agent setups, rock
-solid security. Right. And these ideas, they're
important down the line, sure, but they kill,
what, 90 % of projects before they even get off
the ground? It's total analysis paralysis. It
feels like you should be thinking about that
stuff like it's best practice. But the reality,
the sort of liberating truth here, is that the
90 % you actually need to build something that
works, something you can ship. You can probably
grasp that in, like, under an hour. So welcome
to the deep dive. Our goal here is to guide you
through what really matters with AI agents quickly
without you getting lost in all the noise. Yeah,
we want to cut right through that perfectionism
trap. Isolate those practical ship it today basics.
Exactly. So today we're going to define the four
core parts of any agent. Lay out a simple three
-step launch sequence and cover the essential
lightweight ways to handle tools, security, and
actually deploying this thing. Okay, let's dive
in and try to make this feel a bit less daunting.
So segment one is this perfectionism trap. I
think we've all felt this. You get an idea for
an agent and immediately you're picturing how,
I don't know. Google or OpenAI would build it,
this massive, intricate system. And that's the
moment, right? That's usually where the idea
just stalls. Yeah. Because it's this engineering
exercise for a scale you don't even have yet.
Yeah. But the pattern for success, what we actually
see working, it's developers starting really
simple. Shipping fast. Shipping fast. And then
iterating based on what real users are telling
them. not based on some abstract theory of perfection.
So the core message is really resist that temptation.
Focus on the practical 90%, just what you need
to launch something basic that works. What do
you think is the main psychological block there?
What stops people shipping? It's overcomplicating
that foundation. That's what kills projects before
they start. Simplicity, especially early on,
that's actually a feature, not a bug. Okay, so
let's talk anatomy. Before touching any code,
what are the absolute essential parts? You're
saying there are just four. Just four core components,
yeah. Yeah. Doesn't matter how fancy it gets
later, these are the building blocks. First up,
tools. Think of these as the agent's hands. Okay.
Their functions, letting the agent, you know,
interact with the outside world, search the web,
query a database, send an email. Right. Without
tools, it's just talking. It can't do anything.
Exactly. It's just a chatbot otherwise. Number
two, the large language model, the LLM. That's
the brain. Your GPT -clawed Gemini. Right. The
reasoning engine, it looks at the user's request,
looks at its instructions, and decides, okay,
which tool do I need for this? Then number three
is the system prompt, the instruction manual.
Yeah, that's a good way to put it. It's the high
-level programming. Defines the agent's personality.
Is it helpful? Snarky. Sets its goals, its rules
of engagement. And the fourth piece is memory
systems, the context. Crucial. This covers short
-term memory, basically, the current conversation
history, and potentially long -term memory, like
remembering user preferences from last week or
important facts it learned. And that's it. Everything
else, deployment, monitoring, that's all just
refinement. It's refinement, not the core foundation.
The LLM is the brain, sure, but which component
actually lets it do things in the real world?
That has to be the tools. They're the hands letting
it interact with APIs, databases, whatever it
needs. Okay, so how do we actually launch this?
The hello world for agents. It's just three steps.
Step one. Pick an LLM for prototyping. Just pick
something cheap and fast. Don't agonize over
it. Cloud Haiku is great for this. Okay, cheap
and fast. Step two. Write a basic, clear system
prompt. Just the essentials. You'll refine it
later once you see how people actually use it.
And step three. Add one tool. for just one to
start maybe a simple web search or even just
a calculator and if you do those three things
you have a working agent you can take an instruction
reason about it and perform an action and honestly
this can be like less than 50 lines of code it's
really not complex to start Okay, you mentioned
setting up the LLM connection. You talked about
OpenRouter. Why is that important early on? Ah,
yeah, good question. OpenRouter basically gives
you one API key that works for almost all the
major LLMs, GPT, Cloud Gemini, Mistral, you name
it. So switching is easy. Super easy. You change
like one line of text in your config file, and
boom, you're using a different model. No vendor
lock -in, easy testing. It's a smart move from
day one. You know, I have to admit, I still sometimes
wrestle with the temptation to just let the LLM
handle things like math. Skip writing a calculator
tool. Oh, totally. It feels like it should be
smart enough, right? Yeah. That's a classic trap.
