Agent Experiments is an Arize feature for validating changes to fixed AI agents. It replays datasets of real failure traces against a deployed development endpoint and compares the results with production across quality, latency, and cost before a change is released.
Arize AI is an AI-agent observability, evaluation, tracing, and experimentation platform. It stores production traces, evaluations, annotations, datasets, and experiments so teams can investigate agent behavior and test proposed changes. It combines audio, transcripts, and traces in a session view for investigating voice-agent problems such as latency, interruptions, transcription errors, and incorrect tool actions. Its audio workflows use OpenInference semantic conventions to trace real-time audio across providers, run evaluations for sentiment, latency, interruptions, and task success, and attach evaluation results to spans. Its CLI and skills can bring spans and evaluation results into a coding agent's workspace, where the agent can inspect supervisor, specialist-agent, model, and tool calls, create or use evaluators, and run experiments against application failures. Arize AI is offered through its web platform, Arize AX, and terminal-based tooling.
Arize Signal is an agent observability and improvement product from Arize AI. It continuously groups recurring production trace failures, gathers evidence, investigates likely root causes using application traces and repository code, and can propose fixes or open pull requests. It is presented as part of Arize's platform for agent observability, evaluation, tracing, and experimentation, which uses production signals to support continual improvement.
Arize Skills is a set of installable skills for AI coding agents, maintained by Arize, that guides agents through adding observability, creating datasets, running experiments and evaluations, and optimizing prompts for LLM applications. The skills use the ax CLI to interact with Arize and include workflows for tracing and debugging applications, OpenInference-based instrumentation, dataset management, experiments, LLM-as-judge evaluators, annotations, prompt optimization, provider credentials, and production monitoring. They can be installed into agents such as Cursor, Claude Code, Codex, GitHub Copilot, and Windsurf through npx, the repository's installers, the ax CLI, or a Claude Code plugin. The repository is licensed under MIT.
Datadog is a cloud monitoring and observability platform for collecting and analyzing metrics, traces, logs, and related data across infrastructure, applications, networks, containers, databases, and cloud environments. It provides infrastructure monitoring, application performance monitoring, log management, dashboards, alerting, incident management, security monitoring, synthetic monitoring, and user-experience monitoring. The platform is used to identify broken or correlated systems and investigate operational issues, although the video notes that observability alone does not necessarily establish root cause. Datadog also offers AI features, including investigation, remediation, agent observability, and an MCP server.
FastAPI is a Python web framework for building APIs using standard Python type hints. It is built on Starlette for web functionality and Pydantic for data handling, providing validation and conversion for request data from JSON, path and query parameters, cookies, headers, forms, and files. Type declarations also generate OpenAPI and JSON Schema documentation, including interactive Swagger UI and ReDoc interfaces. The video describes FastAPI routing requests from a mortgage application to five specialized agents.
An agent framework used by the Wonder Toys application. It includes OpenInference instrumentation for sending application traces to an observability system.
Project Rosetta Stone is a workshop and comparison repository for instrumenting the same AI agent with different agent frameworks and observability platforms. Its Wonder Toys application is a chat-to-purchase toy-store assistant with semantic vector search, product browsing, purchasing, order tracking, and cancellation; framework variants are provided without observability, with Arize Phoenix instrumentation, and with Arize AX instrumentation. Readers can compare the instrumentation footprint by diffing each framework's baseline and instrumented directories, then run the applications locally to send traces to Phoenix or AX. The repository also includes shared synthetic requests, evaluation harnesses, voice testing for the OpenAI voice tier, product data, and instructions for adding frameworks and running the end-to-end workflows; it is licensed under MIT.
Wonder Toys assistant is a demo shopping assistant built with the OpenAI Agents SDK and a FastAPI backend. It provides tools for searching and purchasing products. In the demonstrated application, a broken price filter can return products outside a user's requested budget; the example is instrumented with Arize so traces and failed cases can be investigated, used to propose a code fix, and evaluated against the budget constraint.
