If we want them to do Knowledge Work, design them as Knowledge Agents — Benjamin Clavié, Mixedbread

Okay, so hi everyone. I'm gonna give the quickest introduction to myself. I'm Ben Clavié. I work at Mixedbread, where we do retrieval. I'm French, and I live in Tokyo. And today I'm gonna talk, talk to you about the fact that agents should do knowledge work, and so we should design them like knowledge workers. Like, we should design them like Knowledge Agents and not coding agents, and I'm going to explain the difference and why I think that's important. It's a bit of a hot tech talk, but let's start now. So the first thing is, like, first agents give us fun trivia, and I'm

talking, like, early agents, twenty-twenty to agents, back when all you had was, you know, RAG, but agents couldn't even do tool call back then. So all you had is, like, you had an if statement, you did search, you got, like, cool, you could talk to your PDF. That was the very first form of agentic work. It was not very useful. We're not gonna talk about that for long. What came next was programming agents, and that's been all the rage. Like, once agents started being able to search, actually properly search, actually properly understand things, carry tasks out, call tools,

we started designing coding agents, and coding agents are a big thing. I don't think there's anyone in this room that does not use coding agents. We all use CodeCard, Codex, et cetera. And that's a form of knowledge work. But agents were not knowledge workers at the time. Like, agents were coding agents. And now they're becoming knowledge worker. And by knowledge worker, I mean that knowledge work is a big superset. And kind of every workflow you've thought of before is a form of Knowledge Agents just because of the nature of knowledge. So it's like, if you have an agent

that's a lawyer that's looking for legal documents, if you go financial agent, if you're looking for, you know, medication information, like you've got a lot of people on Twitter that try to do those self-diagnosis, and you've got just a lot of medical usage. All of that is coding agents. We're getting an agent. It's trying to find-- Sorry, Knowledge Agent. It's trying to find knowledge, trying to make use of knowledge. And coding is part of that, of course, and even the small RAG bit that we talked about, like, in the first slide is part of that. But that's a very, very small proportion

of the actual full thing. Like, there's so much more to knowledge than any one domain. And what even is knowledge work? 'Cause I'm saying that it's important. They do knowledge work. Coding is knowledge work. And I think there's two ways to define it, in my opinion. One of them is knowledge work is work where your main input is information. Like, your main input is not an actual physical material. It's not something that you can touch. It's knowledge. It's information. And the nature of knowledge work is that you process this

information, which is by nature very ambiguous, very diffuse. And the main output you get from that is something actionable. It's a judgment. It's a decision. It's some-- If it's a lawyer, you're gonna get, you know, their findings on your case, and they might plead for you. You're going to get an actual actionable thinking item, like something still not tangible, but that exists. That's knowledge. There's also a tautological definition which makes sense here, that if you need search, it's a knowledge problem, and if it's a knowledge problem, you

need search. So it's very self-defined. And in the real world, that's basically most of the work that we see in the service economy is a form of knowledge work. Like lawyers, knowledge workers. Academics, knowledge workers. Actuaries, knowledge workers. Software engineers, researchers, also knowledge workers. And the fact that there's so much knowledge work in society has contributed to, like, an ever-improving structuring of knowledge work. Like, there's actually very, very well-defined workflows for how we should do knowledge work, for how knowledge works in

itself, and how we evolve that. But so far, agentics kind of focus on the special case, and that's try to generalize from it, and that special case is coding and software engineering. And the thing is, code is knowledge, but not all knowledge is code. And code's a very, very unique form of knowledge because it has very durable cues. Like on-- in a code base, there's gonna be a lot of references to, like, an identifier or a file or a path. And okay, when we vibe code, that can change, but most of the time it's not going to change all that

much. Like, all the things are, like, very, very durable. Then you've got that surface obviously is, like, now in grepable. Like, there's keywords, there's method definition, there's a lot of things that by definition you can grep in code. And the task-- And this one's actually very important, and we don't talk about it a lot, but people are like, "Oh, why is grep good enough for programming?" Or, "Why can an agent do programming?" And then you're telling me it can do, like, deep research for a legal question. And one of those is because we don't realize it, but, like, when we interact with coding

