**Claude Code: The Complete AI-Native SDLC Guide — Transcript & Summary | SozAI**
Source: https://sozai.app/transcript/claude-code-ai-native-sdlc-guide/

Walkthrough of Anthropic's AI-native SDLC playbook for Claude Code, covering intent files, specs, planning, testing layers, and CI/CD with AI agents.

## Key Takeaways

- AI shortens the build phase, so the value shifts to better requirements gathering, review, and release processes.
- Capturing decisions and context in an intent file (human-readable and machine-actionable) is central to the AI SDLC.
- Separating intent (why) from spec (what) and plan (how/tasks) creates a clear artifact chain from idea to implementation.
- Layered testing plus continuous evals and a shared review guide keep AI-generated code reliable.

## What the video covers

- Anthropic's AI-native SDLC playbook (14 lessons) is presented as the workflow the Anthropic team uses to build applications with Claude Code.
- Traditional SDLC runs planning, designing, building, testing, deployment, and maintenance at human speed, with building being the largest time cost.
- AI agents shrink the building phase, so the bottlenecks shift toward requirement gathering, reviewing AI-written code, and releasing products.
- In planning, AI can interview the user via skills like a 'grilling' skill, walking through decision trees until a shared understanding is reached.
- The conversation outcomes are captured in an intent MD file, readable by humans and actionable by machines, which can live in the repo or in a ticket system like Jira, Linear, or GitHub Projects.
- Anthropic provides an intent template with sections for title, problems, proposed outcomes, affected users, constraints, and open questions.
- The intent file clarifies the 'why', while the spec MD defines the 'what' (technical requirements and expected behavior); the design stage converts intent into a spec.
- A plan MD then converts intent and spec into actionable implementation tasks, with an agent working until tests pass.
- Testing is organized into three layers, with functional unit tests (e.g., Jest or Vitest) as the first layer.
- Continuous evals in CI check whether skills and model or rule changes still work as expected.

## Chapters

1. 00:00 Introduction to Anthropic's AI-Native SDLC Playbook
2. 01:27 Refining Requirements and Review for AI-Driven Development
3. 03:01 Planning Stage: Grilling Sessions and the Intent File
4. 04:26 From Intent to Spec: Design Stage
5. 06:26 Creating the Plan MD and Implementation Tasks
6. 09:16 Building and Testing: Three Testing Layers
7. 12:16 Continuous Evals, Review Guidelines, and PR Review
8. 17:18 CI/CD Integration and Deployment

Answers

## Questions about this video

What is the intent MD file and where should it be stored?

The intent MD file captures the conversation outcomes, decisions, and context from planning, including the problem, proposed outcomes, affected users, constraints, and open questions. It is readable by humans and actionable by machines. It can be stored in the repository or in a ticket system such as Jira, GitHub Projects, or Linear, and multiple intent files can exist, one per change.

How does the intent MD differ from the spec MD?

The intent MD clarifies the why, explaining why the work is being done. The spec MD defines the what, meaning the technical requirements and expected behavior. The design stage converts the intent into a proper spec, and the spec can follow Anthropic's prompt or other templates and skills.

Why does AI change the focus of the software development life cycle?

Because AI agents dramatically shorten the building stage, the remaining time costs shift to requirement gathering, reviewing AI-written code, and releasing the product. Anthropic therefore suggests refining those three areas in the AI SDLC.

What testing approach does the video recommend for AI-built applications?

The video describes three testing layers, starting with functional tests such as unit tests using frameworks like Jest or Vitest. It also mentions continuous evals in continuous integration to check whether skills and rules still work after model or rule changes.

## Full Transcript — Download SRT & Markdown

00:00

Speaker A

Recently Boris Cherny, the head of Claude Code, and the Anthropic team just released their AI-native SDLC playbook.

00:07

Speaker A

Now essentially what SDLC means is software development life cycle. Essentially the way how that works for the life cycle is it usually go through this kind of cycle here where first start with planning, designing, building and eventually testing deployment and

00:22

Speaker A

Now, essentially what SDLC means is software development life cycle. Essentially, the way that works for the life cycle is that it usually goes through this kind of cycle here, where you first start with planning, designing, building, and eventually testing, deployment, and maintaining. And after you're done maintaining, you can see it kind of circles back to planning here, and it just goes forward with a new feature or new bug. Essentially, this entire article, this entire course, which contains 14 lessons, basically contains everything that the Anthropic team actually uses this life cycle for when building applications with Claude Code.