Searchable transcript of Building Self-Learning Loops for Your Agent — Fuad Ali, Arize AI — AI Engineer (38:55). Search for a phrase, then click its timestamp to jump straight to that moment in the video.
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00:12 My name is Fouad Ali. I work in product at Ariser AI and I've been working on agent experimentation and generally agent tooling across the stack at Arise. Uh you just heard from Lori uh who is also part of our team here at Arise. He's going to be flitting around the room. Aparna, our co-founder and CPO, will also be flitting around the room to help you guys out if you require it during this workshop.
00:33 Uh, what we're going to be talking about today is building self-learning loops for your agent. Um, and just to give you a bit of background on myself and and how I got into the space, I think this concept of self-learning loops is really new to this idea of AI engineering. Uh, and there's a lot of features in Arise that kind of help you support it. But to kind of frame this um I actually worked in backend distributed systems at SpaceX and it is actually quite interesting how we're kind of coming full circle in this
01:04 world of telemetry where you know if you think about it uh a rocket system that is continuously course correcting every millisecond is actually consuming its own telemetry and then actioning that telemetry in order to selfcorrect. And basically what I'm going to explain is how we've sort of built this idea of self-learning loops for your agent in Arise and how you can use that to self-correct.
01:29 >> Cool. So to give you a bit of history on Arise, we made a few big bets. Uh we were first to the market with LM as a judge back in 2023. Uh we created the first set of semantic inventions for LM applications with open inference, open sourced. uh we created the first real-time AI data store ADB. Uh in 2025 we released agent evals and Alex which is our in-house agent uh to kind of help you with AI engineering.
01:54 Think of cursor for AI engineering. And in 2026 the next big bet we really want to make is how you can create self-correcting agents with a feedback loop. Um and I think everyone understands sort of like how that is going to be extremely powerful to this idea of of agents generally. Um and so I really think the the industry is evolving quite a bit. Um when it comes to software engineering, uh you sort of have the classic, you know, software 1.0 where there's an app and it's traced to data dog and there's some sort of
02:30 observability platform that human beings read, a human dev then writes code to actually correct it. I think 2.0 was really humans interacting with this in prompts. Um, so same thing, but instead of you writing the code, you write a prompt that then corrects the behavior that you're seeing in your app. Uh, what we're moving to really is phase three, which is the fully autonomous loop.
02:52 In this loop, your application produces telemetry events, observability, and then what is actually reading that is your coding agent. It's not just a human reading it and then putting a prompt in. Um, if any of you guys are using things like cursor, claw code, the data dog CLI, you're already seeing some of this. Uh, the gap is you're still probably prompting that to go read that information from a backend like data dog or whatever it is.
03:18 Um, and I think when you evolve to the to the needs of like, you know, thousands of agents across a stack, it starts to become pretty much impossible for a human being to actually read those things. And so um going to be talking a little bit about some of the new features in Arise that we've released that kind of support this uh flow. But what I want you guys to center on is that observability has to evolve from humans reading dashboards to really programmatic interfaces that agents consume, agents can operate on, and
03:51 agents can self-reflect on as well. So just some proof in the market. Uh Arises processed over one trillion spans. Uh we've run over 60 million evaluations per month. Uh and we have over three million downloads per month. Uh trusted by a ton of different cool teams. Uh whether it's Spotify, Reddit, Uber, Trip Advisor. So just to, you know, build some confidence that I'm not just here vibe coding something and telling you guys how it should work.
04:21 Um and I think if you look at the industry um the industry is really kind of moving towards this as well. It's not just you know these teams but it's also teams that aren't even Arise customers like Ramp Labs and how they've kind of moved to this idea of making software self-improving and self-healing. Um and I just point this out to say the industry is really moving in this direction.