agents, we are giving them extremely narrow tasks. Like, we're not actually expecting that much from them. Like, everything's always kind of about a feature or about, like, a given ticket. Like, there's a task at hand. You're not going to tell the agent, like, "Discover a new programming paradigm and then implement it in this new app." I don't know what it's gonna do. Good luck. But in knowledge work, that's often the case. Like, first of all, you don't have those durable cues. Like, the meaning is always implicit. And more importantly, the same cue can mean a lot of different things. We don't

have function definitions in knowledge work. Like, if you see thirty days, if your agent's looking for thirty days, is it a deadline? Is it a grace period? Is it a retention rule? Is it even in the same domain? Like, are you searching for thirty days on a contract and you're getting medication? There's a lot of contextual information, yeah. But more importantly, the search starts from an intent. Like, even if you're doing, again, legal example, if you're asking about, like, a specific rule that you want to apply to a specific domain, you're going to need to look at the international norms that apply, and then do they apply in

this case? There's a lot of conditional information that is not predefined in the task. Like, that's all up for the agent to find. And so non-code knowledge is very contextual and meaning-driven, which is much harder than code. And that's led to the fact that none of what I'm saying is new. Like, people have been doing knowledge work for a very, very long time, and that's resulted in, like, two endless loops. So you have a tool loop, which is... At the start, we were, we were, like, talking. Then at some point, some guy was like, "We

should write stuff down." Then in Alexandria, we had the Pinakes, which was the curator of the Library of Alexandria, came up with an idea that maybe we should have a way to catalog all of the books we have. Then we developed writing, then we developed bibliographies, then we ended up with, like, the current version of the, like, Dewey system for libraries, and nowadays we have search engines. But we also had an organization loop, which is joint, but also disjoint from the tool one, which is it used to be the one gifted expert. Like,

we've all heard of the polymath of the past, the person who just knew everything about one domain or all domains, and you just went to them if you had information. But that doesn't scale, so we ended up with, like, monasteries, which were like guardians of knowledge, and then we had universities, and then we ended up creating the bureaucracies, and now we ended up creating the modern organization of work where we have very specialized firms. Like at hospitals, you've got the doctor, you've got the senior doctor, you've got the nurse practitioner, the nurses, the healthcare assistants, and all of them kind of like specialize on different levels of tasks, and that's

a really good form of optimization. But the thing is that it's actually just the one loop. Like, I'm showing two loops here, but they're actually just the one loop, which is we have new knowledge, and new knowledge means that we need better tools, and better tools mean that we end up creating new workflows, new roles, like we need people that are trained to use those tools, people that understand what the new tool does. If you have a guy that knows how to go to the library and you're like, "Okay, use Google," you need the knowledge of what Google is. Like, that person needs to be taught

that's a search engine. You can just type stuff in it. There's no need to physically go there. And that means you retrain. You get new knowledge workers who are more efficient, so they create more knowledge, so we need new tools, and so on and so on. So both the tool loop and the organizational loop are actually just this one self-optimizing loop that kind of like triggers the other endlessly.

And the thing about, like, tooling and optimization is that they're not neutral add-ons. Like, I said that we keep optimizing tools and things come up and we create new things out of those tools, but that's never actually a neutral thing. Like, tooling is not just, "Oh, my search is 5% better." The fact that we have a tool or the fact that we don't have a tool is what decides if a task-- not if the task is possible, because you can do things without the right tool, but if the task is actually scalable and can be carried out cheaply, because something being cheap means it can scale. And it's like, yes, of

course, if you go to the Library of Alexandria before the Pinakes, you can find your manuscript somewhere. Like, whatever you're looking for is there. It's probably going to take two or three weeks. So you're going to really, really, really need that knowledge. But if there's a library catalog, it's gonna take you ten minutes, and now, like, it's way easier to just, "Oh, okay, I need to know something more about this, so I'm gonna search for it." Likewise, if you have a map directory or if you even have a map in the first place, which in itself is a tool for information, then exploring the world is a much better idea. Like, you're not gonna rely on

randomly discovering America on your way to the Indies. You know, like, you know where you're going. And likewise, if you have, like, a multimodal search platform, then you can search millions of PDF in a way that we couldn't before. So now there's a lot of use cases where you are like, "Oh, it's in the archives. I'm not gonna touch that," that become actually useful.

