Shadowing Practice: What Does an AI Engineer Do Now? - Aparna Dhinakaran, Arize AI - Learn English Speaking with Video
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Awesome.
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Well, welcome to AI Engineer.
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I thought given we're at AI Engineer New York, we'd kind of revisit the question of what does an AI engineer do?
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I feel like every year we have an idea of what an AI engineer does, and that definition kind of changes.
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And as I was putting this talk together, it was super timely,
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because Karpathy actually just tweeted something this weekend where he kind of talks about this growing gap between
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basically folks at the frontier who are running thousands of agents and swarms of always -on on agents,
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and he really kind of drops this funnel where six billion plus users have maybe touched or interfaced with an LLM.
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One to two billion are maybe like casual free tier users.
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20 million are professionals who have maybe started using it for more, taking some piece of work that would have taken weeks down to days.
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But really, those at the Frontier Labs are, which is a super small percentage, it's like 5 ,000 users,
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are the ones who are running thousands and thousands of agents at once.
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And as I look at this, it really is, I mean, he's kind of dropped a bit of the funnel.
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I mean, the first question anyone looks at this is that, where am I on this?
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And, you know, he's kind of drawn out a bit of a roadmap of how does everyone get to, in the next couple of years,
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one of those 5 ,000 users who are running thousands of agents.
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And this has been happening, I'd say, for a couple of decades now, where the abstraction layer has gotten higher and higher.
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I mean, definitely faster in the last couple of years, but we've gone from engineers writing higher -level abstraction languages to,
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I mean, just copy -pasting code, using local agents, to using cloud agents,
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to really the expectation now is how do all of us start running a team of agents?
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or factories of agents internally and build toward that.
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And what this has led to is essentially what is the then and now of what an AI engineer is.
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And this is a bit, I think, of a roadmap for some,
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but I think internally this is what we've already started to kind of push towards.
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As an AI engineer, your job is no longer to just write the feature, write an AI feature.
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It's about how do you build the loop to write that feature.
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It's no longer about just reading the diff and actually doing code review.
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It's actually how do you set up systems and agents to do the code reviews,
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and how do you write evals to actually do the non -deterministic, to review those non -deterministic tasks.
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It's actually no longer even about just setting up metrics and software kind of telemetry, but it's actually how do you have agents go through
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and read the observability data and and have agents actually consume that telemetry to find patterns.
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And so this loop that we've kind of been talking about, which is the build, the eval, ship, you monitor, you improve it,
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well, the AI engineer is now actually responsible, not just for doing each of those steps,
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but how do you build the loop that actually does each of those steps?
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And at Arise, one of the things we've rolled out in our product,
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and I'm gonna talk about a bit of what we've learned in building this, is we built
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Signal to actually run this loop for AI engineers.
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What Signal does is that
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Signal actually looks at all of the traces, it reads the telemetry, it identifies patterns in that telemetry,
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and it can actually put up a fix or a PR if it has access to the repo.
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And Signal, Signal's an always -on agent.
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So this is an example of how
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Signal starts, but it actually starts off by just reading the traces.
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Super simple example, but it goes through, reads, and all this is configurable,
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but a certain volume of traces that came in in some cadence,
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it reads it, and then the goal is to actually identify issues from that telemetry data.
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It has to keep track of issues that it's previously seen.
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So we have to consider the memory of did it see something before
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that it thought was an issue? did something seem like an issue
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but it wasn't actually captured
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and now as it collects more data it actually thinks it's much more of a critical critical issue
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and so signal actually starts off by reading the traces to identify problems
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and so one of the first things that that we realized was
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that the future of who's actually consuming telemetry data has really changed
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It's no longer about humans consuming telemetry data.
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It's not even about humans consuming it and agents kind of taking on the task,
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but it really is how do you set up your telemetry data so that agents are the primary consumers of that data?
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And what that does is that that observability data can become the trigger for actually running and triggering these always -on agents.
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And so the first thing that if you're building a team of, of a factory of really always on agents is,
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what triggers these agents to wake up or start?
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And there can be a variety of triggers.
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For signal, for us, it's configurable to run on an ongoing cadence.
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So every three hours, six hours, go pull the last X amount of traces, read them, identify problems.
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It can also be triggered by events.
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So Slack messages, and some user complains about something.
