You know, there is this specific kind of paralyzing anxiety that hits when you are staring at a terminal window or a fresh script, and you realize that one wrong keystroke could turn your entire afternoon into a recovery mission. It is the fear of the "blue smoke" or the corrupted partition. But today's prompt from Daniel hits on a fundamental truth of engineering: if you are not breaking things, you are probably not learning how they actually work. He is nudging us to talk about building a "safe sandbox" for agentic AI, and I think this is the perfect time for it because the barrier to entry for these autonomous agents is dropping fast, but the complexity is skyrocketing.
It is the classic "sandbox" philosophy, Corn. If you have a safety net, you perform better on the high wire. And speaking of performing well, I should mention that today's episode is actually powered by Google Gemini 1.5 Flash. It is helping us navigate this deep dive into agentic frameworks. I love Daniel's point about moving beyond the "low-code" stuff. Tools like n8n or Zapier are fantastic for productivity, but if you want to understand the "soul" of the machine—the latent space, the reasoning loops, the tool-calling logic—you have to get your hands dirty with the code. You have to be willing to watch an agent loop infinitely until it drains your API credits or tries to delete your system path, just so you can understand why it happened.
Spoken like a man who has accidentally spent fifty dollars in five minutes because an agent got stuck in a recursive loop with a "Search" tool. I have seen that look on your face, Herman Poppleberry. It is a mix of horror and scientific curiosity. But Daniel is right; the goal here is to lose the fear. We are going to look at how to set up an environment where a "hallucination" is a data point, not a disaster. We will walk through five specific projects that take you from "Hello World" to "My agent is basically my digital twin," and we will talk about the infrastructure you need to make sure that twin doesn't burn the house down.
Well, look, the infrastructure is the boring part that makes the exciting part possible. Most people try to learn this on their local machine, in a messy Python environment, and that is mistake number one. You want a disposable canvas. If I am building an agent that has the power to execute code—which is what frameworks like Open Interpreter or CrewAI allow—I do not want it anywhere near my personal documents or my primary operating system.
Right, because an agent with a "Python Interpreter" tool is essentially a remote shell that thinks for itself. That is a terrifying thought if you are running it on the same laptop you use for banking. So, let's frame this. What is a "test project" in this context? It is not a production app. It is a playground. It is a project where the "Definition of Done" is not "it works perfectly," but "I understand every failure mode that occurred during development."
That is a great way to put it. And the first step to that understanding is the environment. Daniel mentioned a VPS or a home server. I am a huge advocate for the VPS route for beginners. For example, back in January, DigitalOcean released an "AI Agent" one-click droplet. It basically pre-configures a Linux environment with the right drivers and container runtimes. Using a VPS gives you an "air gap" by default. If the agent goes haywire and fills the disk with logs or changes the root password, you just hit "rebuild" and you are back to a clean slate in sixty seconds.
But wait, Herman, for someone who hasn't used a VPS before, isn't there a risk of just moving the mess from your laptop to a cloud server? If the agent has access to the VPS terminal, couldn't it theoretically start sending out spam or participating in a DDoS attack if the LLM gets "prompt injected" by some malicious data it reads online?
That is a brilliant point, and it’s why the "sandbox" has to be layered. You don't just give the agent the keys to the VPS. You run the agent inside a restricted user account, or better yet, inside a container within that VPS. You treat the VPS as your "outer perimeter" and the container as your "inner sanctum." If the agent escapes the container, it’s still trapped on a five-dollar-a-month Linux box that has no connection to your real identity.
I can hear the listeners thinking, "But why not just use a local virtual environment?" And look, "venv" is fine for managing dependencies, but it doesn't protect your OS. If you are learning how agents interact with the file system—maybe you are building an agent that organizes your downloads—a local venv won't stop it from deleting your "Pictures" folder if its logic fails. This is where Docker comes in. Herman, you have been preaching the gospel of Docker for years, but for agentic AI, it feels like it has found its true calling.
It really has. Think of a Docker container as a "Lego block" for your code. It is completely isolated. If I run a command like "docker run dash it dash dash rm python three eleven slim bash," I am inside a clean Python environment. The "dash dash rm" flag is the magic part—the moment I exit that session, the entire container is deleted. No residual files, no broken paths. For agentic testing, you can give your agent the ability to spin up its own Docker containers to run the code it writes. This is what E2B, the Execution to Browser framework, does so well. It provides a secure, sandboxed environment for the agent to "think" and "act" without risk.