And in practice, this kind of looks like that, and I'm getting into the more technical stuff here, which is on a simple deep research task. So this is the Brass Compass leaderboard, which is made to evaluate the quality of search tools on a very bounded deep research task. You have two hundred thousand documents and you have, like, specific queries. Yo talked about this this morning, and it's a really useful benchmark to, like, analyze queries. And what we see is that, like, okay, a bad tool. So that's the thing that people often rant about. You will see that there's two BM25 here. There's two,

like, optimized and unoptimized, and that's because quite often people will tell you BM25 is not great. And the reason they'll tell you BM25 is not great is because there's not one BM25. There's hundreds of them. It's a way to do lexical search. You should always optimize your baselines. You should always, like, optimize what you're getting. And so what you see here is, like, a badly optimized tool is useless. Like 60% accuracy. You're not going to trust someone that's right 60% of the time. You're just going to do it yourself. When you start optimizing the tools, you can see we go up to seventy,

eighty, and then the best, the actual best is Hybrid Harness. It gets to ninety. But that's maybe not the most interesting part because we start kind of plateauing at one point. Like the jump from 88 to 9.8 to 90.2 is in run variance. That doesn't matter. What matters here, however, is that 90.2% accuracy, you reach it with 20% fewer tool calls, and that's huge because in practice, that's 20% fewer tokens, 20% less fewer resources that you use. That's basically 20% free

cash. And if you compare it to the unoptimized baseline, you're spending, like, 5% of what you were spending in the first place. So the tool is actually what makes the task worth doing. Nobody would keep using that tool if it takes 25 calls. But if it takes 8 calls, we're like, "Oh yeah, cool. That's a workflow I can introduce."

And the second part, which goes with tooling and I think is just as important because Brass Compass in the previous slide is interesting, but it's easy. It's a hundred thousand documents. It's just text. It's just the one question. And it's not really that open-ended. It's just a bit convoluted. But when you're actually doing, like, real-life knowledge work, there is that workflow that you see that I tried to doodle, which is you have a client. They come to the big shot. They come to the lawyer. That's the partner of the agency. And they're like, "Okay, this is my situation. That's my problem." And they meet together. But

then what the partner does is they're not going to be the ones doing all the legal research. They're not going to be the ones, like, doing every single step of the problem. What they'll do is kind of understand that, like, okay, this person is, has this problem. That's gonna cause them that. Those are the facts. I need the relevant laws to this, that, and so on aspects. And then they've got paralegals, they've got assistants, and the assistants are going to be doing this research. They're going to be using, like, the same sort of tools they've been trained to use, and they're going to produce memo and notes, and then they're gonna give that back to the big shots that

maybe they research, like, one clarification point, but they mostly rely on what their searcher agents, if you want, like the assistants have found for them. And that's the response that you're gonna get. And that's echoing the point I made before, which is in code, when you're using code code, you're kind of doing that work yourself. You've already broken down the query, you know, like, you know what you want to do. You've got a linear ticket, you've got something that you're giving the agent. In the real world, you've got a client that's got a very open-ended problem, and you need to break it down yourself, and your agent needs to break it down itself, and then needs to use sub-agents

that do this research.