04:48 It's not just us. You want agents that can detect a problem, investigate it, ship a fix in the same loop. Uh, and that's sort of the loop I'm going to show you guys how to build today. Cool. So, um, I think the the big problem I want to outline here is the failures that cost you are invisible at launch. And that's sort of by definition because if you had caught them, they wouldn't have cost you.
05:15 You would have shipped the fix already. Um, and I think like really what I'm trying to set on here is, you know, your agent works in your experimentation phase. It works with the evals that you set up. You don't really know all the evals that you have to set up before you ship it. Otherwise, you would have written it. Um, and something breaks an edge case that your team just wasn't aware about.
05:38 For example, in this in this example, a shoppers asking for a birthday gift under 30 bucks, they get a list of items over $30, $50. And then what you'll see in production is the agent confidently purchases the wrong item. Always does so extremely confidently uh because it never verifies the product. And these don't again these don't show up in staging.
05:56 They show up when users complain. They show up when you get a support ticket. They show up when you get paged on call at 3:00 a.m. Um and that gap it works in staging, didn't work in production, something sees off is what I'm trying to close here. Cool. So, uh, real quick just to ground our kind of talk in an example, we have this thing called the Wonder Toys assistant.
06:23 Um, it's a shopping assistant, very simple application. Has a few tools available to it to search products, get product details, get the information required for one particular product, purchase that product, uh, check an order status and cancel an order. Um, we have the OpenAI agents SDK running on top of this a fast API backend uh search with a vector search and a text fallback and it's traced to Arise.
06:50 So you can actually see the source of truth for what that agent is doing in production. And just to give you a little kind of like sneak preview of what that agent is, very simple, you know, you kind of have like a web interface for Wonder Toys. And I'll zoom in a bit so you guys can see. Um, I can click on ask about the product ask the agent to purchase the product.
07:21 And don't worry, no real credit cards involved in this demo at all. Cool. Um, so that just grounds us a little bit in the example we'll talk we'll be talking about today. Um, and I think the the key thing here is we're going to be going down a few routes to explain some of the new features in Arise. Um, but really it's going to be four commands and then a bit of clicking around in the UI interface.
07:47 You're going to get clone the repo. And don't worry, I'll I'll flash back to the slide so you guys have enough time to walk through this. You're really just going to get clone the repo, navigate to it, add your API keys, mpm rundev, and your tracing should be set up after you've gotten an API key from rise uh and an open a open AI API key to run the open AAI agents SDK.
08:08 Um, and that's pretty much it. And then once you start clicking around, you'll see that the traces automatically start flowing uh into Arise. Cool. So, um we're going to get to kind of like how we can actually start breaking this thing, but um the specific bug I've put in this repo is around this kind of idea of price gates. Um so, most of the most of the kind of queries will work and you guys are welcome to play around with it on your own.
08:41 Uh, but if you ask for a birthday gift under $30, you're going to be get getting items over $30. Very common edge case, one where, you know, a specific gate wasn't triggered. Um, and without the actual kind of context of the coding repo and the context of the traces, it's very hard to find edge cases like this. Um, in, you know, sort of an opaque system that's just emitting telemetry data.
09:06 Um, the agent won't flag it. It'll return a confident list of completely wrong products. How do you actually write an eval for an edge case you haven't seen yet? And so the first of the new features that we have in Arise to kind of talk through this is called signal. Uh signal is an out ofthe-box agent that lives on the Arise platform in a sandbox and it's really just one toggle to turn on in project settings.
09:32 And what signal does is it continuously runs on your production traces um in the background. And so what it'll do is it'll read every trace that's coming in. It'll start to cluster failures by root cause, ddupe those failures across runs if the same failures are occurring. And because it is an agent with an entire harness, it can actually call to external tools.
09:54 So what you can do is you can connect signal to your GitHub repo, you can connect signal to downstream data sources like data dog. And what signal will do is sitting on top of your trace data, which is a source of truth for what your agents are actually doing in production, it will then self-reflect and go look at the code for the agent that you're running.
