Latest / Elon Musk Podcast / A Simple Guide to AI Agents

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A Simple Guide to AI Agents · Elon Musk Podcast

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This is the Elon Musk Podcast, your daily hit of what is really
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going on at Tesla, SpaceX X AI, and the rest of the Musk
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universe. I'm your host Will Walden, and I
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have covered Elon Musk for more than five years, spent a year on
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the ground at SpaceX, Starbase during early Starship
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development, and before this I spent my career as a software
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developer working with billion dollar companies.
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I've also built and sold my own businesses and now I make
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content and help other people grow their companies.
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Now on this show I used that experience to break down the
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news, filter out all the noise, and give you clear context.
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You can actually use a Gentek AI gives an AI system the ability
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to take small sensible steps towards a goal.
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Instead of stopping after one single reply, think of it as
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moving from a one time answer to a finished task with receipts
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that you can actually review. Businesses use it to book
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travel, organize calendars, clean out spreadsheets, and
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draft polite emails that follow company rules.
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And you get outcomes rather than lose suggestions with a record
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of how each decision was actually happening.
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And how does it actually work in everyday life.
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Though in this episode, you'll get a plain English definition,
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a simple loop that you can picture in your head, and a few
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real world stories you can adapt at home or at work.
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And we're going to talk about what it does well, where it
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needs clear instructions of how to keep control with approvals,
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spending caps, and time limits. And by the end, you'll know how
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to start small, test safely, and grow with confidence.
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And we're going to get into that right after this short
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commercial break. Here is the short definition of
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agentic AI. It is a system that pursues a
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goal through several steps, using tools along the way, until
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it succeeds or runs out of allowed attempts.
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Regular chat gives you one response.
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Then you ask it again, and again and again and again.
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You get a bunch of responses. An agent continues on its own
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within the rules that you set for it.
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That shift from answers to outcomes changes how you design
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tasks, how you approve actions, and how you judge success.
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Now picture a helpful assistant with a checklist.
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You say plan a family weekend that fits a budget, includes a
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museum visit. Keep Sunday afternoon free.
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And the assistant breaks the goal into steps, chooses the
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next step, uses the right apps, writes down what it found, and
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it decides what to do next. In agentic AI, the assistant is
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the model. The apps are tools like a
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calendar, a map, a price look up, or your notes.
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In the written record is a log you can read later.
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System repeats this plan, act and check pattern until it
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reaches the goal or asks you for help.
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It may fail and the main parts are easy to remember.
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First, the goal, which states what done looks like in simple
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terms such as stay under $600, keep walking time short, and
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pick refundable options. Second, a planner which turns
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the goal into a short list of small steps.
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Third, tools which are safe buttons the agent can push like
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search hotels, read a spreadsheet, or add an event.
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4th. Memory, which stores notes from
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each step so the next step does not forget what just happened.
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5th. A checker which asks if the last
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step helped or hurt. 6th. A stop rule which ends the run
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when the job is done, when a limit is hit, or when the system
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needs your approval, or when it completely fails.
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Now let us walk through a travel example without any jargon.
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You tell the agent plan a 2 day Austin trip for two adults, keep
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lodging out of $200 per night, include a live music event, and
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leave time for some nice Texas BBQ.
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Now the planner lays out steps like find dates, look up hotels,
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check refund policies, search music events, draft the
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schedule, and prepare a summary for you.
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The agent goes to work. It uses a hotel lookup tool,
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writes down choices for you, compares prices for your budget,
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and drops the top pick onto a calendar for you.
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And after each step, the checker asks if the plan still fits the
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rules. Now the stop rule ends the loop
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when the schedule, budget, and refund notes meet that goal.
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Then the agent hands you a clear summary and a log of every
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single action that happened there.
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And the style of AI works best on chores with clear rules,
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repeatable steps, and measurable results.
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Reading bills, copying key numbers into a tracker, and
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writing a short status note fits well because each step has a
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right or wrong outcome. Drafting polite replies to
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common messages fits well because examples teach the tone
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and structure. open-ended tasks with fuzzy goals like make
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something creative for the website isn't a good one.
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It's not a good prompt for that. It can drift because the success
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target is not clear. You guide the agent by setting a
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very narrow goal. List rules in plain English and
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name the tools it may use. Now you got to think about
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control and safety. It's all within this loop, but
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not at the end. You give the agent only approved
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tools like a read only calendar or hotel search that cannot buy
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anything. You add spending caps, time
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limits, and a maximum number of steps.