And this is how better tools and organization work together because this one is MatQA, which is another form of knowledge benchmark. MatQA is something that Hugging Face and Snowflake jointly released, and it's PDF-based enterprise task. It's got PDFs, and it's got OCI, OCR versions of the PDF. And the current state of the leaderboard really show that, like, both tools and organizations are necessary. And you can see that in the fact that with BM25, however optimized it gets, the

human and Gemini 3 reached the same ceiling. And that doesn't mean that Gemini 3 is as good as a human. That means that even a human cannot get the right information given unlimited searches with BM25. So you have the tool ceiling, and you need better tools to go forward. That's the, like, tool optimization part of the loop. Thankfully, we've got better tools. You know, we've got, we've got models that can handle PDFs. We've got Vision. We don't need to rely on OCR text. And what we see with that is that we get another jump, which is Gemini and the Mixedbread

search tool, which is fully multimodal, so it can read the PDF. You get the tables, you get all that nice stuff in your search, and that gets us a big jump in accuracy. But the interesting part about, like, that doesn't work. The-- Well, that doesn't work. That does work, but that doesn't work as well as we would like because why is my agent getting eighty-eight point nine if the human's getting ninety-nine point four? Like, that's ten percent I'm leaving on the table here. But that-- I don't understand. That's an agent. It gets, I think, ten turns in the benchmark, so it's a fully agentic system. It gets to think about its results, and yet it's missing

performance. And that's where we introduce the Mixedbread search agent, which is exactly that breaking down of work we saw earlier, where we basically tell the, the main agent, the one answering the question, being like, "Okay, that's, that's a big topic. There is, like, thousands of PDFs. You're not gonna, like, search yourself. Just please break down the problem for me. Please write queries about the aspects that you think are important to answer the actual query." And we get searcher that go off on their own, and they find the right results, and they bring you, like, a little memo to your agent, and then your agent actually answers that. And

that gets the accuracy up by three point five points. And that doesn't sound like a lot, but I like to think of it as, like, an oracle gap. And the oracle gap is the difference between perfect documents and your search system. And the oracle gap here is about, about ten points before using the agents, and it goes down to six points after using the agents. So that means that we have about a forty percent reduction in mistakes. Like, the gap between humans and agents goes down by forty percent just by, like, having a better architecture to search for it.

And I think this is the end of my slide 'cause I'm running out of time, and that's perfect 'cause that's my takeaway slide. And what I want you to, like, get from this talk is that we know how to design better knowledge work for humans, and AI agents really benefit from this pattern. Like, we have designed this. We know how to do this. Humans have worked on this for centuries. Like, people have always needed more knowledge. The empires used to have librarians. We have paralegal. We've got legal firms. We know exactly how legal, the legal industry has figured out paralegal. The medical industry has

figured it out. And none of it looks like programming. Programming is a very different system because it's a very specific use case, and we should really learn from the knowledge world to know how to design agents that will do work for the knowledge world. And then you must not, like, overfit on tools because tools don't exist as a way to do things by themselves. Tools exist as a way to overcome ceilings. You want a better tool when you see that you're hitting a ceiling, that your performance is not where you want it to be. So we design better tools to overcome the

ceilings. And more importantly, the tools need to be co-designed with the agents. Like, the agents need to know how to use tools because one thing you would often see is agents will try to write grep queries because grep's everywhere in the training data. BM25, they will end the query in the data, and that's not always what you need. Sometimes you need semantic search of a PDF, and you can't grep a PDF. You can't BM25 a PDF. You need to, like, write a better query. So it's very important that your agentic harnesses or even your agentic models know that they have got more than one tools, and it's

about primitives. Grep's a primitive. BM25 is a primitive. And semantic search is a primitive. And all of those need to be, like, very well-trained. Like, the models need to know about all of them. And the last one is that the right orchestration of search will get you much better results because context is a finite resource. And even if we get to a model that's got, like, a hundred-million token context, A, that's gonna cost you a lot of money, and B, that's still nothing. You're not even getting half of, like, one state's legal

code, let alone the US, let alone international law, let alone specialized course, et cetera. So you need to have a way to break down your task, and you need to have your orchestrator or your main agents and people that can actually organize the knowledge for them.

And yeah, cool. So we've got two minutes for questions.

Thank you.