It is like giving a toddler a set of finger paints but covering the entire room in plastic sheeting first. You can let them go wild. But there is a security angle here too. If you are using a VPS, you are essentially putting a computer on the public internet. Daniel mentioned Tailscale and Cloudflare Access. I am a huge Tailscale fan. It is basically a zero-config VPN. You can have your VPS sitting in a data center in London, but to your laptop, it looks like it is on your local network. You don't have to open any ports to the scary, open internet.
And that "Zero Trust" model is crucial because, as we move into "Agentic AI," these systems are going to be making API calls and potentially receiving webhooks. You want that traffic to be encrypted and authenticated. Cloudflare Access is another great layer. It lets you put a "login" screen in front of any self-hosted tool, like an n8n instance or a custom agent dashboard, without you having to write a single line of authentication code. It is about building layers of defense so that when you inevitably make a mistake in your Python script—like hardcoding an API key or leaving a port open—the infrastructure catches you.
Okay, so we have our "Safe Sandbox." We have got a VPS, we are running Docker, we are secured by Tailscale. Now, let's talk about the actual projects. Daniel mentioned his movie recommendation bot. I love this as a "Level One" project because it sounds simple but exposes all the "agentic" friction points immediately. Herman, why is this harder than it looks?
It is the "State" problem, Corn. If I ask a standard LLM for a movie recommendation, it gives me a list based on its training data. But an "Agent" needs to actually check what is available on my specific streaming services in my specific region. That means the agent needs a "Tool" to query an API like TMDB or JustWatch. Then, it needs a "Memory" layer. If I told the agent last week that I hated "The Godfather"—which, for the record, I would never do—it needs to remember that. It shouldn't suggest it again today.
Right, and Daniel's point about geo-specificity is huge. If I am in Jerusalem and you are in the States, our Netflix libraries are different. So the agent has to: one, identify the user's location; two, query a live database; three, cross-reference that with the user's "Seen" list in a local database like SQLite or a vector store; and four, reason about why "Inception" is a better fit than "The Notebook" for a Friday night. That is a lot of "thinking" steps. How do you actually keep the agent from getting overwhelmed by all those steps?
You use a "Planner" pattern. Instead of just saying "Recommend a movie," you prompt the agent to first "Generate a plan." Step one: Get user location. Step two: Fetch user preferences from SQLite. Step three: Search JustWatch API for high-rated Sci-Fi movies in that region. Step four: Filter out anything the user has already seen. By making the agent write out its plan first, you can actually see where its logic is about to go off the rails. It’s like a cognitive "pre-flight check."
And that is where a framework like PydanticAI comes in. If you are building this, you want "Type Safety." You want to define a "Movie" object with specific fields: title, year, streaming service, and "why I recommended this." By using Pydantic, you force the LLM to return data in a structured format. If the LLM tries to give you a fuzzy answer, the code crashes at the validation step. In a "test project," that crash is your best friend. It tells you exactly where your prompt logic failed.
Now, let's move to "Level Two." Let's talk about a "Code Review Agent." This is a classic multi-agent setup that you can build with CrewAI. Imagine you have three agents. Agent One is the "Developer"—it takes a prompt and writes a Python script. Agent Two is the "Security Auditor"—it looks at that script specifically for vulnerabilities like SQL injection or hardcoded keys. Agent Three is the "Refactorer"—it takes the feedback from the Auditor and rewrites the code.
This is where CrewAI shines because it handles the "orchestration." You aren't just writing one long prompt; you are defining roles and tasks. The "Developer" has a specific "Backstory" and "Goal." The "Auditor" has a different one. What you learn here is "Agentic Friction." You will see the agents argue. You will see the Auditor reject code that is actually fine, or the Developer get stuck in a loop trying to satisfy a weird security requirement.
I once saw a "Security Auditor" agent refuse to let the "Developer" agent use the os module at all because it was "too risky." They went back and forth for ten minutes. The Developer was trying to justify why it needed to read a file, and the Auditor was basically saying, "I don't trust you with file handles." That kind of emergent behavior is exactly what you want to experience in a sandbox. It teaches you how to tune the "temperament" of your agents.