10:16 Go look at downstream data sources like data dog and can actually write an investigation, write a severity entity type and and collect some evidence for that and actually write a PR for you and open that PR. Um, so you're, you know, kind of flashing back to the beginning of this presentation that I was talking about where we have this evolution of software engineering.
10:37 We're going really to that phase three where the agent is consuming its own telemetry data off of the observer view platform. It's then looking at its own code and then trying to propose a fix based on some evidence that it has. Cool. So that's that's the first part signal. Um the second part is really around this idea of skills and we'll be having a workshop later today that goes really in depth to all the skills that we've released.
11:09 Uh but maybe you don't want to have that agent managed on Arise. Maybe you just want to be able to pull data from some store that you have. Uh what we've also released is this concept of Arise skills. Um so Arise skills allow your coding agent that you're working in whether you're using cursor cloud code uh codeex or 40 plus other coding agents open code or amse whatever it is um and this can actually start to query the traces from rise so um a good example of that I think is uh give me one back.
11:50 I can actually ask my coding agents, this is just cloud code, but I can ask my coding agents to actually go read my traces once I'm in the repo. And so it'll be able to see what the agent's doing uh from the trace data as well as the actual code for that agent at the same time and make some really impactful fixes. Cool. Uh so variety of skills. We have skills for creating data sets off of failures.
12:19 We have skills for uh going in and reading traces and doing some of that trace error analysis. Uh as well as skills for you know actually running evaluators and running tasks on the platform as well. So the first part of this kind of skills workflow is that claw can actually write the fix for you because it's sitting in your coding repo. similar to Signal.
12:39 Signal is running on top. It has access to your GitHub repo. It can go push up a PR for you. Um, and so these two paths are sort of equivalent. Um, you have claw code with the Arise trace skills. That's kind of like an ondemand method. Let's say you're an engineer working on your product continuously and you're actually sitting there and trying to improve it versus signal which is the always on sort of continuous method.
13:03 It's continuously serving up uh you know new insights, new analysis for you based on your trace data. Um and signal like I mentioned before can actually kind of root cause that because it has access to your coding repo. It can write an investigation report. Um and it can actually try to uh put up a PR based on that. And it's not just you know individual traces that are looking at it's looking at the entire context of what your agent is actually doing in production.
13:34 So, um, real quick is I want to get to the meat of this presentation. I'm going to pause here and keep this slide up. Like I mentioned before, we have Lori in the room and Aarna in the room um, to help out and run around and kind of help you guys set up. A partner is right here. I think Lori is somewhere else in the room, but he'll he'll be running it around.
13:54 And we got Dat as well from our team. Uh, so real quick, these are literally the only four commands you have to run. I would recommend signing up for a free account at arise.com to just get your Arise API key to start seeing some of the traces flowing in. And you will need an OpenAI API key as well for this presentation. So, I'll pause here, leave this slide on, and then we can start walking through getting the traces for the Wonder Toy signal.
14:19 And then what I'll do is we'll walk through how that loop actually starts to get into production. Um, so yeah, and if anyone needs a key, uh, DAT is more than willing to give give you guys a key. All right. Hey, how's progress going so far, guys? And if you don't have your laptop, don't worry. I'll be walking through sort of what this flow looks like.
15:00 Uh, if you were to have done it yourself as well, I'll give you guys maybe two to three more minutes to set up your API keys and to run these commands and clone the repo. I know that the Wi-Fi has been a little wonky today, so won't fault you guys for that. accessories. >> Oh, no you do. >> You still get access to evaluatories. You don't get there's a few features like code evals and things like that.
15:33 Yeah. And for this flow, we'll mostly be showing how cloud code can do that trace analysis for you through the skills and through signal. Um, so it won't be it won't be necessary for this one. Anyone having any trouble? Anyone needs some help? Feel free to raise your hand and we'll flit around the room. >> Sorry. >> Here, I'll jump down. >> Sorry. issue.