00:36

Speaker A

basically contains everything that anthropic team actually using this life cycle here to building applications with cloud code. So that's why in this video I'm going to show exactly how this playbook works, how we can actually apply this into our own AI development

00:48

Speaker A

So that's why, in this video, I'm going to show exactly how this playbook works and how we can actually apply this to our own AI development life cycle, so we can use it to build applications with best practices. With that being said, let's not waste any time. Let's get straight into the video.

00:59

Speaker A

agents, every stage kind of runs on a human speed. So here you can see that the biggest time that it took for the traditional SDLC is the building process. But now you can see that with AI we can actually to shrink that

01:11

Speaker A

Now, essentially the way this works is that before we have AI agents, every stage runs at human speed. So here you can see that the biggest time it took for the traditional SDLC is the building process. But now you can see that with AI, we can actually shrink that building process to be a lot smaller, right? So the cycle here, entirely, you can see, is a lot shorter.

01:16

Speaker A

And because of that what Enthropy suggests us to do is to refining our requirement gathering our review process. How we can be able to better review the code that AI writes as well as how we can be able to release the

01:27

Speaker A

And because of that, what Anthropic suggests we do is refine our requirement gathering and our review process: how we can better review the code that AI writes, as well as how we can release the product that we built. So the entire process that we need to follow, you can see, is a bit more focused on those three. That's why, in this video, we're going to cover exactly how Anthropic suggests we refine those stages so that we can make our entire AI SDLC life cycle a lot better.

01:38

Speaker A

those stages so that we can to make our entire SDLC AI life cycle here to be a lot better. Okay. So let's get into the first part which is the planning stage how anthropic here actually suggest us to do our planning. Now you can see that

01:50

Speaker A

Okay, so let's get into the first part, which is the planning stage: how Anthropic suggests we do our planning. Now, you can see that in the traditional SDLC, the planning stage usually requires you to talk to stakeholders, customers, project managers, or teams, and try to write those requirements and pull everything together. But in the new world, with the new AI SDLC, you can see that AI can actually ask questions, right? It can gather all the requirements thanks to skills like the grilling skill, which can interview us, diving deep into questions across every aspect of the plan and walking through the decision trees branch by branch until the AI reaches a shared understanding between you and the AI agent.

02:06

Speaker A

can see that AI can be able to actually questions, right? Be able to gather all the requirements thanks to skills like the grooming skill which can actually help us to interview us, right? Diving deep into questions across every aspect

02:17

Speaker A

Now, once we have the AI agent fully collected our context, the next thing Anthropic suggests is to capture those conversations, the decisions, and the context into a single intent MD file. This file is readable by humans and also actionable by the machine. One thing I want to quickly clarify is that the intent MD file is not limited to just one per project. You can have multiple intent MD files, one per change, right, in a single folder called the intent folder.

02:29

Speaker A

thing that anthropic suggests to do here is to capture that conversations the decisions the context into a single intent MD file and this file here is readable by human is also actionable by the machine. Now one thing that I want

02:44

Speaker A

The other thing I want to clarify is that the intent MD file doesn't have to be inside the repository. Let's say you have Jira tickets, or GitHub projects, or a Linear ticket system. You can put all the source of truth in the ticket itself. You don't have to store the static file inside the repo. You can also store that intent inside the ticket system.

02:56

Speaker A

thing that I want to clarify here is that the intent MD file here doesn't have to be inside of the repository.

03:01

Speaker A

And you can see here that Anthropic actually created a template for the intent file, so you can see what it looks like. It starts with the intent, the title for the intent, and what the problems are. What are the proposed outcomes, the affected users, the constraints, and the open questions? I took that same template and created a prompt that you can screenshot and follow. The only thing you need to change is the location where you want to store the intent file. It could be locally, or it could be in Jira or any other MCP tool.

03:13

Speaker A

static file here inside the repo. You can also store that intent inside the ticket system as well. And you can see here that Enthropic here actually created us a template for the intent ID of what it actually looks like. So you

03:25

Speaker A

So after you finish your grilling sessions, you can paste this prompt, or you can create a skill or a command based on it and trigger it at the end of the grilling sessions. That way you have an intent MD file, or an intent section, somewhere in your project.