And because you are in your Docker sandbox, you can actually tell Agent One: "Execute the code you just wrote and show the output to the Auditor." If the code throws an error, the Auditor can say, "Hey, your code failed with a ModuleNotFoundError, you forgot to install the requests library." This is a closed-loop system. It is how you learn about "Error Handling" in an autonomous context. You are essentially building a tiny, digital engineering team.
It’s the closest thing we have to a "Perpetual Motion Machine" for software development. You provide the goal, and they provide the iterations. But you have to be careful—without a "Manager" agent or a maximum iteration limit, they will burn through your token quota trying to achieve perfection. That is a fun fact: some of the most expensive "bugs" in AI history aren't logic errors, they are "politeness loops" where two agents keep thanking each other and asking if there is anything else they can help with.
I love the idea of two agents arguing in a terminal window while I just sit back with a coffee and watch. It is like "The Real World," but for LLMs. But let's pivot to something more data-heavy. How about a "Personal Finance Analyst"? This would be "Level Three."
This is a great one for learning about "RAG" or Retrieval-Augmented Generation, but with a twist. You don't want to use your real bank data for a test project—that is rule number one of the sandbox. Instead, you use the Plaid API's "Sandbox Mode." It generates fake transaction data that looks real. Your agent's job is to fetch these transactions, categorize them, and look for patterns. "Hey Corn, you spent forty percent more on digital subscriptions this month, did you mean to sign up for three different AI video generators?"
Hey, those were for research, Herman! But seriously, the challenge here is "Long-term Memory." If I tell the agent in January that "Adobe" is a business expense, it needs to remember that in June. You would use something like ChromaDB or Pinecone to store these "memories" as embeddings. When a new transaction comes in, the agent "searches" its memory to see how it handled similar items in the past. This teaches you how to manage a vector database and how to "chunk" data so the agent doesn't get overwhelmed by a massive list of transactions.
And it teaches you about "Context Window" management. If you try to shove a whole year of transactions into one prompt, the model will lose the thread or get too expensive to run. You have to learn how to summarize. "Here is the summary of your January spending," and then store that summary in the memory layer. It is about building a hierarchical understanding of data.
But how does the agent deal with ambiguity? Like, if I have a transaction for "Amazon" that could be a book for work or a new blender for the kitchen? Does it just guess, or does it know to ask me?
That is the "Confidence Score" hurdle. You can prompt the agent to assign a confidence level to its categorization. If it’s below 80%, it flags it for human review. This is where you learn that "Agentic" doesn't have to mean "Fully Autonomous." Sometimes the best agent is the one that knows its own limitations. Building that "I'm not sure" branch into your code is a high-level skill.
Okay, "Level Four." Let's go deeper into the "Research Summarizer." This is more than just "summarize this PDF." I am talking about an agent that monitors arXiv for new papers on, say, "Sovereign AI" or "Small Language Models." It downloads the PDFs, uses a tool like "Marker" or "Unstructured" to turn that PDF into clean text, stores it in a RAG pipeline, and then—here is the agentic part—it cross-references the new paper against papers it already has in its database.
"This new paper from Google seems to contradict the findings of the Anthropic paper we read last week." That is the "Aha!" moment for an agent. To do this, you need to learn about "Document Loaders" and "Metadata Filtering." If you are using LangChain or LlamaIndex, you can tag each "chunk" of text with the author, the date, and the core claim. Then, when you ask the agent a question, it doesn't just give you a summary; it gives you a synthesized answer with citations. "According to Smith et al, this method is inefficient, which aligns with what we saw in the project we did in February."
This project is the ultimate test of "System Prompting." You have to be very specific about how the agent should handle conflicting information. Does it prioritize the most recent paper? Does it flag the contradiction to the user? You are essentially programming "Critical Thinking" through prompting. And again, if it fails—if it hallucinates a paper that doesn't exist—you are in a safe environment. You can dig into the "Trace" using a tool like LangSmith to see exactly which "chunk" of text led the agent astray.
