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AI Engineer Europe 2026

Comprehend First, Code Later: The AI Skill I Rely On Daily

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Comprehend First, Code Later

Priscila Andre de Oliveira uses AI to recover context in Sentry’s changing codebase, turning repeated questions into a skill that helps her understand before she authorizes changes.

From a talk by Priscila Andre de Oliveira

Before you start: Familiarity with Git repositories, pull requests and automated tests will help you follow the repository example.

Managing agents across three monitors

What changes when a software engineer starts managing agents instead of writing the code herself? Priscila Andre de Oliveira introduces her daily work at Sentry through that change of role. A Brazilian living in Vienna, a parent, a Verdaccio npm registry maintainer and a ViennaJS co-organizer, she has given herself a promotion from senior software engineer to “agent manager”—without a salary increase. Her colleague Dominic Dorfmeister captured the arrangement in a photograph: Priscila at her desk, managing agents across three monitors.

Slide showing Priscila seated at a desk surrounded by monitors, under the heading “Agent Manager @ Sentry.”
“Agent Manager @ Sentry”: a desk with multiple monitors.

She reports that since December 2025, she has worked by prompting rather than coding directly. Even the presentation was created by a skill. Her contributions with Claude include bug fixes, features, refactors and changes across repositories. Generating code has become part of the workflow; responsibility for what enters the codebase remains hers.

0:280:43
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The codebase that pays the salary

Sentry’s scope makes that responsibility concrete. Known for error and performance monitoring, it has expanded into observability, including metrics, profiling and tools for monitoring agent applications. Priscila describes a company founded in 2010, with more than 15 years of code, around 400 employees worldwide and 100,000 organizations depending on it. This is also the codebase that pays her salary: keeping it working is a direct, personal reason to care about what agents produce.

“This is Sentry” slide listing monitoring capabilities, with figures for founding year, years of code, organizations and employees.
Sentry’s monitoring tools and organizational scale.

That concern has not stopped experimentation. A Sentry hackathon gave employees several days to get familiar with AI and try new projects. The resulting tools address different parts of everyday engineering:

  • Abacos: tracks internal AI usage.
  • Warden: reviews code in pull requests.
  • Junior: can be triggered in Slack when a thread raises a bug or questions a UI change. Priscila describes it analyzing the thread and creating a corrective PR.

The useful starting point for Junior is the conversation people are already having: a complaint, an unexpected change or a bug report supplies context for an investigation.

The AI SDK integration testing repository predates that hackathon. It contains tests for Sentry’s AI integrations, and it gave Priscila a more demanding introduction to agent-assisted work: her team asked her to contribute only through prompts, continuing until she obtained a satisfactory result. These tools became part of daily internal use, rather than remaining hackathon experiments.

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Keeping up with a moving codebase

AI adoption sits alongside deliberate maintenance. Priscila reports that Sentry devoted a three-month quality quarter in the preceding year to improving its codebases. The work included removing TypeScript any types, addressing accumulated TODO comments, simplifying code and removing unused feature flags. These are concrete ways to reduce technical debt and keep the code understandable as it evolves.

Priscila reports about 100 merged PRs per day, with contributions across four offices and from outside contributors to Sentry’s fair-source codebase. Components are deprecated, replacements appear and lint rules change. Even after more than six years at Sentry, she can return from vacation to a PR full of conflicts. Resolving those conflicts requires more than reconciling lines: she needs to catch up with the conventions and decisions that changed while she was away.

Understanding is therefore a daily engineering task. Priscila cites an unspecified study estimate of 70% of developer time spent reading and navigating code; that figure depends on the study and its definition of comprehension. Her practical point is that engineers already spend substantial effort understanding existing systems. AI offers a way to recover that understanding faster.

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Recover context before directing a change

Telling an agent to explore a repository and then make a change leaves an important gap: the agent may have misunderstood what it found. The engineer needs enough understanding to steer the investigation. Otherwise, a plausible explanation can carry a mistaken assumption straight into implementation.

Priscila’s examples concern recovering context that previously required manual investigation or waiting for another person:

QuestionPrevious routeAI-assisted route
Where did this regression begin?Open GitHub and follow git blameAsk for the regression’s history
Why did this product behavior change?Ask a colleague in SlackAsk for the decision context

For incident investigation, she says a short prompt can now return context in seconds. For product questions, a colleague in another time zone might previously have replied the next day. These are examples from her own workflow: the productivity gain she emphasizes is reaching an informed understanding sooner.