03:37

Speaker A

a prompt here that you guys can be able to take a screenshot and follow. And the only thing that you need to change here is the location of where you actually want to store the intend file. It could

03:45

Speaker A

Pretty much after we have our intent MD file, we have the artifact chain. We take that intent MD file and pass it to the design stage, and the design stage converts everything in the intent into a proper spec. Now you may be wondering, what's the difference between the intent MD and the spec MD? The clear difference is that the intent MD clarifies the why, why we do this, and the spec MD defines exactly the what: the technical requirements and the expected behavior. Based on the article, Anthropic gives us a clear prompt that we can follow to convert our intent MD file into a proper spec.

03:57

Speaker A

based on this and trigger this at the end of the actual grilling sessions. So you can be able to have a intend MD file or intent section here inside somewhere in your project. Now pretty much after we have our intend MD file, we pretty

04:10

Speaker A

You don't have to follow this exact prompt to convert your intent to a spec. You can also use skills like the MAP-PoKE skills or the superpowers skills we've talked about on this channel, or any skills you prefer from your organization, or your own templates or formats, to convert any intent into a proper spec.

04:22

Speaker A

wondering okay what is the difference between intent MD and the spec MD. Well the clear difference is that the intent MD here clarifies the why why we do this and then the spec MD here defines exactly the what the technical

04:34

Speaker A

Before we continue, I recently launched our school community. We're helping people master AI agents, automations, and building real AI products. This is coming from someone who used to work as a senior AI software engineer at companies like Amazon and Microsoft. In this community, you're going to get a full AI builder roadmap, where you start with automations, then move into AI agents, research systems, SaaS building, and eventually how to productize and market your AI skills. You'll get templates, workflows, weekly live calls, and direct community support, so when you get stuck, you can ask questions and get help right away. The goal isn't just to watch more videos. It's to actually build, post your progress, get feedback, and level up. If you're interested, jump right in, start the first course, and I'll see you in the community.

04:47

Speaker A

don't have to follow this prompt here to actually converting your intent to spec. You can also using your skills like map poke skills or superpower skills that we talk about on this channel. Any skills that you prefer from your organizations

04:58

Speaker A

Okay, so far we've talked about how we can create our plan, brainstorming it and creating an intent MD file, and then how we can create our design, what kind of system spec we're going to have, which is our spec MD. The next thing we're going to do is create our plan MD. This plan MD takes everything we've talked about before, our intent and our spec, and converts that into actionable tasks that the AI agent can follow and complete either in parallel or step by step. So let's take a look at how we can do that.

05:13

Speaker A

coming from someone who used to work as a senior AI software engineer at companies like Amazon and Microsoft. And in this community, you are going to get full AI builder road map where you're going to start with an automations then

05:24

Speaker A

In terms of the plan MD file, there's no one-size-fits-all. This is the template Anthropic has given us for the plan MD file. You can see the template format: we have a plan title, the files that are going to be changed and exactly where they are, and the order of the work. What does it do first, what does it do second? Then there are the risks we need to consider, and how we prove the work is actually finished. That could be a test file that tests certain areas of the code to show that the actual

05:39

Speaker A

get help right away. And the goal here is not just to watch more videos. It's actually build, post your progress, get feedback, and start to level up. So if you're interested, make sure to jump right in, start the first course, and

05:50

Speaker A

I'll see you in a community. Okay. So, so far we talked about how we can be able to creating our plan, right?

05:56

Speaker A

basically brainstorming it and creating a intent MD file and then eventually how we can be able to create our design right what kind of system spec we're going to have and that's going to be our spec MD and the next thing we're going

06:06

Speaker A

to do here is we're going to creating our planned MD and this plan MD here takes everything that we have talked about before right our intent and also our spec and basically try to convert that into a actionable task that AI

06:18

Speaker A

agent here can follow and try to do them either in parallel or step by step so in this case let's take a look at how we can do Now in terms of planned MD file, there's no one sizefits all and you can see that

06:30

Speaker A

this is the template that Enthropic has given us for the planned MD file. What's the template format look like? Right? So you can see that we have a plan title.