LLMs are fundamentally token prediction machines.
They predict the next likely word or token. That
makes them surprisingly bad at precise arithmetic,
like really bad sometimes. So adding a simple
calculator tool isn't just a nice -to -have,
it's often necessary. It compensates for a core
limitation of the LLM itself. Why is adding a
tool for something basic like arithmetic so crucial
then? Because LLMs are just inherently weak at
precise math. They need tools to make up for
that fundamental gap. Okay, let's nail down some
specifics. LLM choices. For prototyping, like
I said, I really like Cloud Haiku 4 .5. It's
super cheap, incredibly fast, great for just
getting things working. And then when you think
about maybe moving towards production. Cloud
Sonnet 4 .5 seems to be hitting a sweet spot
right now. Good balance of intelligence, speed,
and cost. But again, the real point here is use
something like OpenRouter. Right. So switching
between Haiku, Sonnet, maybe even GPT -4 is just
changing that one line. Don't get locked in because
of hype. Test what works for your use case. Makes
sense. And the system prompt, that instruction
manual. You mentioned a template to avoid staring
at a blank page. Yeah, a simple five -section
template really helps structure your thinking.
What are the core sections? First three are key
to start. One, persona and goals, just two or
three sentences. Two, tool instructions, how
and when to use the tools you've given it. Three,
output format tone. How detailed should it be?
That sort of thing. And the other two sections.
You said ignore them at first. Right. Section
four is for examples really only needed for complex,
multi -step tool workflows. And section five
is miscellaneous instructions. This is like the
fix it section. So why start with just the first
three and ignore the others initially? Because
you should only fill in that miscellaneous section
based on observed behavior. Let your agent mess
up in testing. If it tries scheduling meetings
at 3 a .m., then you add a rule. Never schedule
meetings before 9 a .m. Use real feedback. Don't
guess about edge cases up front. Start simple.
Okay. Tool strategy. You mentioned a limit. Yeah.
A general rule of thumb is try to keep it under
10 tools per agent. Once you go beyond that,
the LLM tends to get confused. The performance
drops. It starts picking the wrong tool. It just
gets overwhelmed. Only 10. I've seen people build
agents with like dozens of tiny little function
calls. Why the limit? It's really about the LLM's
cognitive load and the context window. Every
tool you add, you have to describe it in the
prompt. That eats up precious token space. And
the more tools it has to choose from, the more
time and tokens it spends just deciding which
one to use. Often incorrectly if there are too
many similar options. Keep it focused. And if
you could only build out one core capability
at first. RJ. No question. Retrieval Augmented
Generation. That's giving the agent access to
search private documents, right? Internal knowledge
basis. Exactly. And the data we're seeing suggests
something like 80 % or more of the real business
value from agents comes from this capability.
Letting it use your company's internal data,
customer history, technical docs. Master RRA
first. Master RAG. And you unlock the most valuable
use cases right out of the gate. Wow. Yeah, I
can see that. Imagine just asking it to pull
key risks from the last thousand customer contracts
instantly. Yeah. Yeah, that's huge for a business.
Totally transformative. Now, security. Don't
panic. Just basic hygiene. First rule. Never,
ever hard code API keys in your code. Use environment
variables. Standard practice. Standard practice.