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It can be a metric -based trigger, but these are essentially triggers to kick up the agent actually starting.
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Once the agent actually starts, we have to spend some time figuring out, well, what does an agent run actually look like?
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For us, we actually pull in all of the context of the traces, any additional third -party data,
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so we allow collecting to APM logs, APM metrics, but all of the third -party telemetry data that we want to pull in,
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access to the repo, access to the actual,
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the access to any keys that are needed to actually run this agent,
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but all of this information is put inside of a sandbox, and this is actually what's started up every time the agent wakes up.
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And so the agent's triggered, we build a sandbox, We put everything that we need inside of that sandbox.
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There's a proxy.
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The only way to get information in and out of the sandbox is by the single proxy.
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And everything that happens inside of that sandbox is actually already getting traced.
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And every output, so the issues that are generated and created are all getting saved to some kind of memory.
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And so this happens every single time the agent actually runs.
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Every time the agent finishes running, so it's sweep through all of the traces, it either found something or it didn't.
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There's what actually stays and what actually gets thrown away.
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What stays is that if it found any issues, all of the output is actually saved off in some kind of memory
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so that the next time an agent gets spun up, it doesn't have to start from ground zero.
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It has a history of what are the issues that it actually found.
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The agents are traced.
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So all of the telemetry data of what the agent called, you know, if it actually put up a PR,
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all of those things are actually traced.
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And any skills that it may have learned while actually doing a run can also get saved to the actual repo.
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But the things that go away is the sandbox gets torn down.
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Any credentials, you know, obviously get removed.
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The repo access is removed and so all of the stuff
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that doesn't need to be saved kind of gets torn down after the run of Signal. And we've designed
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Signal in a way to actually keep this agent durable.
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So that means that either if the sandbox fails or if the harness fails,
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then it shouldn't have to start everything from scratch in the middle of the run.
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It should actually just be able to pick up from the step
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that it failed and all of the state
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so -called is kind of saved off in kind of a memory outside of the sandbox.
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And so we've designed this to actually be a very durable agent so that if it fails, it can spin it back up.
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You can launch a new sandbox, launch a new harness to kind of pick back up where it started.
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So all of this is essentially the underpinnings of how we built signal to run every single run.
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Agent gets triggered, agent gets started, loaded with all the context, the evidence, it sweeps through the telemetry data, it either finds something or it doesn't,
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all of that gets saved to memory.
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If something fails in the middle, the agent can get reset up.
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And we built
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Signal to also be agnostic of the model or the harness.
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So today, Signal supports not just cloud code, which is what we started with, but it also supports cursor and codecs,
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and the goal is to actually support all of the major, even the open source harnesses out there.
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And Signal actually also supports multiple, will support kind of multiple models.
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So even if you're using cloud code, you can pick any of the models inside the anthropic family.
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And this is really important to us because,
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one, it's.. an awesome way for us to evaluate the quality of the agent every time we spin it up.
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It helps us control costs so we can see as we use more powerful models the better quality maybe the PRs are,
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but can we reduce the quality of the, can we reduce the cost and actually still maintain the rigor
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and maintain the quality of issues that are actually detected by signal?
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So this is something we deeply thought about from first principles when we were designing signal.
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And this is actually a pretty good segue into what does an AI engineer need to do.
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Well, an AI engineer really needs to kind of learn how to write evals.
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And so we actually, every time signal runs, signal finds an issue, it puts up a PR.
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Well, there's evals that are run on top of the agent to test that PR.
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And an eval can be as simple as testing something as part of your development workflow, you have a data set,
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you can be a simple prompt with outputs that you're testing to make sure that there's no regressions.
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But these evals can also, they need to be kind of aligned with human judgment.
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And I'd argue
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that this is actually probably where an AI engineer should be
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spending a lot of their time as they're building the system to actually run the loop.
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They should be actually spending time looking at the traces with their agents, identifying what the common errors are.
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And there's a lot of skills.
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I think I was hearing
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Lance yesterday talk about the Claude skill to build evals.
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We have a skill with Arise Evaluator to actually help you build and write evals, as well as align those evals with the human judgment.
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but this is probably where the shift to defining what is good becomes really, really important for an AI engineer to actually do.