06:38

Speaker A

We have the files that's going to be changed exactly where that is and what's the order of the work, right? What does it do first? What does it do second? And then what are the risks we need to

06:48

Speaker A

consider and how we prove that the work is actually finished, right? That could be a test file here that basically tests a certain areas of the code and to show that this actually works. So you can see that these are the things these are list

07:02

Speaker A

of sections that we have inside of our plan MD file that Enthropic has suggested us. And here you can see that this is the prompt that Enthropic here has created for us so that we can be able to take the intent MD file as well

07:14

Speaker A

as the spec that we designed from the previous two stages and try to apply for all the skills and convert everything into a proper planned MD file that we have. Now, obviously, like I said, there's no one size fit all, right? So,

07:27

Speaker A

if you are using like two ticket skills from Matt PCO skills or if you're using the superpower writing plan skill, which honestly those are both basically taking the spec, right? And try to convert everything into a proper plan. And you

07:40

Speaker A

can see that this is the plan, right? So, basically what it does here is it takes everything and it kind of just converts it, you know, you can see here that it has its own way of doing implementation plan. So it has goal

07:49

Speaker A

architecture text stack and where's the spec the global con constraints and then task structure you can see that every task has a file of where you change has an interface has a kind of like a checkbox on what to do first and what to

08:03

Speaker A

do second right so if you're already using those kind of specd driven development framework for how you can be able to converting spec into actual plan that agent here can follow then you can honestly do that right and always try to

08:15

Speaker A

use what you already have that's working and try to use this acceler like a reference to see what are the things that you can actually to add into your SDLC to make it even better. Right?

08:26

Speaker A

Don't take everything that we have in this video and try to use what you're already working and see what are some areas that you can refine with your current development cycle and see about that. Right? So that's basically the

08:37

Speaker A

building process is basically try to convert that into a full plan before we're actually going to do the implementation. But one thing to note here is that before we actually do implementation, we also need to focusing on writing tests first. Focusing on

08:50

Speaker A

testdriven development here and that's what also enthropic here has also recommend. Now in terms of testing here, like I said before, Enthropic here wants to you to use testdriven developments which essentially what it does here is that before you write the code, we want

09:04

Speaker A

to create the test first, right? We wants to create the check first before it actually creates it. So we want to create this kind of feedback loop so that while the tests are failing then we want to have AI here to kind of fix the

09:17

Speaker A

code that it writes until the tests are passing. So here exactly what it says.

09:22

Speaker A

So give claw a feedback loop. Always give claw a way to verify his own work whether it's tests or a build. Maybe it could be linting or a screenshot. And the session here checks his own work fix his own mistakes before us have to see

09:36

Speaker A

them. Okay. Okay, so we want to kind of delegate it more to the automation testings before we have a human here to kind of step in and try to kind of verify the changes. Now, speaking of test-driven developments, there's many

09:49

Speaker A

skills that we have reviewed on the channel, like for example, the Matt Poc test-driven development skills where you can actually move to focusing on writing test first before you do the implementation. And then we also have our superpower testdriven development

10:01

Speaker A

skill which also does very very similar stuff. But you can see that this is the anthropic version of test-driven development here. And essentially you can see that we have our test, we have our linting, we have our build. Now

10:12

Speaker A

build here is not coding is actually just try to do a build to see if it actually really compiles. Right? So those are the things that I actually check to see if it able to see if the software is runnable. See if there's any

10:24

Speaker A

um outputs that are like not making any sense to see if the function or the program here is actually behaving in the right behavior. Right? Those are the three things that it checks. And you can see that it says do not report done

10:36

Speaker A

until all three are green. So essentially what it does here is that don't try to report done while those three are not passing. So that's exactly how this works. Before we jump into the next part, I've actually been looking

10:48

Speaker A

around for a course that goes a little deeper into AI agents because there's a big difference between using an agent and actually understanding how to build one. Then I came across the John's Hopkins University certificate program in Agentic AI from Great Learning. It's

11:02

Speaker A

18 weeks, it's online, and it's about agentic rag and multi-agent systems in Python. And what I like about the approach is that it starts with the actual foundations. So you're working with Python, LLM, API, tool use, and then building your way into more

11:16

Speaker A

advanced agent workflows. It's also taught by John's Hopkins University faculty who've led AI teams at Fortune 500 companies. So you finish with a certificate of completion and 13 CUS from John's Hopkins University. There's also a dedicated module and master class

11:32

Speaker A

on Claude models and constitutional AI where you work with the claude API, structured outputs, tool use, and MCP.

11:39

Speaker A

So the focus isn't just on using Claude to run an existing agent. It's on using Claude as part of the system you're actually building. And then you take those concepts into three projects.

11:49

Speaker A

starting with a single agent workflow, moving into an autonomous financial research agent, and eventually building a multi- aent system where specialized agents coordinate with each other for something like mortgage underwriting.