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Turn recurring questions into a skill

As the same questions recurred, Priscila asked Claude to analyze her local cache of 116 sessions. It classified her activity into comprehension, modification, process, review, generation and other. In that personal analysis, she reports 67% comprehension and 2% code generation. The percentages describe her classified usage; she does not specify the denominator or classification procedure, so they should not be read as shares of working time.

Those repeated prompts became Catch Me Up, a skill stored locally on her computer that she could share if she wished. The slide groups its exploration areas into Architecture, Convention, Feature trace, Syntax / API, Testing and History. The command /catch-me-up gives her a reusable entry point for questions about structure, behavior, tests and past decisions.

“So I built a skill for that” slide showing /catch-me-up and six areas: Architecture, Convention, Feature trace, Syntax / API, Testing and History.
The /catch-me-up skill organizes six areas of exploration.

The packaging is simple: a Markdown file containing a detailed natural-language prompt with clear goals. Because much of Priscila’s work is on the front end, she favors visual explanations. The skill asks for structures she can inspect—an organizational diagram, a flow or a table—instead of relying only on paragraphs. A compact instruction file following that approach could look like this:

markdown

# Catch Me Up

Help me understand the repository before proposing changes.

Explore the area relevant to my question:
- Architecture
- Convention
- Feature trace
- Syntax / API
- Testing
- History

Explain the relevant structure with a flow or table.
Answer my specific question directly.

The reusable part is the combination of an exploration goal and an explanation format that helps the person understand the result.

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Does the test simulate an envelope?

The demonstration returns to the unfamiliar AI SDK testing repository where Priscila had been asked to contribute only through prompts. She shows a prepared run, avoiding the risk of a live run failing during the presentation. Using Claude Opus, she asks for orientation as a new contributor and adds a concrete question: does the repository simulate a Sentry envelope and intercept it during tests?

The output provides a summary and a flow explaining how the repository works. It also corrects the premise of her question: the prepared answer says the tests intercept real envelopes rather than simulate them. The answer begins describing a spawned collector, but Priscila does not walk through its remaining details. The important distinction is where the test data comes from: a simulated envelope would be constructed for the test, while this answer describes capturing a real one. That is the prepared demo’s explanation of the repository, not a claim that its current revision has been independently checked.

This is useful because the output changes what she understands before she contributes. Without that assistance, she would need to establish the same facts herself. She distinguishes experimenting on a vibe-coded project from shipping code at work that she does not understand. The skill also helps with PR review: knowing the surrounding project is not always enough to approve a particular change, so she uses it to recover the missing context before making that decision.

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Put comprehension between research and planning

In Vibe Coding Our Way to Disaster, Jake Nations draws on Rich Hickey’s Simple Made Easy philosophy. Priscila describes the proposed workflow as research, planning and implementation—a familiar division for anyone using Claude Code’s planning mode. She agrees with those phases but adds an explicit human checkpoint: understand the research before authorizing the plan.

The resulting procedure is straightforward:

  1. Research: have the agent explore the relevant code and context.
  2. Comprehend: read the findings and establish your own understanding. Redirect a mistaken interpretation or request more investigation where context is missing.
  3. Plan: once the understanding is sound, ask the agent to propose the change.
  4. Implement: proceed with the agreed direction.

The comprehension step can send the work back into research. Its purpose is to make sure the person directing the agent can recognize when the investigation has gone the wrong way, before that direction becomes code.

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Own the mental model

Armin Ronacher, Flask’s creator and a former Sentry colleague, raises the same concern in The Final Bottleneck: people are reporting that they no longer know what code is in their own codebases. Priscila’s response is to make comprehension the central benefit of AI assistance in a large codebase. Faster generation does not remove the need for someone to understand the result.

Her usage analysis surprised her, and she invites others to inspect their own patterns. Recurring questions may reveal where a reusable skill would help most. AI can be a tireless teammate for those questions, including the ones an engineer might hesitate to ask another person. Use that availability to align your mental model before prompting for changes—and protect the codebase that pays your salary.

She closes with “ship kino code,” her counterpoint to shipping slop, and discloses that the talk is sponsored by Sentry. The audience invitation was to visit Sentry’s downstairs booth and scan a QR code for a three-month trial of its business plan.

14:5615:09
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Resources

From the talk

  • Jake Nations discusses deliberate research and implementation planning through Rich Hickey's ideas about simplicity and thoughtful design.