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And for a lot of teams I think we speak with and we work with, their question becomes, well, how do I trust the evaluator?
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And do I really trust that the eval is going to catch
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or how do I trust that it's as good as my judgment?
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And I'd say what we spend a lot of our own time doing for our own products is
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that we actually evaluate the evaluators.
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We spend a decent amount of time on actually building these data sets, golden fruit data sets.
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We run evals, verify them against the human output, and then try to align to make sure
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that the judge actually agrees with the human and they're in agreement before we actually ship out a new evaluator.
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And these evals can then be used both in an offline testing as well as in an online testing manner.
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And this is probably what I would say as an AI engineer is more and more of what the job is becoming.
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And so what I'm going to leave you guys with is as an AI engineer, you've built this agent.
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You've built this agent to actually run, be always on you run evals before you actually ship it into production.
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You can ship it, and then once it goes into production, the goal is to actually trace everything.
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And tracing everything now means not just tracing the agents, but also tracing the coding agents behind the agents that built this.
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So you can trace cloud code, you can trace cursor, you can trace codecs, but all these coding agents can be traced
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so you can understand what happened in the factory of agents that you all are building.
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And then these agents that all the traces can then also be evaluated online.
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So there's an offline evaluation when you're actually testing what a new change is,
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but as it goes into production these become your online evals that you're running on top of the telemetry data.
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This for us ends up becoming the new data that then gets pulled back into the agents to read the telemetry data,
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the evals, and surface up kind of problems inside of the traces.
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So the system really becomes, how do you go from collect data, eval that data,
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run agents on top of it so that you can find problems
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and figure out where the gaps are to actually improve your systems?
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So if I go back to kind of the question that we started this whole thing with, It's what does an AI engineer do?
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I mean the gap between,
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I think there's a pretty big gap between what's happening in the labs today and what's happening with a lot of businesses.
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And some of it could just be the data inside of
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businesses are a lot more messy than what is very constrained and testable inside of a lab.
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But AI engineers are kind of the role to close that gap.
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And I think it really starts with tracing everything, tracing the agents you're actually building.
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running evals, being able to learn how to test these products that we're really building, and then how do you build a system so
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that agents can actually improve from the telemetry that's actually being collected so that you're no longer just building individual features,
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but you're actually building the system to improve your products.
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If you want to learn more, come check out.
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We have a booth.
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You can check out
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Signal, learn more.
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But thank you so much for listening to us.
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Thank you.
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אוצר מילים והערות הגייה לשיעור הזה
שיעור הדיבור הזה ברמה C1 מבוסס על הסרטון “What Does an AI Engineer Do Now? - Aparna Dhinakaran, Arize AI”. המילים שחוזרות הכי הרבה: agent, signal, eval, engineer, telemetry. בסרטון הזה יש 171 משפטים ו-2536 מילים לתרגול Shadowing. הדיבור נמשך 15:12. הדובר מדבר בקצב טבעי של כ-167 מילים בדקה, קרוב לשיחה יומיומית. 86% מהמילים נמצאות בין 3,000 המילים הנפוצות ביותר באנגלית; כדאי ללמוד את השאר לפני שמתחילים.