12:01

Speaker A

So, if you're trying to go beyond just prompting agents and actually understand how these systems are designed, built, and evaluated, this is the kind of workflow the program is focused on. I'll leave the course link in the description

12:12

Speaker A

if you want to check out the curriculum and projects. All right, let's get back into it. All right, so now you know that testings are important. The next thing that we're going to take a look at is how we can using the right tools for our

12:22

Speaker A

testing. And for that I actually break down into three layers. Now the first layer that we have here is functional tests. Right? Writing unit tests. So here you can see that the framework that we can use here is Jess or Vest. And

12:34

Speaker A

essentially what we're testing here is try to test a function. Let's say we have a function here to do a computation to do a endpoint call try to fetching some information. Well can we test that function? Yes, we can do that by using

12:46

Speaker A

just or test. And once we have our function tests are all covered, the next layer that we're going to move on to is the component testing. Now let's say if you have a UI for application, it could be a mobile app, it could be a web

12:58

Speaker A

applications, we can use a react testing library here to test this UI component and UI component here could covers like buttons here, could be forms, could be whatever that user can look at and be able to interact with. Okay. And the

13:11

Speaker A

relationship here is that one components could have many function calls, right? But functions don't call UI components.

13:19

Speaker A

So that's basically the relationships, the dependencies there. So that's why we want to make sure that we create testing for function first, right? Those bottom layers and then we can go to writing tests for our UI components. And after

13:32

Speaker A

we have our UI components covered, the last part that we can do here is we can do the end to end testing which will basically simulates the entire user flow. Let's say we have different pages.

13:42

Speaker A

We want to have user here to navigate through and we can be using tools like Cyprus or Playright which are both popular end to end frameworks that we can simulate to test out the entire pages. Okay. So you can see that those

13:54

Speaker A

are all the three layers that we have in terms of testing frameworks for our applications. Okay. So once we have set up the testing, the next thing to look at here is the continuous evals in the continuous integration. And essentially

14:05

Speaker A

the way how this works is we're going to have AI here to set up the evaluations based on the skills that we run. Maybe it could be the test-driven development skills that we just ran. What we want to

14:14

Speaker A

do here is that we want to make sure that we set up the eval skill. And why do we do this? Well, because after you swap the different models, there could be a situation where this new model here

14:23

Speaker A

could be overlooking some things that is already working. And we want to know exactly what's working so far. So enthropic has recommend to taking in 20 to 50 real task that you have already have claw here completed and use that as

14:36

Speaker A

the evaluation for the new model. So it could be a pull request, it could be a GitHub issues that you keep track and let AI here to read through that and using the skill like skill creator to set up the eval and make sure that it

14:49

Speaker A

runs the test to see if it actually able to do this for the new model that we swap to. And here you can see that and has mentioned just write a task as the eval and the prompt plus the expected

14:58

Speaker A

outputs right and then here you can see that it also suggested we can be run it in a continuous integration which means that it could be run it inside of your uh CI/CD pipeline right so here below you can see it creates a GitHub workflow

15:10

Speaker A

which is a agent eval to evaluate to see if the skill here still works. So you can see having a CI/CD pipeline that runs the skill check to making sure that everything is working and evaluate it right so let's say if you're making a

15:23

Speaker A

change for the model or making a change for a rules well does the skill still work with that right so you can see a workflow like this that has those things in place it's going to check for it

15:34

Speaker A

right so for each eval here you can see it runs clock here headless and it using all the tools right these are allow tools that we have and it's going to see if the model here or if the rules rules

15:44

Speaker A

change is actually able to satisfy the eval that we set. Now once you have the eval in place the next thing that Enthropy recommend is basically have AI here to do the pull request review and specifically the takeaway that I got

15:57

Speaker A

from this is that you can be able to have claw here basically write a single review the MD file to see exactly what are the things that we need to filter out right so here you can see it have a

16:06

Speaker A

review instructions so pass the three tests and tag each findings which is passed right so any bugs any securities any compliance and what are the important means here caps and nits and also do not report right so and not

16:18

Speaker A

report generative files under the source gen or any things that are kind of like a temporary files here that can also be ignored and specifically you can see that it mentions that we can be able to have a human right it could be your team

16:29

Speaker A

tech lead and basically writes a review MD to see exactly what is the SOP for reviewing things right reviewing pull request and have AI here to follow that reviewed MD file and try to review the things that it writes right so it could

16:42

Speaker A

be the best practice now my recommendation is that have AI here to take the past pull request that has run and you usually have human here to give comments that pull request and let it look at those comments. let it look at

16:53

Speaker A

what are some common patterns that I see that we can probably add it into review MD that every time when it looks at a new pull request well this is exactly what it going to look at before it's

17:02

Speaker A

going to tag or be able to give a comment but obviously having the review process here is not enough we can also adding hooks here to also approve things before we have AI here to merge the pull request into the actual production and

17:15

Speaker A

essentially what we can do with hooks here is that whenever AI is trying to do something right it could be like okay well try to touch this production database Or it could be like oh try to merge the pull request something like

17:26

Speaker A

that. Well there's something that we can do here which is adding hook to block any certain actions the AI is trying to do. It could be that whenever it tries to do a fix like for example the reviewer here the reviewer agents sends

17:37

Speaker A

some comments to the builder agents and let the builder agent here to fix a certain things in the pull request.

