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The Codebase Singularity: “My agents run my codebase better than I can” — Transcript

by IndyDevDan · 3,076 words · 456 segments · language en · Watch on YouTube

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0:00There is one mental framework that sits
0:03at the center. An idea so important that
0:06if you capture it, it can change the way
0:08you engineer forever. The agentic layer.
0:12This is the new ring around your
0:15codebase where you teach your agents to
0:17operate your application on your behalf
0:20as well and even better than you and
0:23your team ever could. Focusing on
0:25building the agentic layer of your
0:27codebase is the highest return on
0:30investment action for any engineer in
0:33the age of agents we live in. Why is
0:36that? As you know, when you scale your
0:38compute, you scale your impact. We're
0:42not just AI coding anymore. Our agents
0:44can take actions on our behalf. And this
0:47has changed engineering forever. Once
0:50you've built a sufficiently powerful
0:52agentic layer, something will happen.
0:55The codebase singularity. In this
0:58moment, you, the engineer, will realize
1:00one simple fact. My agents can now run
1:04my codebase better than I can. I trust
1:07them to ship more than I trust myself or
1:10my team. Nothing ships to production
1:12without my teams of agents. Yes, this
1:16might sound crazy. It might sound far
1:18out, but if you've been pushing what you
1:20can do with agents, if you've been
1:22putting these tactics to work already,
1:24maybe you already see this future on the
1:27horizon, the agentic horizon. I can tell
1:30you this for certain right now. There is
1:32an agentic layer that could exist inside
1:35your codebase so powerful that your
1:39codebase runs itself. The only question
1:42now is, do you know how to build it? As
1:46we work through building agentic layers,
1:49keep this idea in your mind and think
1:52through what it would take for you to
1:54trust your agents to run your codebase
1:57better than you could from prompt to
2:00production. In this lesson, we're going
2:01to put together the missing pieces and
2:04see how we can attain the codebase
2:06singularity.
2:11There are three concrete classes of the
2:15agentic layer. Class one, class 2, and
2:18class three. Each defined by a unique
2:21element that makes them distinct from
2:23the rest. Our whole goal here is to look
2:26at the components, the raw elements that
2:28make up the new ring around your
2:30codebase where you have agents drive
2:33your engineering experience and you
2:35drive your agents. the green squares
2:38here, the outer layer, this is the new
2:40aentic layer. The inner layer here,
2:43right, these dark squares, this is going
2:46to be your application layer. And we're
2:48bundling a ton of things underneath your
2:50application layer. We're talking about
2:51your database, your front end, your
2:53backend, your scripts, all the
2:55application stuff, even your DevOps
2:57stuff, right? This all goes underneath
2:59the application layer. Why is that? It's
3:01because we want to be able to bundle
3:03your different repositories underneath
3:06your agentic layer so that we can do
3:08something like this. Code bases often
3:11contain more than one application. So by
3:14bundling your agentic layer around your
3:16applications, your agents can
3:18effectively see everything. Every class
3:21is going to have one to n grades that
3:24will give you a rough understanding of
3:26how powerful your agentic layer is.
3:28You're going to be able to quickly
3:30identify where you are as we move
3:31through classes and grades so that you
3:34can make improvements and get to that
3:36next level. And then once you reach the
3:38limit of a certain class, look to make
3:40that next jump uniquely identified by a
3:43new dimension that we'll talk about as
3:45we progress from grade to grade from
3:47class to class all the way up to class
3:49three and the final grade. Now, let's
3:53start by looking at a powerful version
3:55of a class 3 agentic system with an
3:58orchestrator guiding the workflow.
4:00You've seen powerful systems like this
4:02in our previous two agentic horizon
4:05lessons. What we're going to do now is
4:07run two brand new workflows and combine
4:10some really big ideas. I'm going to open
4:12up the prompt interface here as usual.
