# Beyond Conversation: Why Documents Transform Natural Language into Code - Filip Kozera AI Engineer · 2025-06-10 Filip Kozera argues that chat-based interfaces like ChatGPT create ephemeral, polluted context windows and lack forced clarity, making them poor for specifying complex systems, while documents naturally force rigor and structured thinking. He advocates shifting from conversational brainstorming to document-driven workflows, where humans precisely articulate intent for AI agents. Kozera introduces background agents that run asynchronously, triggered by events like emails or meetings, and surface only when needing human approval, turning the human role into one of 'swiping left or right' to approve or edit outputs. He envisions a future where humans manage swarms of such agents, first adopted by prosumers and later by enterprises, with taste and intent becoming critical for imbuing personal brand into agent outputs. The episode presents a clear progression from chat-based brainstorming to structured document-driven agent systems. ## Questions this episode answers ### Why does Filip Kozera argue that documents are better than chatbots for specifying complex AI systems? Filip Kozera points out that chatbots cause context pollution, lack structure, and force you to be present (concurrency of one). Documents, he argues, are the original tool for specifying complex systems like product requirements. They force clarity and rigorous thinking, enable structured iteration, and support background agents executing tasks concurrently. This shift from ephemeral chat to document-driven workflows is crucial for building reliable AI systems that can work unattended, with human oversight only when necessary. [1:56](https://aie.addtry.com/8befffbf-864a-4c30-8ba4-015639ac0aeb?t=116000) ### What are background agents and how do they work according to Filip Kozera? Filip Kozera describes background agents as AI systems triggered by events like emails or meetings, working asynchronously without your constant attention. They handle repeatable processes, and a human-in-the-loop approves or edits their output. Over time, trust grows as the user refines results, swiping left or right to accept. He envisions prosumers adopting them first, then enterprises, with agents eventually managing other agents or even creating Jira tickets, forming an 'agent economy' driven by documentation rather than chat. [2:59](https://aie.addtry.com/8befffbf-864a-4c30-8ba4-015639ac0aeb?t=179000) ### According to Filip Kozera, how will the future of work change with AI agents? Kozera predicts a 'swipe left or right' future: users will primarily approve or reject the work of background agents, much like Tinder. After creating a document specifying the agent's task, the agent runs triggered by events. Users oversee swarms of agents, ensuring outputs match their personal taste and intent. He sees prosumers leading adoption, followed by enterprises where agents may manage other agents or generate Jira tickets, transforming work into managing AI rather than doing tasks. [10:04](https://aie.addtry.com/8befffbf-864a-4c30-8ba4-015639ac0aeb?t=604000) ## Key moments - **[0:00] Introduction** - **[0:19] Chat Flaws** - [0:19] Chat-based systems fill the context window with gibberish, causing context pollution, says Filip Kozera. - **[1:56] Documents** - [2:17] Documents inherently force clarity and rigorous thinking, unlike chatbots, says Filip Kozera. - **[2:59] Background Agents** - **[4:21] Human-in-the-Loop** - [4:21] Human-in-the-loop approval allows agents to tackle higher-importance tasks, according to Filip Kozera. - **[5:08] Agent Protocols** - **[5:49] Agent Economy** - [6:07] Agents managing humans will create Jira tickets for engineers, predicts Filip Kozera. - **[7:13] Managing Agents** - [8:22] Developers who excel at both IC work and managing interns benefit most from AI, says Filip Kozera. - **[8:37] Repeatable Workflows** - **[10:04] Enterprise Adoption** - [10:04] "Our main job is to swipe left and right as if it's Tinder and approve and edit the results of the work of the agents." ## Speakers - **Filip Kozera** (guest) ## Topics Agent Skill Design, Multi-Agent Orchestration ## Mentioned OpenAI (company), Wordware (company), Zapier (company), ChatGPT (product), Claude (product), Gmail (product), Jira (product), MCP (product), Manus (product), Notion (product), Slack (product) ## Transcript ### Introduction **Filip Kozera** [0:00] Hi, I'm Philip, and I'm the CEO at Worldware. Today I want to talk to you about what sucks about chat-based interfaces, how documents can actually solve those issues, and how they lead to, um, background agents that do tasks for you in the— well, in the background. ### Chat Flaws **Filip Kozera** [0:19] So, firstly, let's start with: what are the problems with chat-based systems? When I interact with Claude or OpenAI, it all seems very ephemeral. I end up often creating workflows for myself using projects or just copy-pasting stuff. And in that way, when I'm kind of having these long conversations that I populate the context window, I realize that a lot of these things in the context window are just, like, gibberish and garbage, and so we get context pollution. I also don't get to iterate in a structured manner. If I'm working with artifacts, it ends up that I basically make the context window dirty enough with not being able to, you know, change one sentence that I really wanted to make sure that it's precise. I also sometimes lack another level of forced clarity. ChatGPT often asks me that one particular question around, "Oh, in deep research, what would you like to actually find?" but it never actually asks me in theright way when it's not actually certain about some things, and therefore not forcing me to clarity. Also, there's a couple more issues about poor version control, limited reusability, model laziness. The more the context grows, the less, you know, the worse response I'm getting. Also, chat interfaces don't support any logical grouping or nesting in any way. ### Documents **Filip Kozera** [1:56] And we also are interacting with a single abstraction layer. We don't get to see and choose whether we want to specify every small detail of a particular task or just set it up in some way. Hence, documents. Documents are actually the original way of specifying more complex systems. The first, um, product requirements doc that dates probably to Noah's Ark around 3,500 years ago— don't check me on that one, however— and it's the first kind of take on someone explaining a more complex system to somebody who is not aware of how to build it. And therefore, documents are actually the ultimate way of humans communicating these more complex ideas. And so in that way, we get forced clarity, which is great. But next, the problem with chat, and one of the biggest problems with chat, is concurrency. We have concurrency of one with all chat-based systems. We need to be sitting there, and we are getting, like, the inklings of how the future will look like when Manus or Deep Research are running in the background and actually, you know, doing things for