  • The Final BottleneckArticle15:09

    Armin Ronacher examines review bottlenecks, codebase understanding and responsibility as AI accelerates code creation.

  • Sentry's public repository for testing Python and JavaScript AI integrations, with setup instructions and a documented assessment pipeline.

  • Agent-based code review with a local CLI and GitHub Action for pull requests. The repository provides current setup instructions.

  • A Slack bot runtime for investigating issues, summarizing context and taking actions through connected tools.

Read the complete timestamped transcript
  1. 0:00

    [upbeat music] Hello, everyone.

  2. 0:15

    Uh, today I'm gonna share with you how I use AI at Sentry, and the skill I use the most in my day-to-day work. Um, so but before we dive into that, let me tell you who I am.

  3. 0:28

    Um, my name is Priscila. I'm a [REDACTED:origin] based in Vienna, Austria. I'm a mom of a [REDACTED:age], very energetic toddler. Uh, I'm a maintainer of Verdaccio, uh, an open source npm registry.

  4. 0:43

    I'm a co-orga- co-organizer of ViennaJS, a very traditional meetup in Vienna, and we talk all about, uh, JavaScript. And, uh, I'm, I'm a senior software engineer at Sentry. Yesterday, someone told me that I don't look like an, a, a, a software engineer, but guess what?

  5. 1:00

    I am. [laughs] Um, so my, my title, my official title is senior software engineer, but I have given me a little promotion, and I am now an agent manager. Um, no salary raise, but at least my reports, they don't complain.

  6. 1:20

    Yes. Uh, this was me at work, uh, a few wee- a few weeks ago. Uh, my colleague, uh, Dominic Dorfmeister, found it funny to see me managing a couple of agents, uh, and took this picture.

  7. 1:33

    Um, yeah, luckily, I have, uh, three monitors, so that works pretty well.

  8. 1:40

    Um, this is my new reality. So the indust... Uh, sorry. Uh, this is how I feel actually orchestrating a bunch of agents. Uh, yeah, it's, it's weird, but it's fun.

  9. 1:55

    And the industry is changing, right? That's why you all are here, and I'm also adapting. Uh, since last year, since December, uh, two thousand and twenty-five, I haven't coded anymore.

  10. 2:09

    I'm only prompting. Yes. Um, and even this presentation was created by a skill. I have... Yeah, I didn't do anything. Uh. [laughs]

  11. 2:23

    Um, so as you can see, these are some of my recent contributions to Sentry, uh, and I created a, a few PRs together with my favorite teammate, Claude. And it's not just, uh, bug fixes, you know.

  12. 2:39

    It's also features, refactors, cross repositories contributions, so it's real. It's working. Um,

  13. 2:52

    so, and this is Sentry. Um, maybe you don't know Sentry, but we are very well known for error and performance monitoring. But we ha- we have grown into a, a full observability platform.

  14. 3:04

    So we have error monitoring, we have metrics, we have profiling. We have also, uh, agentic, uh, tools, like, uh, to monitor your agentic platforms. Um, yes, uh, the code base is very complex and, uh, it was founded in two thousand and ten.

  15. 3:23

    It has fifteen plus years of code. We got around, uh, four hundred employees around the globe. We have one hundred K organizations depending on this code base working every day.

  16. 3:37

    And as employee, I also depend on this code base working because I get my salary from it, right? So I don't want it to just, uh, ship slop code.

  17. 3:45

    Um, yeah, so it's a serious business. And we vibe code as well at Sentry. Uh, recently, we had a hackathon, uh, where we could... We, we had a few days to just, uh, get ourselves familiar with AI and try out new things, and a lot of good projects came out of this, uh, week, this hackathon project.

  18. 4:11

    And, uh, we have, for example, Abacos. This was created to track, uh, the usage of AI internally at Sentry. Uh, we have Warden. This is a, a code review agent.

  19. 4:24

    Uh, you can have it in your PRs. Um, we have Junior. Junior is a bot we have in our Slack. Uh, and because usually people, they say like, uh, "Oh, um,

  20. 4:37

    I don't like this UI. Something changed. Uh, can you go fix it?" Or, uh, "Why this was changed?" They like to, to go and share something, maybe some bug they found in Slack.

  21. 4:48

    And then you can-- we can just trigger Junior, and Junior can analyze that thread and create APR and already fix the bug. And people are having a lot of fun with Junior.