4:14And you'll notice we have one new
4:16section. Our orchestrator agent can now
4:19kick off AI developer workflows. I'll
4:22paste in this prompt here. And I'm
4:23commanding the orchestrator agent to
4:25kick off this specific workflow in this
4:27specific directory with just a single
4:30simple prompt. We're not over
4:32complicating this. I just want to show
4:33you so that you have a concrete vision
4:35of the types of systems you can build
4:38out. So we're going to fire this off
4:39here. Let's go ahead and run another
4:41build out a markdown preview application
4:44here in one shot with our plan, build,
4:47review, and fix. And this is an AI
4:49developer workflow. So the orchestrator
4:51doesn't need to do anything, right? This
4:52runs end to end. We're going to run one
4:55more workflow here. And then I want to
4:56show you something awesome. You've seen
4:58the generic log view. We have pushed our
5:00orchestrator to be able to control AI
5:03developer workflows. We now have two
5:06workflows running. So plan, build,
5:08review, fix, and just a simple plan
5:09build. We have given our orchestrator
5:11agent the ability to run arbitrary
5:14endtoend workflows. Now let's dial in
5:17and start from zero, right? Let's reset
5:20because we don't start here. We don't
5:21start at class 3. We don't start with
5:23powerful orchestration systems. We start
5:25with nothing. So as mentioned, every
5:29single codebase should now have these
5:31two key components. The application
5:33layer and the agentic layer. These two
5:36squares here represent a brand new
5:38codebase. Now, oftent times you'll be
5:40operating in an existing codebase that
5:42will look like this, right? You'll
5:43already have some pieces, right?
5:46Modules, files of your application layer
5:48and then you'll start adding on your
5:50agentic layer around your codebase. Now,
5:53what does this really look like? Let's
5:56start from a green field brand new
5:57application just to make this all clear.
6:00What exactly is in our agentic layer?
6:03This is our class one grade one agentic
6:06layer. This the thinnest possible
6:09agentic layer. You can have a small
6:10amount of code and you have a prime
6:12prompt andor memory files. It doesn't
6:15get thinner than this. If you have this,
6:18you technically have an agentic layer.
6:20Now, let's dive into a concrete codebase
6:23structure to see what this looks like.
6:24This is an example project structure.
6:26We're going to work through several of
6:27these to make it absolutely clear what
6:29it looks like to have an agentic layer
6:32surrounding your application layer. All
6:33right, so we have application layer
6:35files, folders, directories, so on and
6:37so forth. And we have our agentic layer
6:39files. If we open up dogcloud here, do
6:41commands, we can see we have this prime
6:44command. As you know, the prime command
6:45effectively represents a memory file.
6:48But the big difference here is that you
6:50can activate this whenever you want to
6:52and you can fine-tune the prime command
6:54to run any specific workflow you want
6:57to. Okay, so this is just a prompt that
6:59effectively activates your agent to read
7:01specific files, right? So there's
7:03nothing new here, right? We have a
7:04classic agentic prompt doc of course is
7:06our memory file that's always going to
7:08load. You've seen this, you understand
7:09this, you know this. And oftent times I
7:12like to take the agents.mdia from
7:13working with other agents and just
7:15reference that claw.md memory file. This
7:17is the simplest possible agentic layer.
7:19Now in our application layer, if we're
7:21going to user management where we have a
7:22backend for a user management system.
7:24This is just a starting place, right?
7:26This is a great way to when you're
7:27creating a new application, just start
7:29with something an agent can come in here
7:30and understand how to operate this right
7:32away with the memory file and with the
7:34prime command. Okay, so as we work
7:35through classes and grades, we'll talk
7:37about the compute advantage you get by
7:39scaling to this class and grade of a
7:41gentic layer. And then we'll of course
7:43talk about trade-offs. So the compute
7:44advantage here is obvious, right? We get
7:46a clean, minimal setup. It's a great
7:48foundation for agenda growth and agents
7:50understand context immediately. It's a
7:52super small code base. This is easily
7:54reproducible by anyone. There's no value
7:56in a codebase this small. It's useless
7:58for large code bases. Limited capability
8:00and there are many leverage points of
8:02aentic coding that the class one grade 1
8:04aentic layer purely misses. Let's scale
8:07this up. Now we have the class one grade
8:10two. This is where we start surrounding
8:12our codebase with more useful leverage
8:14points of agent coding. Here we
8:16incorporate specialized prompts to help
8:19us plan work. We have a prime command
8:21and we now have access to agents also
8:24known as sub aents. Here we can start
8:27incrementally improving what our
8:29codebase can do. So let's go ahead and
8:31look at what this codebase looks like
8:32now. Right, we have a new specs
8:34directory where our plans are written.
8:36You're very familiar with that pattern.