Then look at Guardrails AI. It's an open source
Python tool. It lets you basically wrap your
agent. Wrap it? What does that do? It gives you
input protection. It can block things like prompt
injection attacks or filter out PII, you know,
personally identifiable information like names,
addresses. Okay, so it cleans the input. And
it filters the output, too. It can check for
factual consistency, make sure the agent isn't
leaking sensitive data. It's like a basic safety
net, lets you ship things internally with a lot
more confidence. And you also mentioned SNCC
for vulnerabilities. Yeah, there are prepackaged
tool collections, those called MCP servers, that
bundle security checks. Using something like
SNCCs can automate vulnerability detection during
development. Just good practice. Yeah. So if
you're focusing on just one tool capability for
that first agent, what's the priority? It's got
to be our key. Because that ability to tap into
private company data, that's where you'll find
the most immediate business value. Okay, last
lap. Optimization and actually shipping this
thing. Cost is a big one. Context window costs.
Right. You pay per token, both input and output.
So keep your system prompts concise. Keep your
tool descriptions tight. They get sent with every
single call to the LLM. Don't be verbose there.
And for memory. For the conversation history.
You absolutely need that sliding window memory
trick we talked about. Don't send the entire
chat history back every time, especially if it's
a long conversation. Just the last, say, 10 or
20 messages. Exactly. Use that simple list slicing
like conversation 10 in Python. It dramatically
cuts down your token costs. What about things
the agent needs to remember long term? like user
preferences or facts it learned weeks ago. For
that, you'd look at something like MEM0. It's
an open source tool specifically for persistent
memory. It uses AG principles itself to store
facts sufficiently and only retrieve the relevant
ones for the current query. So it doesn't stuff
the main context window with old info. Right.
Avoids those unnecessary token costs for stuff
that's not immediately needed. Then there's observability,
seeing what's going on inside. Yeah, you really
need this for debugging and just understanding
costs. LangFuse is a great, easy to integrate
option. Gives you a dashboard. What does it track?
Tracks the whole execution flow step by step.
Shows you token usage per step, latency, which
system prompt was used. Yeah. Invaluable when
your agent does something weird and you need
to figure out why. And finally, deployment, getting
it running. Think Docker native right from the
start. Yeah. Build your agent inside a Docker
container. Makes it super portable. Is it heavy?
Does it need big servers? That's the surprising
part. AI agents are usually really lightweight.
All the heavy computation, the LLM inference
that happens on open AIs or anthropic servers.
Oh, okay. Your code is mostly just managing API
calls and maybe running a simple tool. So it
can run on a small, cheap server. A Docker container,
maybe with a basic web front end like Streamlit
if it's chatbot. Or just a serverless function
like EWS Lambda if it runs in the background.
Keep it simple. So token costs, they add up fast.
What's the absolute simplest, most effective
way to manage memory for a long chat? Use that
sliding window. Send only the last 10 or 20 messages.
That saves huge amounts on token costs compared
to sending the full history every time. Okay,
let's recap the big idea here. The 90 -10 rule.
What's the 90 % that really matters? The stuff
you should do now. All right. Bic and LLM Haiku
is great for testing. Write a basic system prompt,
just those first three template sections. Add
one to three tools. Really focus on ARG for accessing
internal data. That's usually the highest value
starting point. Add basic security with guardrails,
AI input, and output protection. Add simple observability,
something like LangFuse. Control that short -term
memory cost using the sliding window trick. And
build it all with Docker in mind from day one.
Easy deployment later. And critically, what's
the 10 % you should actively ignore at the start?
Ignore the complex multi -agent systems. Ignore
crafting 5 ,000 -word perfect system prompts.
Ignore Kubernetes orchestration for your first
simple agent. Ignore building massive custom
evaluation test suites before you even have user
feedback. Right. The agents that actually ship.
They shipped because the builders resisted that
urge to over -engineer everything up front. Exactly.
Focus on the foundations, get it out there, get
real feedback, then iterate. So the call to action
is pretty clear. Find that 50 -line code example
mentioned in the source material, or just start
fresh with those core components. Build something
simple, like today. Yeah, don't wait for it to
be perfect. You don't fail by shipping something
simple or slightly flawed. You fail by letting
perfectionism stop you from learning. And you
only really learn from real -world usage. So
a final thought to leave you with. What simple
problem, maybe even an annoying little task you
do every day, could your first basic agent solve
right now? Outro music fades in.