  22. 4:58

    Daniel can... Yeah, he loves that. [laughs] And, uh, there is also this AI SDK testing repository. Uh, this was created before this hackathon, uh, but this is basically a repository where we create tests to...

  23. 5:14

    for our AI integrations. So and this was really weird because I started contributing to this repository, and, um, uh, my team told me I shouldn't code at all. I should only prompt until I would get a nice result.

  24. 5:31

    So it was a, like a, a different experience. Uh, but yeah, it's working.

  25. 5:37

    We are using all of these tools like internally every day. [laughs]

  26. 5:43

    And yeah, at Sentry, we are going all in AI.

  27. 5:48

    But quality still matters. Um, so, uh, also at Sentry last year, uh, during three months, it's li- uh, we- We use this time like a quality quarter. We use this time just to improve our code bases.

  28. 6:05

    So remove all the any types from TypeScript, for example, or all the to-do, because usually... I don't know if you guys have the same, but we had a lot of to-do, uh, do something else at certain time.

  29. 6:17

    And, uh, so we use this time to simplify code, and have, uh, remove unused feature flags, and really have always our, our code base in a, in a good shape.

  30. 6:29

    And this is very important, right? We wanted that. This is, uh, called, uh, technical debt.

  31. 6:38

    Um, so, and, uh, as I told you before, the code base, the Sentry code base is very complex, and it's a moving target. We have about 100 PRs, uh, merged every day.

  32. 6:53

    We have four offices. Uh, Sentry is fair source, so we... You can contribute to Sentry. We have also contributors. Um, and we are all the time deprecating components, uh, adding new components, uh, adding new lint rules.

  33. 7:08

    Uh, I don't know, you name it. Like, all the time something happens at Sentry. I'm there over six years now, and I can go on vacation, I come back, and maybe my PR, uh, is full of conflicts, and I have to solve those conflicts.

  34. 7:23

    And I really need to understand. Ev- like all the time, I need to align and, and understand something. It's a daily practice.

  35. 7:35

    So, and this is not new, right? Like, uh, you guys know, uh, there are studies behind it. Like 70% of a, uh, developer time is spending reading and navigating code, and et cetera.

  36. 7:49

    Uh, this hasn't changed, you know? But, uh, now we have a very smart tool which can help us to understand faster, and that's how I'm using AI.

  37. 8:01

    So, um, I'm using AI a lot to understand, to explore and understand. And maybe you, y- you, you may think like, uh, you just tell AI to go explore the code base, and after do something.

  38. 8:15

    But, like, maybe AI understood, uh, not the, the correct thing, under- understood wrongly, you know? And you need to also understand, because maybe you need to steer the AI to go on the correct path, you know?

  39. 8:30

    And yeah. So this is how I'm using AI. Uh, it made me faster, but not the way you think. May- because like before, um, let's say, um, an incident happened, and I would have to track that down.

  40. 8:50

    I would have to open APR... uh, uh, open GitHub, sorry. Uh, go git blame, and then trying to understand, like, where the regression happened. And now I can just, uh, prompt a simple phrase, and I have it in a few seconds.

  41. 9:05

    Or before maybe a product de- product decision. Um, why this changed? And then I would, I don't know, ch- uh, ask this question in Slack. Maybe my colleague is in another t- another country, another time zone, and I would need to wait for that answer in the next day.

  42. 9:24

    And now I can just, uh, ask AI, and I have it. So it's been really useful, and this made me really productive. Uh, but, but like, the understanding part of it, like, yeah.

  43. 9:37

    And my prompts, they kept repeating. Uh, so I had this idea to let, to, to let, uh, Claude analyze my cache and see, uh... So it analyzed it, uh, 116 sessions, and it classified everything in six categories: comprehension, modification, process, review, generation, and other.

  44. 9:59

    And, um, guess what? Uh, this im- impressed even me.

  45. 10:07

    Uh, oops. It's fine. So 67% of my AI usage was, uh, comprehension, and only 2%, uh, code generation.

  46. 10:23

    Uh, so this... I was very surprised. [laughs] So because my prompts kept repeating, repeating, I created a skill for me. Uh, this skill, it's locally in my computer.

  47. 10:39

    I could share it with someone if I wish. But I use... This is for me here, and it's called Catch Me Up. Um, that structures those comprehension questions into six exploration modes: architecture, convention, feature, trace, syntax, testing, and history.