8:37We also have AI docs so our agents can
8:39pull in documentation to aid their
8:42context. This is all part of the agentic
8:44layer. And our user application is
8:45starting to grow. We have a few tests.
8:47We have modules. But the most important
8:49pieces are here. In addition to our
8:51commands, right, our raw prompts, we now
8:53have some sub aents or just some agents
8:57that we can run, right? So for instance,
8:58you might have a fetch docs sub aent
9:01that will save them in the AI docs
9:02directory and you might have a test
9:04writer. These are just simple examples
9:06of starting to scale up your agentic
9:08layer. You likely have something more
9:09powerful than this, but we're just
9:11working our way up. And now you might
9:13have something like a brand new plan
9:15file. So when you're doing work, you
9:16might prompt this plan file and then
9:18execute it. Very simple. We're still
9:20just stacking things up, slowly building
9:22up our agentic layer. This is a simple
9:24class one grid 2 agentic layer. The big
9:27advantages here is that we have
9:28specialization with sub aents and we can
9:30scale this and parallelize certain
9:33workflows. All right. Right? And now
9:34we're starting to collect documentation
9:35for our agents and we're planning our
9:38work before we implement. So this is
9:40grade two. Many engineers have passed
9:42this level. This is not that novel. It's
9:44not that interesting. We're still
9:45limited in many ways. Right? The big
9:47thing we're missing here is custom tools
9:49which brings us right to grade three. So
9:52in class one, grade three things start
9:54getting interesting and our agentic
9:55layer starts to wrap our application
9:58layer. So here we have three key new
10:00pieces. skills, MCP servers, and prime
10:04commands with tool access. You likely
10:06already know what that is. All these
10:07three things give us the same thing.
10:10Custom tools that enhance our agents
10:12core 4. Let's take a look at what this
10:14actually looks like in an agentic layer
10:16class one grade three. Now, we have a
10:18couple additional base level files. You
10:20might have a MCP.json, Postgress,
10:23Firecrawl, Jira, Notion, whatever tool
10:25you want to use, you can now throw it in
10:27here. But that's not all. We know that
10:28MCPs are very tokenheavy and we can just
10:30prompt engineer properly to get our
10:32agents the tools they need. One of the
10:34ways that that can be done is with
10:36skills. Our agents have learned to
10:38migrate the database. And so inside of
10:40this file, you know, you've seen these
10:41before. We have taught our agent how to
10:43use a specific tool and it can now use
10:46it to perform a specific task. We're
10:48starting to specialize our agents a
10:50little bit more outside of our sub
10:52agents. But we can also do things like
10:53this, right? Start and stop application.
10:55If we open up this skill, we are just
10:57teaching our agent how to use scripts.
11:00There's no need for an MCP server.
11:01Oftent times, you can just build your
11:03own script that exposes the right
11:04functionality. Here we have a start and
11:06a stop tool that starts and stops the
11:08application. Okay, so we're just scaling
11:10up what our agentic layer can do. We're
11:13giving our agents more capability,
11:15right? We are scaling our compute to
11:16scale our impact. Our agents now have
11:18access to custom tools, skills, and
11:21prime commands, right? And keep in mind,
11:22this is something that I want to keep
11:24emphasizing. Skills and MCP servers can
11:27both be replaced with just a simple
11:29prompt. Okay, so we have this prime DB
11:31with tools. You can teach your agents
11:33how to execute CLI commands. Here we're
11:35just using PSQL, right? So that our
11:38agent knows how to interact with the
11:40user management database for this mock
11:42application. And so you can bypass
11:44everything by just understanding how the
11:46core 4 works, context, model, prompt,
11:48and tools. Okay. And notice what we're
11:51doing here, right? We're constantly
11:53giving our agents more capability.
11:55Custom tools is a huge step. Grade three
11:57is very important. Now, this is when
12:00prompt engineering and context
12:01engineering becomes more important.
12:03Skills, MCP servers, and prompts must
12:06have tools carefully designed. This is
12:08where things can start going wrong. And
12:09a lot of engineers do get stuck at grade
12:11three. They think they get past this,
12:13but actually their tools are terrible
12:15and they won't scale. And they're
12:17chewing up tokens. They're burning cash
12:19and they're overengineering their tools,
12:21right? Like one of the most common
12:22problems is that engineers will have way
12:24too many tools. Being able to design and
12:26give your agents the right tools via MCP
12:29server skills and just raw prompts is a
12:33critical skill for every agentic
12:35engineer. Let's move on to grade four.