us. ### Background Agents **Filip Kozera** [3:16] It feels great. It feels great that there's something in the background that's happening. So now, you know, let's riff off this idea of the background agents that I'm going to introduce. So we've been doing work, and computers have been doing work for us in the background for quite a while. And, you know, we've kind of created workflows which are kind of handcrafted. You can think of the Zapier of this world. And then we're just barely starting to create specialized agents. That basically means that at some stage it kind of had an if-else statement that was somewhat fuzzy, and it made one or two decisions. And as we can see on this diagram, when the importance of some workflow is high and the occurrence is high, we actually end up using handcrafted workflows. We're only now entering an area where the general agents are starting to take some decisions, but whenever we have kind of higher importance, we don't let that general agent to, um, kind of enter our life. So how can we remedy this? We remedy this by introducing a human-in-the-loop. So essentially, now, with the human-in-the-loop, the agent can do a bunch of work, and we get to approve, reject it, change the way that it's created the end output, or even fix its logic entirely. ### Human-in-the-Loop **Filip Kozera** [4:44] They normally react to some kind of implicit or explicit user intent or trigger. So you can think of these background agents as being activated by a sent email, sent Slack message, maybe your meeting. That could be an implicit trigger that you had a meeting with a named party, and that party with investors, and that could prompt you to update your CRM. And with having a bunch of these ambient agents, you end up having to create protocols of how humans and the AIs communicate between themselves. And that basically means that, you know, both humans can control the agents and its outputs, but also different agents can communicate with each other to educate themselves around, you know, sources of data. ### Agent Protocols **Filip Kozera** [5:33] An agent, one agent could be communicating with a more general agent around what is in your notion. And in that way, we'll probably start with a prosumer first, where a bunch of agents are working in the background, but very soon that starts to be about an organization. ### Agent Economy **Filip Kozera** [5:49] And we'll start having organizations which have their own agents and also external agents. And in that way, we might even get agents which manage other humans, which sounds ridiculous at the beginning, but actually, you know, that could be just an agent which creates Jira tickets for all of your engineers. And in that way, we basically create the graph of the enterprise of the future. I think when I think about these background agents, firstly kind of working for the prosumer, maybe managing your emails, and just making sure that you have more time for yourself, I think this idea, like, so naturally represents a bottom-up movement to enterprises, which will be more slow-moving and trying to make sure that all of their agentic tools that these agents need to be using are verified and have theright authority and they have theright permissions and they don't mess things up. So as I am thinking about the future of the agent economy, I think that the stochastic mindset of, like, we need to adopt that because we essentially need to lead with leverage over uncertainty. If something that you don't fully get how it works closes you clients and delivers on business value, 99.9% of the time we're not going to care about the fact that we don't understand what's happening in that 0.1% of the time. We're just going to make sure that the impact of the 0.1% of the time is not catastrophic. ### Managing Agents **Filip Kozera** [7:22] I also think humans will manage a bunch of agents, and that's why taste and intent is so important. You will need to imbue your own personal brand onto agents and take responsibility for their actions. We'll also need a lot more communication protocols between humans and AI, and also in between agents. The MCP is the first protocol that kind of sets it up, but I think it lacks more information about what are the constraints of a particular agent, what are the authority that it needs to have in order to act, whether it needs approval from human-in-the-loop, etc. Right now, when I think about, like, kind of humans managing agents, we only see this properly in coding. And in coding, we basically see people who are good engineers, who are good both at IC work and at managing a team of interns, really, really, really being able to take the benefit of the AI revolution. A lot of excellent IC engineers end up saying, "Oh, I don't want to use AI. It's actually not that good as people are saying." And it probably isn't for them, but their bar for code might be too high. They might want to have everything optimized in theright manner. And in this way, you know, this is the first time where engineers are kind of managing the swarm of agents, and they need to be good at managing in order to actually distill ### Repeatable Workflows **Filip Kozera** [8:53] leverage and distill benefit for their organization. So just to wrap up, I think the concurrence of one of chat-based systems and the pollution you get from playing with them, it almost is like brainstorming. But after brainstorming, you need to sit down and create aright document to explain what an agent should be doing. This is only needed for repeatable processes. Once you have a repeatable process that you trust and you think will be very useful, you can hook it up to a trigger. That can be either, you know, a cron job, or it could be a Gmail trigger, or it could be an implicit trigger. Then that agent is then able to act in the background. The latency matters a little bit less there and only surface issues to you once it is struggling with something or needs your approval. Therefore, your work is mostly around creating these assignments for the agents, making sure that your taste is imbued there, and then approving the results of the work, making sure you trust it more and more and more as you keep going. In that way, we are creating swarms of agents which are working in the background, and our main job is to swipe left andright as if it's Tinder and approve and edit the results of the work of the agents. ### Enterprise Adoption **Filip Kozera** [10:18] I think from there, the prosumer market is going to adopt this much more widely, and we're going to see it slowly entering the enterprise market. And I'm super excited about the enterprises creating most incredible tools that are going to be agentic, and they are going to be used by the state-of-the-art newest models, but the tools are going to be the mode. So a very clear progression for the future. Let's see if it's true. Thank you so much. I'm Filip, the co-founder and CEO of Wordware. And at Wordware, we actually enable these background agents to work. Come build yours. --- This library is powered by PodHood (https://podhood.com), the podcast website platform.