16:19 >> Ah, okay. >> Cool. That's probably a Wi-Fi issue. So, if you guys are having Wi-Fi issues, let me know when you can start to see um the repo pull up. It might take a few seconds. Uh but this is generally the structure of the repo. Um we have kind of the agent, all the scripts that it needs, um and a bunch of other things there. So, I'll give you guys a few more seconds because I know the Wi-Fi is a little slow.
17:03 Uh, once we get folks starting to get into the repo, uh, please make sure to sign up for an API key as well, uh, and get your Open AI API key as well so we can get started. Should I just keep going? >> I just like do it along. And just a reminder, all you have to do is run those few commands. So you get clone um the repo, then you'll cd into that repo.
17:59 So I'm already in there. Um then you'll mpm install. And then make sure that you have your M file loaded. This is where you're going to be putting your three keys. >> No dot at the end here. Wait, sorry. Which one? >> Oh, the doc get at the end. for example. Got it. Can't change it right now, but uh we'll we'll get it going. >> Cool. All right, I'll start I'll I'll leave this up for for maybe 10 more seconds.
19:29 Um very simple. Make sure you put your M values in. Uh there may be a bug there as well. U but just make sure you look for the M file, input your own keys. Um just to show you an example. Uh I will take this off so you don't see my keys, but there are there are some keys in there as well. Um and then you mpm install an npm rundev. So what you'll then see is that your Wonder Toys interactions, so your interactions with this chatbot are being traced to Arise.
20:00 Uh and in your Arise traces, you'll actually be able to see um the things that are coming in through this chatbot, right? So I can see that I started an agent workflow. The Wonder Toys agent then um responded. It searched products uh took another turn and actually found the products and returned them to me. uh and I can kind of see the output of what that agent is doing.
20:22 So this is like kind of like baseline value prop can I trace can I understand what's going on uh and then the second part of this that I wanted to show is sort of like some things that signal can do on top of this. So you have all this trace data uh coming in through Arise and what you actually want to start doing is you want to enable Signal to actually run on top of your agents.
20:45 And I'll show you an example of Signal uh in a repo. So Signal runs continuously in the background. It takes a few minutes to actually get started once your traces start flowing in. Uh so I'll show you. We're getting hit with a little slow Wi-Fi, but I'll show you an example of signal already set up and ready to go. and sort of how it's it's sort of doing that loop of self-improvement.
21:29 So kind of like I mentioned earlier, traces are flowing in through arise. Every interaction you do with the app comes in. Um and then this is sort of what signal looks like. Um so signal has kind of this clustering analysis of different issues that it's seeing across your application. Uh you'll see sort of this table of categorization of what those issues are.
21:51 Uh you can see those issues trending over time. Um, and then you can actually go read examples of what that are, right? So, in this example, uh, we can see some serialization issues with Python. Um, and that that is corrupting the agent, um, input. And you'll see that it actually opened a PR for me already. Um, and so I can just one click go into that PR and start viewing it.
22:15 Um, there's also this idea of like writing at least the ticket out of that investigation. So you'll see this overview of what um this stock agent is doing, fabricating results for example, uh a hypothesis of why that's happening, evidence for that within an individual trace, um and then a proposed fix for you to go and and then do yourself. Um so kind of going back to this concept of like agents consuming the telemetry data, this agent is sitting in the back consuming the telemetry data, actually writing out the
22:49 evidence and proposing a fix for you. Uh, in this example, we've still, you know, left the code review to a human. Your mind's the limit on how far you want to take these things. We do recommend you do that as well. Um, but kind of this idea of like the self-healing software you can see uh in action. How are we feeling on um setup? Is was the Wi-Fi able to get up and running for people?