אוצר מילים מרכזי בסרטון הזה
11 המילים המתקדמות ביותר בסרטון, עם ההגייה והמשמעות שלהן:
| מילה | הגייה | משמעות |
|---|---|---|
| harness פועל | /ˈhɑɹ.nəs/ | רָתַם |
| consume פועל | /kənˈsum/ | צרך |
| metric שם תואר | /ˈmɛt.ɹɪk/ | מֶטְרִי |
| abstraction שם עצם | /æbˈstɹæk.ʃn̩/ | הפשטה |
| cursor שם עצם | /ˈkɜːsə/ | סמן |
| proxy שם עצם | /ˈpɹɑk.si/ | שָׁלִיחַ |
| reset פועל | /ɹiːˈsɛt/ | אפס |
| messy שם תואר | /ˈmɛsi/ | חסר סדר, מבולגן |
| verify פועל | /ˈvɛɹɪfaɪ/ | וידא |
| lance שם עצם | /læns/ | רֹמַח |
| detect פועל | /dɪˈtɛkt/ | גילה, זיהה |
Phrasal verbs שתשמעו
| מילה | הגייה | משמעות |
|---|---|---|
| check out פועל | בדק | |
| figure out פועל | הֵבִין, מצא תשובה | |
| wake up פועל | /ˌweɪk ˈʌp/ | התעורר |
| back up פועל | נָסוֹג | |
| throw away פועל | הִשְׁלִיךְ |
דקדוק בסרטון הזה
המבנים שהדובר משתמש בהם הכי הרבה, עם המילים המדויקות מהסרטון:
| מבנה | בסרטון |
|---|---|
| הווה מושלם (Present perfect) have/has + past participle — פעולה שקרתה בעבר ועדיין יש לה חשיבות או השפעה עכשיו | has gotten · we've gone · has led |
| סביל (Passive voice) be + past participle — הדגש הוא על מה שקורה, לא על מי שעושה את הפעולה | wasn't actually captured · be triggered · are needed |
| משפטי זיקה (Relative clauses) who / which + clause — מידע נוסף על אדם או דבר | are, which is · ones who are · about, which is |
הגייה ששווה לשים לב אליה
הדובר משתמש ב-34 קיצורים וצורות מקוצרות, כמו you're, we've, doesn't. אמרו אותם בצורה הקצרה, כפי שאתם שומעים אותם.
- הצלילים של “sh” ו-“zh”: abstraction /æbˈstɹæk.ʃn̩/, credential /kɹɪˈdɛnʃəl/, regression /ɹiːˈɡɹɛʃ.ən/, expectation /ˌɛk.spɛkˈteɪ.ʃən/
- מילים ארוכות — שימו לב להטעמה: telemetry /tɪˈlɛm.ɪ.tɹi/, configurable /kənˈfɪ.ɡə.ɹə.bəl/, deterministic /dɪˌtɜː.mɪˈnɪs.tɪk/, internally /ɪnˈtɜːnəli/, expectation /ˌɛk.spɛkˈteɪ.ʃən/
צלילים שדוברי עברית מתקשים בהם:
- /w/ — שפתיים מעוגלות, לא /v/: sweep /swiːp/, workflow /ˈwɝkfloʊ/
- /æ/ — פתוח יותר מה-“אֶ” העברית, לא כמו “a” ב-“cup”: eval /ˈiː.væl/, sandbox /ˈsæn(d).bɑks/, abstraction /æbˈstɹæk.ʃn̩/, anthropic /ænˈθɹɒp.ɪk/, lance /læns/
- /r/ — הלשון לא נוגעת בשום מקום, לא ה-“ר” הגרונית: telemetry /tɪˈlɛm.ɪ.tɹi/, harness /ˈhɑɹ.nəs/, metric /ˈmɛt.ɹɪk/, abstraction /æbˈstɹæk.ʃn̩/, configurable /kənˈfɪ.ɡə.ɹə.bəl/
איך לתרגל עם הסרטון הזה
- הקשיבו לכל הסרטון פעם אחת בלי לדבר, ורשמו את המילים שאתם לא מכירים.
- התחילו במהירות 0.75×, תרגלו Shadowing משפט אחר משפט, וחזרו למהירות רגילה כשזה מרגיש קל.
- הקליטו את עצמכם והשוו למקור, תוך תשומת לב למילים כמו harness, consume, metric.
מהי טכניקת השאדואינג?
שאדואינג היא טכניקת למידת שפה מבוססת מדע שפותחה במקור להכשרת מתורגמנים מקצועיים ופופולרית על ידי הפוליגלוט ד"ר אלכסנדר ארגואלס. השיטה פשוטה אך עוצמתית: אתה מקשיב לאודיו באנגלית ילידית וחוזר עליו בקול רם מיד — כמו צל העוקב אחרי הדובר עם עיכוב של 1–2 שניות בלבד. בניגוד להאזנה פסיבית או תרגילי דקדוק, שאדואינג מכריח את המוח והשרירים בפה לעבד ולשחזר דפוסי דיבור אמיתיים בו זמנית. מחקרים מראים שזה משפר משמעותית את דיוק ההגייה, האינטונציה, הקצב, הדיבור המחובר, הבנת השמיעה ושטף הדיבור — מה שהופך אותו לאחת השיטות היעילות ביותר להכנה לIELTS Speaking ולתקשורת באנגלית בעולם האמיתי.