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You require human approval for anything irreversible, such as
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purpose purchases, deletions, or messages to customers.
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You keep private information safe by redacting secrets,
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limiting who the agent can contact, and preventing it from
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pasting data into public sites. These basic moves act like
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seatbelts, locks, and curfews for a very fast helper, and the
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helper can do these tasks within seconds sometimes.
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So you have to have rules. Now there are a few simple
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shapes for how agents works. One helper handles a straight
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path, like reading a form, filling a tracker, and writing a
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summary about it. A planner and a doer split the
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job, which reduces trial and error and keeps the log knee.
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A small team uses a supervisor to route tasks to specialists,
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like a calendar specialist, a data specialist, and a writing
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specialist. Many teams do well with the
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planner and do repair because it stays simple, exposes the
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decision points, and makes reviews really fast.
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Now you do not need a big platform to start.
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Think of tools as small safe buttons with clear labels and
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clear limits. One button reads a file, 1
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button looks up a price, 1 button adds a calendar entry.
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Each button returns a simple result that the agent can
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understand, and the log shows which button the agent pressed,
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which words it sent into the button, and what came back.
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If something looks odd, you can replay the run and see exactly
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where it went off track and you can figure out how to fix that.
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Humans are still needed at this point with AI.
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Some are really good. I run an agent sometime to get
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me the latest news on Elon Musk and it does wonders for me.
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Now. The process that it takes is
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very straightforward. I say check these amount of news
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sources, check 20 news sources, etcetera, etcetera, and then
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send me the links. It's very simple.
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My my agent is very simple. Send me the links, give me a
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like a three bullet point rundown of what the article is
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about. See if it's worth my time.
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I'm going to redevelop this agent into something a little
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bit more robust. So it's easier for me to do this
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podcast. I've been doing this podcast for
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a very, very long time, five years or so thousand episodes
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plus. So if you are a fan of the show,
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thank you, and if you aren't, thank you for stopping by and
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listening. And also, since you are a new
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fan to the show, please take a second and hit the follow
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button. That'll be really helpful.
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Or pick up some merch at starshipshirts.com.
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That helps out tremendously. So agents are really cool
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because you can measure an agent the same way you measure a
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person who handles a task. Then it finished the task, yes
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or no. How long did it take?
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How much did it cost to run it? How often did it ask for help?
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Now those numbers give you a clear picture of whether the
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system saves time or actually creates more work for you.
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When I first started developing my agent, I was, it was taking
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me more time than it than it was before I had it just to get it
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right. But by fine tuning it, I have a
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clear path to, you know, the best results for the day.
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I can't just Google things. You get a bunch of just junk in
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there and you can't just chat TBT things because it doesn't
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give you the right answers. So you have to make something.
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I had to make something custom. I had to make a custom agent to
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do the things that I wanted to do.
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And it has a very simple clear task.
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And then it doesn't fail anymore because it knows exactly what to
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do, has like 4 things it needs to do.
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Search the web on certain sites. Send me back the headline.
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Send me back three bullet points about the article because it
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reads the article. Send back three bullet points of
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the article. Send me a link to the news piece
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so I can check it out myself. And that's it.
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That's all it does. And then I read everything I can
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and come up with stuff on my own.
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So my agent is very simple. You can make something
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absolutely complex. Crazy, you know?
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How often does it ask for help? That's the important one, right?
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So those numbers give you a clear picture of whether the
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system saves time or creates a huge headache for you.
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And if it creates the headache, either work on it more or find
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another process. I had to go through 5 or 6
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different processes in order for my agent to work properly.
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If you have the time, great. If not, there are systems out
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there that will make you an agent.
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Or you can do like a drag and drop agent for this kind of
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stuff. And you have to treat each run
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like a transaction with a result you can accept or reject because
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each one of these costs compute time or tokens.
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If you do it on something, you know where somebody else hosts
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the LLM. And when you reject a result,
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add a quick note about what went wrong and then turn that note
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into a rule the agent can follow the next time.
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And let's talk about like a story from an office because
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that might put it into perspective.
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So customer support, we all hate it, right?
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We all hate going through customer support.
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If something goes wrong, we want to answer now.
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But if you're on the other end where you're a customer support
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agent, inbox assistance are important.
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And if you have a goal, the goal would say, read each new
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message, match it to one of five common issues.