LangSmith is a game changer for this. It’s like a microscope for your agent's thoughts. You can see the exact "retrieval" step where it pulled a paragraph from a 2019 paper and tried to apply it to a 2024 problem. It turns the "magic" of AI into a series of visible, debuggable steps. If you're building a Research Summarizer, you'll spend 10% of your time writing code and 90% of your time looking at traces trying to figure out why the agent thinks a specific researcher is a "leading expert in underwater basket weaving" because it misread a footnote.
And finally, "Level Five." This is the one that bridges the digital and physical worlds. The "IoT Home Automator." Now, Daniel mentioned a home server. If you have a Raspberry Pi or an old laptop running "Home Assistant," you can build an agent that sits on top of it. Instead of a "Scene" that you trigger manually, the agent monitors sensor data via MQTT.
"The temperature in the office is eighty degrees, and I know Corn has a meeting in ten minutes because I checked his calendar. I should turn on the fan now so the room is cool when he starts." That is "Agentic Logic." It involves "Function Calling" at a high level. The agent has a tool called "TurnOnFan" which, behind the scenes, sends a JSON packet to a smart plug.
The "Breakable" part here is "State Awareness." What happens if the fan is already on? What happens if the sensor is offline? You have to teach the agent to "check then act." This is a fundamental principle of robust engineering. If the agent tries to turn on a fan that is already on, and the smart plug API returns an error, how does the agent recover? Does it panic and loop? Or does it say, "Oh, it is already on, I will just proceed to the next task"?
There is a famous story in the home automation community about a guy whose automated blinds were controlled by a light sensor. A cloud passed over, the blinds opened. The sun came back, the blinds closed. It created this "strobe light" effect in his living room for three hours because he didn't have a "cooldown" period in his logic. With an AI agent, that kind of "oscillation" is even more likely because the agent might "reason" its way into a loop. "I should open the blinds for Vitamin D. Oh, it’s too hot, I should close them for cooling." Back and forth, forever.
This project also introduces "Human-in-the-Loop" or HITL. You might not want an AI agent having full control over your heater while you are asleep. So you build a "Checkpoint." The agent sends a notification to your phone: "I am planning to turn on the heater because it is freezing, do you approve?" You learn how to build that interaction layer between the autonomous logic and the human user.
It is the "Review-then-Execute" pattern we see in the 2026 AI developer guides. It is the gold standard for safety. Even in a "test project," building that checkpoint is a massive learning experience. It forces you to think about "Intent" and "Authorization." You start to realize that the hardest part of AI isn't the intelligence; it's the boundaries.
So we have five projects: the Movie Rec Bot for memory, the Code Reviewer for multi-agent orchestration, the Finance Analyst for RAG and data validation, the Research Assistant for complex synthesis, and the IoT Automator for real-world function calling. That is a full curriculum right there.
It really is. And the beauty of Daniel's "Safe Sandbox" approach is that you can start with Project One on a five-dollar-a-month VPS. You don't need a four-thousand-dollar GPU rig. You are using API calls to models like Gemini or Claude, and your "Code" is just the glue that holds the agentic loops together. The total cost of "breaking" these projects is basically the price of a couple of cups of coffee and some VPS uptime.
And a little bit of your pride when the agent tells you that your movie taste is "statistically basic." But seriously, the "break-fix" cycle is where the intuition is built. You can read the documentation for CrewAI all day, but until you see a "Manager Agent" get caught in a "Delegation Loop" where it just keeps asking the "Worker Agent" the same question over and over, you don't truly understand how to write a good "System Prompt."
You have to see the failure to appreciate the fix. And that leads to Daniel's point about "Snapshotting." If you are on a VPS, use the provider's snapshot tool before you run a major experiment. If you are using Docker, commit your images. If you are using Git—and you should be using Git for every single one of these—commit your changes every time you get a piece of logic working. That way, when you decide to "optimize" the code and everything breaks, you are one "git checkout" away from sanity.
It is the "Save Game" philosophy. You wouldn't play a boss fight in a video game without saving first. Why would you try to build an autonomous agent that can write to your database without a backup? It is pure hubris, Herman. Pure hubris.