17:43

Speaker A

Well, we can have a builder here to basically write a llinter to make sure that okay, everything's formatted nicely. testing are fully passed or let's say if it's actually going to write some credentials into the codebase well prevent that right we can use a

17:55

Speaker A

hooked here to prevent those things or maybe there's certain files that we don't want AI here to edit or fix then we can also prevent that as well right we can using hooks here to actually adding this guard rail to making sure

18:06

Speaker A

that it doesn't have anything uh bugs or sensitive data here pushed to productions which is what anthra here has recommend now let's say we have AI here fully review the pull request and eventually merge the pull request the

18:17

Speaker A

next thing we want to talk about here is the CI SED integration deployment how does it actually going to deploy the changes that is going to wrote into the production. Now essentially we have two parts right so before deployments and

18:28

Speaker A

after deployments and for before deployment here essentially the workflow basically calls claw headlessly and try to have claw here to look through the entire bill lock and see why it failed and try to create a pull request and

18:40

Speaker A

submit for review and it's going to go through that deployment process that we just talked about and make sure that it's able to fix the build and it's going to release the feature to production and once that feature

18:51

Speaker A

released to production the next thing we're going to focus on here is the after deployment Right. So after deployment here, let's say what we're going to do here is that we're going to have claw here to call a status tool. So

19:00

Speaker A

this status tool here could be data dog, could be sentry, whatever system performance tracking tools you have and have claw here to look at it to see if there's any dramatic things that are breaking, right? Let's say if there's

19:13

Speaker A

something breaking, the system is breaking after this feature release. We can have claw here to roll back, right?

19:18

Speaker A

I give claw the tool here to roll back the feature and have it to find the root cause, create a pull request and go through that process, deployment process again. Okay, so that's basically what anthropic here has recommend in terms of

19:30

Speaker A

deployment process with cloud. So pretty much so far we have talked about here is planning designing building testings and finally deployment so far. Right? So the last step we're going to take a look at is maintaining parts. So how can you

19:43

Speaker A

have claw here to maintain the system after the feature has released? Well, that basically leads us to the last lesson, which is basically the closing loop on metrics. Now, what do I mean by metrics? Well, metrics, like I said

19:54

Speaker A

before, it could be centuries, data dog, whatever system performance tracking tool you have. And we can be able to periodically, like, for example, here you can see it creates a sample YAML file and it looks at the past 30 days.

20:07

Speaker A

And you can have claw here to look at the logs to look at everything to see if there's anything that we can improve in our system. It could be some errors that are keep happening. It could be a data

20:15

Speaker A

query that keep getting really slow or it could be something that user turning from the application, right? And whatever the problem is, we can have claw circle back by creating this intend MD file and have claw here to go through

20:26

Speaker A

this entire process, this SDLC process again and try to improve the application. All right, so pretty much that's it for this video on enthropic SDLC. And you can see that we can use AI here to pretty much automate every stage

20:38

Speaker A

that we have for our software development life cycle. So, if you do find this video, please make sure to like this video and of course, if you're interested for more content like this, make sure to subscribe. So, with that

20:48

Speaker A

being said, I'll see you in the next video.

Topics: Claude Code AI SDLC Anthropic intent file spec driven development AI agents software development life cycle testing CI/CD code review

Study this video

- [Flashcards from this video](https://sozai.app/tools/ai-flashcard-generator/?from=transcript&id=86582)
- [Quiz on this video](https://sozai.app/tools/quiz-generator/?from=transcript&id=86582)

Free, made by AI from this transcript. No signup.


---
This is the markdown twin of https://sozai.app/transcript/claude-code-ai-native-sdlc-guide/ — the same content, without the markup.
Published by SozAI (https://sozai.app). Reuse and quotation are allowed with attribution and a link back.
Machine-readable index: https://sozai.app/llms.txt · data API: https://sozai.app/api/