Agent Swarms Is One of The Most Powerful AI System Yet

Okay, so I've been going down a bit of a rabbit hole this week and it genuinely shifted how I think about where AI is heading. There's a system from Abacus AI called Agent Swarms, part of their chat LLM platform, and if you haven't looked at this yet, you probably should. Because what this thing produces in a single session is the kind of output that should be making SaaS founders, enterprise software teams, and honestly management consultants pretty uncomfortable. The core mechanic is this. Instead of one AI model grinding through a complex task linearly, [music] you have a master agent that takes your prompt, understands the full scope of what you're asking for, breaks the whole thing into subtasks, maps the dependencies between those tasks, and then deploys a set of specialized worker agents to execute them. Sometimes in parallel, sometimes in sequence, depending on what needs to exist before the next thing can start. It's a hierarchical multi-agent architecture and there are six demo videos from Abacus AI that show what this actually looks like when it runs. The first one throws Agent Swarms into a serious build right away. A user asks for a full supermarket management system with both a web app and a mobile app. So this is already the kind of job that usually turns into a long project with a backend, >> [music] >> interfaces, business logic, data flow, all of it. What makes the demo interesting is how the system reacts before the build even starts. It reads the request, understands it is dealing with something much bigger than a one-shot coding task, and then sets a plan that actually makes sense. The web app comes first because the mobile app depends on the backend APIs from that web layer. So before you even get to the output, the system is already thinking about sequencing. From there, the first worker starts shaping the core platform. It creates the structure, sets up the backend, builds the authentication layer, prepares the database, and starts forming the main business modules. Then, once that part is in place, another worker picks up the mobile side and connects it back into the same system. That's where the whole thing starts landing properly because the mobile app doesn't feel like something bolted on later. It feels like a natural extension of the same product. By the time the demo finishes, you're watching a supermarket platform that actually feels usable. There's a live dashboard, inventory and supplier tools, point of sale flow, product views, [music] and a companion mobile app that plugs into the same backend and shows real-time information. The reason that demo works is not just because a lot got built. >> [music] >> It works because the build has shape. One thing leads into the next and the whole thing grows in the right order. Then the next demo pushes the system into an even trickier kind of product. A user asks for a Notion-like workspace app, which sounds [music] simple until you really think about what that means. Products like that live or die on flow. The editor has to feel connected to the rest of the app. Structure matters, navigation matters, state matters. The moment any part feels separate, the whole experience starts feeling fake. So again, the system looks at the scale of the request >> [music] >> and makes a call. It splits the work into the main web product and the mobile companion. That sounds obvious on paper, though the way it plays out is what makes it interesting. The web worker builds the main app with the editor, authentication, storage, version history, all the core product logic. Then the mobile worker comes in and extends that same world into a React Native app tied into the same backend. And then you see the user move through it. They log [music] in, create a page, paste content into it, shift over to mobile, start adding entries with statuses and due dates, and the whole thing stays coherent. The data carries across. The experience carries across. You don't get that weird feeling where one side looks polished and the other side just exists because [music] it had to. It feels like one product moving across two platforms and that continuity is what gives the demo weight. Then the HR platform demo takes the idea further because now the swarm is not just handling two connected [music] pieces. Now it is juggling three work streams at once and they all have to land together. The request here is a full HR management platform with hiring, onboarding, attendance, payroll, reviews, [music] leave management, employee self-service, analytics, and a weekly report that gets emailed every Monday morning. So now the system has to handle business software, employee workflow, and automation all inside the same job. The master agent responds by breaking it into three clear tracks. One worker takes the main HR portal. One handles the employee mobile app. One handles the reporting system. What makes this demo work is the way those tracks move without drifting apart. The portal becomes the company-facing side of the product. The mobile app turns into the employee layer where staff can clock in, request leave, and check payslips. Meanwhile, the reporting worker writes a Python process that pulls from live company data and generates the weekly HTML report. So the output doesn't feel like three separate assignments. It feels like three moving parts inside one coordinated build. And when that report runs successfully in the terminal, it lands in a satisfying way because you just watched everything line up around it. The company platform exists, the employee app reflects the same system, and the reporting layer is