Guilty as charged. I have definitely "YOLO-ed" a script or two in my time. But the older I get, the more I love my snapshots. One other tip for the environment: use a "Logging" layer. Don't just rely on the terminal output. Use something like "Loguru" in Python to write every agent thought, every tool call, and every error to a structured file. When an agent "breaks," the terminal often moves too fast to see the root cause. Having a log file you can search through is like having a "Flight Data Recorder" for your AI.
That is a great analogy. "Why did the agent decide to buy three hundred copies of a movie on DVD?" "Oh, look at the log at two-forty-five PM, it misinterpreted the 'Buy' button as a 'Check Price' button." That is how you debug the "Mind" of the machine. It’s much more about forensics than it is about syntax.
And that is the shift. We are moving from debugging "Code" to debugging "Reasoning." In a traditional program, a bug is a syntax error or a logic flaw. In an agent, a bug is often a "Misunderstanding" of the goal. You only catch those misunderstandings by looking at the "Trace"—the step-by-step internal monologue of the agent. You have to ask, "What was the agent thinking right before it decided to delete the root directory?"
Which brings us back to why Daniel is right about learning code frameworks over low-code tools. In n8n, you see the "Nodes" and the "Lines." It is very visual, and it is great for seeing the flow. But in a Python framework like PydanticAI, you can see the "Trace" at the function level. You can see exactly how the "Context" was built before it was sent to the LLM. You have total visibility into the "Black Box."
Precisely. You are building the box, not just sitting inside it. And for those worried about the "Code" part—LLMs are the best coding tutors in history. If you don't know how to write a Dockerfile, ask the LLM to write one for you and explain every line. If you don't understand why a Pydantic model is failing, paste the error into the chat and ask for a "Deep Dive" on type validation. The "Test Project" is the context that makes the LLM's teaching effective.
It turns the LLM from a "Magic Wand" into a "Pair Programmer." You are working together to build this sandbox. It is a virtuous cycle. You build a safe place to break things, you use the AI to help you build a project, the project breaks, you use the AI to understand why it broke, and in the process, you actually learn the underlying technology. It’s like having a senior engineer sitting next to you who never gets tired of your stupid questions.
It is the only way to stay relevant in 2026. Things are moving too fast to rely on "Static Learning." You have to be in a constant state of "Active Prototyping." And look, we have covered a lot of ground here, from VPS setups to IoT home automation. The "Practical Takeaway" is simple: pick one of these five projects—I’d suggest the Movie Bot or the Code Reviewer—and commit to building it this weekend. Don't worry about making it pretty. Make it functional, and then make it fail.
But don't just "Build" it. Commit to "Breaking" it. Try to make the Movie Bot hallucinate. Try to make the Code Reviewer approve a script that is obviously broken. See if you can "Jailbreak" your own agent. The more you understand the "Edges" of the sandbox, the more confident you will be when you eventually have to build something for "Production." You want to know exactly where the cliff is so you don't go over it when it matters.
And document the failures! Write a blog post, or a tweet, or just a note to yourself about "Three ways I broke my agent today." That is the real "Syllabus" of the future. I think we have given Daniel plenty to chew on here. His "Movie Bot" is a fantastic start, but adding that "Geo-Specific" and "Memory" layer is where the real engineering happens. It’s the difference between a toy and a tool.
He is already halfway to being the "Agent King" of Netflix recommendations. He just needs to get that vector database dialed in. And maybe a small script to make sure it doesn't recommend "Cats" more than once a year.
Even an AI should have better taste than that. Well, this has been a blast. I feel like I need to go spin up a new VPS just thinking about it. There’s something addictive about a clean terminal and a fresh API key.
I already have three running in the background while we have been talking, Corn. I am currently "Stress Testing" a new summarization loop. I will let you know if it tries to take over the podcast. It’s currently at step 42 of a 100-step reasoning chain, and it hasn't crashed yet, which is both impressive and slightly terrifying.
Please don't let it replace us just yet. One Herman Poppleberry is more than enough for this show. Huge thanks as always to our producer, Hilbert Flumingtop, for keeping the gears turning behind the scenes. And a big thanks to Modal for providing the GPU credits that power our research and the generation of this very episode. Without that compute, we'd just be two guys shouting into a void.
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This has been My Weird Prompts. Go build something, break it, and then build it better. The sandbox is waiting.
See ya.
Bye.