23:12 Did they were you able to No. Okay, cool. I will continue kind of through the presentation and maybe we can take some time at the end as well to kind of flit around the room and help you out. So kind of the four steps that I wanted to walk through you guys were one detect so surface the fallback failure before a user had reported it. Two based on that failure and eval running in your platform actually investigate what's happening with real traces.
23:40 Um, and so in our example, there was a bug with the price check. And so what signal and your skills via cloud code was able to do was look at the traces, look at what a user is asking within the trace around, you know, the price being under $30, actually marry that to an investigation of the code path that checks the price filter, and then propose a fix for you.
24:06 So um in my code there was a bug with how I was actually doing that price filter and using signal using the trace data that's in Arise and using uh the skills via cloud code. I was able to see that interaction happen with a real user and then fix that you know kind of wrong filter in my code for the agent. Um and then finally we have this verify path.
24:28 Uh wasn't planning to get to it today because it is a little bit of setup. uh but happy to kind of talk through that and then we'll make sure to uh kind of return when the Wi-Fi is good as well. So again just a just a quick recap on the basics here. Uh we have this idea of you know the core loop running on a real agent which detects the issue around the h filtering bug investigates uh using the arise trace skull or through signal uh fixes that issue claude actually patching the fallback uh and then verify it uh which
25:02 I'll kind of walk through how that works in arise as well uh with harness as a judge and agent experiments um and then signal it'll kind of do this in parallel as well for when you don't want to be hands-on with the keyboard and want to run this in the background. Uh signal will be tuned to your own taxonomy, your own traces. Um it you can also configure it to be set off alerts.
25:24 Um so for example, if you have an alert on a specific eval that needs to stay below um you know 1% trigger rate, you can configure signal to be triggered off of an alert um or you know a latency spike or a cost spike or anything like that. Um, you can then have custom harness judges running in CI on those PRs. Um, you can experiment with those and then you can kind of optimize things from there.
25:48 Uh, and quick quick shout out as well at 2 p.m. Lori will be doing a workshop hopefully the Wi-Fi is a little bit better as well where we dive deep into some of these workflows as well. Um, so kind of centering us I talked about this loop. I think we all kind of get the picture of I have an app. it's traced. Those traces flow in. And then in addition to the trace data, uh whether it's signal, whether it's your own coding agents, having trace data, which is the source of truth for what your agent is doing, as well as
26:20 your actual repo data, which is the code that defines what your agent should be doing. Um you get some really really interesting analysis, um and the ability to actually make improvements at scale. Um, I think one thing that we didn't talk about yet that I want to get into is sort of how do we evaluate agents using agents. So, this is kind of like an example of pull the traces, you know, look at my code, figure out what's wrong when you want to have sort of this more adaptive analysis.
26:47 We tend to think of it as harness as a judge. So, I think raise your hands if you guys are familiar with LM as a judge, loosely speaking. Cool. Awesome. I think pretty much everyone in the room should be familiar with that at this point. um you know very common pattern you feed in an input your LM as a judge reads the input uh whether it's a summarization task or a hallucination eval whatever it is it then responds with a set of rails um on how well uh the sort of the agent did on the response what we're really moving
27:17 to is harness as a judge and this is for a few different reasons I think firstly LM as a judge is really a fixed rubric so when you have a use case in mind and when you have an eval file that you know you want to score on. It works really well at catching those known issues. However, like I just kind of explained with the price filter, you don't always know what those issues are going to be like before you hit production.
27:41 And so harness as a judge is a way to do that adaptive analysis. Um so observe what the agent is actually doing and adjust what you actually test as the agent goes. So the criteria kind of emerges from the trajectory. um was this the right tool to pick? Were the arguments valid? Was this grounded in the right context that was handed off by another agent?
28:05 For example, this idea of that unknown unknowns and that price filter was an example where I wouldn't have written maybe a test case for that. Um but an harness as a judge is able to kind of do that adaptive analysis for me with the trace analysis that it has in it. Um, so this is sort of like verifying in plain language. You're not column mapping. You're not writing boilerplate, you know, sort of eval templates and and text.