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Suggest a reply that follows the playbook, which is, you know,
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you have a playbook that you feed it and hand anything
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unusual to a person. So the agent opens an e-mail,
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finds key details, checks the playbook, drafts a reply and
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puts it into a queue for a person to review.
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And if the person reviews it and it needs a little bit of help,
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type in there, you know, sorry, sorry you're having this issue.
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My name is Will. I'm here to help you.
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And that person confirms that the draft uses the right issue
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type and the right tone. So it's basically a huge
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database of things like the playbook.
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Now, a person gives a quick approval on the first week of
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use, then reduces approvals to only high stakes messages once
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the numbers look steady. So you're training this model
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while it's doing its job. Now, another one we can think
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about is where you mix tools and approvals.
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So if you're a finance helper and it could check small
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purchase requests, the goal says ensure the request sits within
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the budget, confirm the vendor is on the approved list and
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prepare a draft purchase order. The agent reads the request,
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looks up the budget balance, matches the vendor name, and
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fills a form. The stop rule blocks any e-mail
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to a vendor until a human clicks approve, and the log acts like a
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receipt for an audit, which keeps everyone comfortable with
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the tool that touches their money.
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You don't mess with people's money, man.
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So if you want to start this, it's straightforward and there
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are tools, you have to look them up.
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I don't want to suggest anything because I'm not very, I'm not
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very educated in what tools are out there other than the ones
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that I build myself. I'm a coder, so I build these
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things myself. But to start, you can pick a
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tiny job that annoys you somebody like pulling a number
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from a document and dropping it into a tracker or drafting a
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follow up e-mail after a meeting.
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Google does this a lot. You know the all the e-mail apps
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do this now, but a follow up e-mail after a meeting?
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You could have your agent do that for you.
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Write a goal in one sentence and list 3 rules the agent must
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follow. It's super simple, a goal.
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You could tell the the AI agent every time I get an e-mail, send
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it to my phone or what you know, like give me a give me an alert
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on my phone, something like that.
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Send an alert to my phone number and text you, you know, text you
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a summary of the e-mail. And if it sees seems like a big
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deal, then sure, answer it. But if not, just leave it alone.
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And I know there are e-mail apps that do that by themselves, but
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you know, we're just making a, a silly model right now.
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Or if certain person emails me, send me a text.
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How about that? That would be great.
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That's an actual like a list of a actual thing that could be
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helpful. If my boss emails me, send me a
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text because sometimes your e-mail alerts don't go through
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or sometime it's after office hours, but your boss might need
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to get a hold of you. You don't have your e-mail app
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on your phone. Your your work e-mail app on
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your phone. So you know you do you get a
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text from your boss. It's 8:00 at night when you're
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putting your kid to bed. That would be the worst app
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ever. Don't do that.
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I, I'll tell you this, do not have your work and your home
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like and your personal phone as one phone.
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That's the worst idea ever. Unless of course, you run your
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own business and then you got to do it.
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So eventually you can just run it on autopilot and does all the
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stuff for you. You can have your agent do so
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many different things and you can check out different
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services. Like I said, I don't want to
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recommend anything because I don't really use them myself
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personally. I built them, so I build them
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for my own personal use. So I hope that this has cleared
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a few things up for you. It's like a helper, you know, if
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you, if you have an executive assistant, that's kind of what
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an AI agent, agentic AI is all about.
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It helps you do those tasks that you just don't want to do.
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It turns a chatty program into a helper that completes tasks by
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planning those steps using tools that are safe and then checking
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along the way so you can get the results that you want.
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You just got to give it a clear goal, a few rules and some
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buttons that it can push. And you keep all the control
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too. You get readable logs from it.
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You measure success with finish rates, time to finish and
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escalations. Like does it send it back to
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you? So start small and confirm the
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value and grow your agentic AI at your own pace.
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You don't have to make a huge project, make something very
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small that'll fix a task or fix a problem that you're having
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right now. Hey, thank you so much for
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listening today. I really do appreciate your
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support. If you could take a second and
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hit the subscribe or the follow button on whatever podcast
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platform that you're listening on right now, I greatly
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appreciate it. It helps out the show
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tremendously and you'll never miss an episode.
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And each episode is about 10 minutes or less to get you
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support the show even more, go to patreon.com/stagezero and
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please take care of yourselves and each other and I'll see you
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tomorrow.