  48. 11:01

    So a skill is just a very detailed prompt, right? With some very clear goals. Uh, I can actually do like this here. Uh, you can see how, how is it.

  49. 11:14

    But it's, it's just like it's a, an MD file, uh, with, uh, human language. And I am a very visual person. I work at Sentry a lot on the front end part of it, and I like to see things to understand.

  50. 11:28

    So, um, this skill brings me like, uh, the organogram, like, uh, the structure, uh, a table for me to understand. I think it helps a lot. And, uh, I can now give you a short demo.

  51. 11:46

    Um, just a minute. Um, by the way, this is my presentation running. [laughs]

  52. 12:05

    Oh, it's here, actually, here. Um, I already run this, uh, skill because I don't know, maybe I would have some issues. But,

  53. 12:15

    uh, do you remember that project I told to you that I should only prompt and, uh, don't code anymore? So in the beginning, I was not familiar with that project.

  54. 12:26

    It was a new repository for me, and I, I used my skill for that. I said, "I am a new..." Uh, can you guys see this well? Yeah. So I said, "I am a new contributor.

  55. 12:39

    Catch me up on how this repository works, and clarify whether it simulates a Sentry envelope and intercepts it during tests." Um, I am using Claude, as you can see, Opus.

  56. 12:52

    Uh, and it gave h- it gave me here a summary, and this I like, this flow, like how it works. Um,

  57. 13:03

    and here also answered my question, like, uh, "Does it simulate envelopes? No. Inter- intercepts real ones. They spawn collector..." Yeah, I'm not gonna read it, but this information, all of this, it's very useful.

  58. 13:18

    Like, if I didn't have AI, I would have to do this myself, you know? Um, I mean, I don't like to, to, uh, just ship something I don't understand.

  59. 13:29

    If it's a vibe coded project, that's fine, but this is a real serious business. It's my work. Uh,

  60. 13:37

    so yes, uh, this skill is helping me a lot with that, and also to review PRs, because maybe I'm reviewing a PR of a colleague. I have a lot of context, but not enough to approve that PR, and I wanted to have that context, so I use this skill to give me that.

  61. 13:55

    Uh, okay, so back to my vibe coded presentation. [laughs]

  62. 14:01

    So, uh, Jack Nations wrote a blog post called Vibe Coding Our Way to Disaster, drawing on Rich Hickey's, uh, simple made easy philosophy. He proposes three phases, research, planning, and implementation.

  63. 14:20

    I think you guys also heard about it o- outside, like even Claude Code has this planning mode, right? Um, so yes, I agree with all of that, but I think it's missing this step.

  64. 14:32

    Like, you need to understand the research, uh, your agent did, you know? You need to understand that, and to steer, as I said, maybe it's going the wrong direction, or maybe you need to explore something else, and you, you need to have that to understand that.

  65. 14:47

    Then after that, you can say, "Okay, plan that for me, and let's do the implementation. Let's go ahead."

  66. 14:56

    Um, so Armin Ronacher, uh, he's, uh, the creator of Flask and a former Sentry. Now he's worked on his own startup. Today, he's gonna give a talk here, by the way.

  67. 15:09

    He's around. Um, so he wrote in his blog post, "When more and more people tell me they no longer know what code is in their own code base, I feel like something is very wrong here."

  68. 15:22

    And yes, I agree. Um, so what I hope you, you can take away from this presentation is that, uh, the biggest unlock from AI in a large code base isn't generation, it's comprehension.

  69. 15:39

    Uh, I tracked my own usage, and I was surprised, uh, sixty-seven percent of my prompts are co- uh, comprehension, and only two percent generation. Maybe you track your own AI usage as well, and you can improve it, right?

  70. 15:55

    Uh, so AI is the teammate, uh, who never gets tired of your questions, so there is no dumb questions. It's the cheapest senior engineer out there, so just go for it.

  71. 16:07

    Um, yes, and align your mental model before you prompt, because, uh, you know, the code is gonna flow naturally. And don't ship, uh, slop code into the code base that pays your salary.

  72. 16:20

    Uh, ship kino code, really. [laughs] This is the term, um, the industry is using. And, uh, yes, thank you. Um, try Sentry. This was a sponsored talk. We have a booth downstairs if you wanted to stop by to say hello.

  73. 16:35

    If you scan this QR code, you're gonna get a three months free trial of our business plan, and I hope you enjoyed the presentation. [laughs] [applause] [upbeat music]