28:33 The rubric is really just a sentence and it reads the actual trace tree directly. Um, then runs in a sandbox against those real traces. Um, and so I think it gives you a bunch of, you know, tools on top of that because it also runs in an environment. So, one example that I see from a lot of customers is around this idea of access checks. Um, an LM as a judge might be able to read an input and read an output, but because it has no data outside of that unless you paste in every user in the system into the system prompt,
29:05 it doesn't actually know or have context on what that user should be doing or should have access to. Whereas a harness as a judge because you can connect it to external systems, it can then go make a call to actually say, "Hey, is this user allowed to do this, does the trace reflect the access that they had?" Um, and so harness as a judge is essentially moving from just a foundational model call that scores an input and then comes out with an output rail to a more agentic flow where you can actually call tools um and
29:34 and sort of do that unknown unknown catching as well. Cool. Um, so I think we we kind of talked about uh harness as a judge. Um, how that would have worked in this scenario is scoring the fix to fail and and actually deciding whether to pass the example or not. So for example, pass of every return products price is within the requested min price max price range.
30:09 Fail for any result exceeds the budget. Um and it's actually able to go into the code and check that those filters are working correctly. Um and this rubric now will catch this on every subsequent run and not just uh you know an individual test case that you've coded for before. Um, so quickly checking in, we talked about the detect, investigate, and ship the fix.
30:32 Um, shipping the fix part is kind of what I want to talk about now with agent experiments. So maybe we've found an issue, we've grounded that in real trace data, we've investigated that, we know why it's wrong, and we've decided that we have a fix worth shipping. Before you ship the fix, you probably want to test this on a set of results. And I'm not sure how you guys are doing this today.
30:56 Maybe you're deploying to a dev environment and then manually testing. Um what Aris has come out with is agent experiments that allow you to test that in an automated way via CI as well. And so uh I don't think any of us are strangers to the concept of you know data sets of failure cases that we collect over time. Um what you can do with agent experiments is you can actually stand up that dev agent behind an endpoint and hit that with a data set directly from rise and that data set will be actual traces that your agent
31:28 has failed on. So in the previous example I kind of showed you uh the wonder toys where maybe I have a few traces asking for um birthday gifts under $30 that actually failed. um what I can use arise skills to do is actually go create that data set. So I have like call it 25 examples of budget constraint searches that failed right and then I ran the workflow with claw code uh I did some trace analysis I identified the bug in my code and I have a fix ready as a PR or maybe signal did that all for me in the background.
32:01 How do I actually go hit my agent with that data set to make sure that the proposed fix is actually validated? And that's sort of what agent experiments allow you to do. With agent experiments and the agent playground in the product, uh you can actually run against that fixed version. So you deploy it to dev, it stands up behind an endpoint, you stream sort of the uh data set inputs into that agent and the agent actually responds and then you can score it with the same judge rubric that you have in production.
32:31 Uh so then you can compare kind of side by side what the agent in dev mode is doing with the fix versus what the agent in production did and look at whether you're actually catching uh any of those edge cases or not. So, similar to kind of like other data set experimentation workflows, what you want to do is you want to look before the fix and after the fix, look at your judge, which is in this case the price correctness judge.
32:57 Um, look at your examples and queries. Look at whether you know maybe latency is increasing, cost is increasing, so you have sort of a view of those operational metrics as well. Um, and then quantify the results of your experiment. Um, and then you know maybe alluding a little bit back to this idea of the self-correcting loop as a full storyline, you now have you an agent that's operating autonomously on your traces looking to see if issues are arising.