12:37How do we scale this up even further?
12:39What comes next? Right. So at this
12:41point, we start scaling up our prompts
12:43and we start building up feedback loops
12:46in the codebase. Okay, so this is where
12:48things become very powerful and where
12:50you can really scale what you can do.
12:52So, of course, we have our plan prompt,
12:54but now we'll have things like our build
12:55prompt, right? A higher order prompt.
12:57Grade four is distinct and that you
12:59start asking your agents to review their
13:02work. This is where you realize that you
13:04should always add feedback loops into
13:07your agentic layer. You're effectively
13:09adding more compute to get more
13:11confidence in your agents results. All
13:13right, this is the big idea in tactical
13:16agentic coding. Lesson five. Right here
13:18we're building closed loop prompts. All
13:20right, let's understand the application
13:21structure. So now we have things like
13:23app review. So not only are you planning
13:25your work, you're having your agents
13:26review the work done and then report it.
13:29And you're doing this with powerful
13:30prompts like the code review prompt, the
13:32review prompt. You're then running
13:34reproduce bugs and then things like test
13:36backend, test front end. All right, so
13:38for instance, what does review do? Code
13:40review current changes, review all stage
13:42changes, do a bunch of stuff, and then
13:44we're going to output some concrete
13:45results. You might also do something
13:47like reproduce a bug. This is where we
13:49would save a concrete resolution file
13:52into app reviews. This becomes really
13:54powerful when you start giving your sub
13:57agents these abilities. Okay, so we can
13:59run something like a review agent and
14:01you can scale this up and then it will
14:03output its review into a specific file.
14:06Inside of our application layer here,
14:08you'll also notice something. As you
14:10start to grow your codebase, your
14:11agentic layer and your application layer
14:13will be growing side by side. We've now
14:15split out our application into client
14:17and server. At some point, your small
14:19codebase will grow and it will start to
14:21fracture. This is important. We'll look
14:22at this in the next grade. But with this
14:25application directory structure, we're
14:27still able to keep all related code for
14:30this product under the same code base.
14:33Now, how this operates in Git is
14:35important. We'll talk about that in just
14:36a moment. Since we have both a front end
14:38and backend application, you can start
14:40specializing your prompts, right? test
14:42backend, test front end. Here we have a
14:45concrete closed loop prompt. I'm being
14:47really verbose here with my language
14:50inside of these mock prompts. You know,
14:51we're following that three-step workflow
14:53from tactical agent to coding lesson 5.
14:56Request, validate, resolve. This creates
14:59a closed loop where your agent can spin
15:01and spin until the job is done. This
15:04distinctly marks grade four. Again, this
15:07is a place where engineers fall off and
15:09they miss out on using more compute to
15:11get the job done, right? We're starting
15:13to split our prompts to focus on
15:15specific areas of this codebase. Okay,
15:17so things are going from really generic
15:19and vague, right? Plan build to more
15:22specific, right? Test our backend, test
15:24our front end, review with this code how
15:26we like to do it. All right, and the
15:28advantages here are really clear, right?
15:30Once you start using closed loop prompts
15:32in your codebase, your agents start
15:33resolving their own work. You can create
15:35specialized tasks for your front end,
15:37for your back end, for whatever part of
15:39your codebase, and it's all still on
15:41this top layer. Now, we're not going to
15:43dive into codebase organization too much
15:45here. There are some leverage points of
15:46agent coding that we're not going to
15:48re-emphasize here. Here, we're focused
15:50on giving our agents the highest
15:51capability possible so that they are as
15:54autonomous as possible. Now, there are
15:56some trade-offs here. Again, things
15:58start to get more complex. You need to
16:00know how to prompt engineer. This is why
16:02our agentic prompt engineering lesson
16:04was so important. You must know how to
16:06write these prompts properly.
16:08Self-correcting agents is a massive
16:10massive win for your agentic layer.
16:12Let's move on to grade five. How does
16:13our agentic layer continue progressing?
16:15Now, by this point u many engineers have
16:18many many prompts, many agents, several
16:21skills. They've used many different
16:23types of MCP servers and they start
16:26scaling their work even further with a
16:29key concept we discussed in lesson
16:31three.

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