33:25 So, you know, I can see that the user asked for under $30. The response was clearly over $30. Investigating why that's happening with reference to a codebase. putting up a fix in the form of a PR for what how it thinks it can improve that. In this case, it was a filtering error and then because our experiments are actually exposed via skills and API as well, the agent can actually redeploy itself on the new PR and verify itself against a data set of actual failure cases before it even notifies you or decides that it's
34:01 worth your human review. So you know this full loop happens completely autonomously. The agent will actually go collect the failures, create a data set for itself, run it against the new version of itself and prepare a response back to you that says, "Hey, I think I found this issue. I investigated it. I know why it might be happening. I pushed a PR.
34:22 I then ran that PR in dev against the data set of failures I found. and I saw you know um in this case a 36% increase in performance on this specific evaluator and all that information to you is available to you in the PR itself for you to then just go read validate the code do some code review and ship um so I think this makes it like you know kind of coming back to this idea of the loop um I think it makes it really really powerful to go you know full circle where you have the detect investigate hypothesize and fix
34:59 loop and then you also have the validation loop right so how do I actually know that this fix is correct and it's not just you know an agent in a sandbox saying it's correct how do I actually go test that on real failures and real uh edge cases from my working example um and how do I actually you know start to stand that up and auto test that for me um and so I think you know we'll we'll come back we'll we'll have some time as well to kind of help you guys around uh in the rest of the workshop if the Wi-Fi is doing a
35:29 little bit better. Uh but I I'll go back to the slide that was previous um so that you guys can kind of walk through this yourselves. One, getting the tracing started. If you're not collecting tracing, nothing else works. Um takes subsecond sub 10 minutes on on your own agent. We have instrumentation skills. We have support for all the big agent frameworks, all the foundational model providers.
35:52 um enabling signal and going deep uh or using claw code to actually use the arise skills to do that investigation for you. Uh verifying it with a harness as a judge and then measuring that with agent experiments. So before we end, I will go back to this and we'll we'll all walk around the room and try to help you guys get this set up. Uh if you're having trouble with the M files, um if you just search through the repo or ask a coding agent where the M file is, um it will kind of help you do that.
36:21 Uh other than that, make sure you get clone the repo. Um and you get your API keys from the Arise platform. Uh and then you can actually start to see these traces coming together. Um in the spirit of AIE workshops, I think this really works better when you guys see it and touch it yourselves and actually try to do it. So hopefully the Wi-Fi works and I'll I'll come around and hop around.
36:42 If you do have questions or you're blocked at any point, feel free to ask me. Odds are someone else in this room is experiencing the same issue. Uh, so I'm happy to kind of shout out some advice as we go. All righty. And if you're having trouble installing the skills as well, uh, I have it up on the slides here. Uh, very simple MPX skills ad arise AI arise skills.
37:00 This will give you access to an interactive mode where you can choose what you want to install or you can just choose to install all of them as well. And this includes skills for trace analysis, data set curation as well as evaluators. So as an example of like some of the skills you can run uh I can ask cloud code hey check my traces for any errors or anything I can fix cross reference with the code for the agent in this repo and use the agent skills and uh we will be sending out kind of a recording of this as well as
37:36 all the resources you need to kind of get started with this. So apologies for the internet if it doesn't work out today you guys will be able to get up and running very quick as well. All righty guys, we got to close up uh our workshop for now. Thank you once again. I just want to leave you guys with sort of this detect, investigate, fix, and verify slide uh which kind of covers the throughine of how uh you can get started with Arise and how to create your self-improving agents.
38:02 And then once again, uh feel free to connect with me on Twitter or LinkedIn. Uh and if you want a free year of Arise Pro as well, uh just use the code on the screen. Uh we have a free tier that's always free, free to use. For this conference only, we're also giving out Arise Pro for free, which gives you a bit more of the usage limits uh if you guys are running agents with, you know, thousands and thousands of traces.
38:24 So, thank you guys so much. We're running another workshop at 2 p.m. uh led by Lori that'll go into a few more code examples. Hopefully, the Wi-Fi is also a little bit better then and yeah, we'll get going. Thank you guys. Appreciate you.