drawing from the same source of truth. >> [music] >> That's where the swarm idea really starts making sense. You are seeing coordination, not just generation. Then the whole thing shifts into a completely different kind of work with the McKinsey replacement demo. Here the goal is not to build software. It is to do serious research and turn that research into something a board could actually review. The user asks for independent analysis on how AI can improve productivity across seven enterprise functions with quantified ROI, real-world case studies, risk analysis, and a boardroom-ready presentation in the 20-to-30-slide range. That kind of request usually turns into a mess fast if one system tries to brute-force the whole thing. There are too many threads, too many angles, [music] too much material that has to be organized well. So the swarm handles it the way a real team would. It sends out seven research agents in parallel, one for each business function. Then a synthesis agent pulls those findings together into one executive document. Then a presentation agent turns that into a polished slide deck. And the interesting part is how grounded the research process looks while it is happening. One agent is searching around operational AI use cases, integration complexity, adoption risks, ROI evidence, >> [music] >> forecasting, manufacturing examples. Another is working its own lane. The research has direction from the start. By the end, the output feels structured enough to be useful. Executive summary, maturity heat map, ROI comparisons, roadmap across different time horizons, governance framing, board-level conclusions. That's where Agent Swarm [music] starts feeling bigger than a coding tool. It starts looking like a system for coordinating knowledge work at a serious level. Then the fintech startup demo adds something else to the picture because now product feel starts mattering just as much as technical execution. The user wants a personal finance ecosystem with a web dashboard called FinFlow and a mobile app called FinTrack along with AI-powered spending analysis, anomaly detection, [music] savings goals, recurring expense detection, forecasting, multi-currency support, and a modern visual style. [music] They even make one very human request in the middle of it. No purple because language models keep defaulting to purple. That tiny design preference actually says a lot because the system carries it through the whole build. The dashboard and mobile app don't just share functionality, they share a sense of identity. The web side becomes the place where the bigger picture lives with trends, categories, [music] budgets, insights, and reports. The mobile side turns into the day-to-day layer where quick entries, tracking, search, and savings goals make sense. And the whole thing stays visually aligned while doing it. That's important because usable software depends on more than whether the code runs. The product has to feel intentional. It has to feel like one thing. This demo shows the swarm handling logic, design consistency, and cross-platform structure at the same time, and that combination makes it much [music] easier to believe. Then the final software demo brings all of those ideas together in one of the hardest builds of the set, a full CRM. The request includes contact management, customer history, [music] lead tracking, sales pipeline, workflow automation, communication tracking, [music] Gmail integration, Google Calendar Sync, dashboards, tasks, and role-based access for different types of users. >> [music] >> That is the kind of system where weak planning shows up almost immediately because the whole product depends on structure. Once deals, contacts, activity logs, and integrations start touching each other, messy systems fall apart fast. Here, the swarm keeps the whole thing under control. The master agent defines the product, lays out the sales stages, and [music] gives the build a clear frame. The web worker handles the main CRM system with the database schemas, [music] authentication, metrics, dashboards, and core workflows. Then the mobile worker extends it into a field-ready app with contact access, pipeline visibility, task tracking, activity logging, notifications, even an AI-generated app icon to give the product a finished [music] feel. What really helps that demo land is that the code itself looks like something somebody could keep building [music] on. The TypeScript is clean, the mobile structure makes sense, async fetching and pull to refresh are handled properly, navigation [music] is thought through. so the whole thing feels closer to an actual product base than a flashy mock-up. And after you watch all of these demos back-to-back, the main point becomes very hard to miss. The big deal here is the orchestration. That's the part that keeps showing up, no matter what the task is. The system understands the shape of the problem, breaks it into parts that make sense, figures out the order of operations, and keeps those parts moving toward one shared result. In the supermarket build, it understands the back-end has to come before the mobile layer. In the HR demo, it runs multiple connected tracks at once without losing alignment. In the research demo, it sends work out in parallel and holds the synthesis until the inputs are ready. Across all of them, the same pattern keeps repeating. The work stays organized. That's why this starts feeling important in a bigger way. A lot of people still picture progress in AI as one giant model getting smarter until it can do everything on its own. What these demos suggest is a different path that honestly feels more believable. You get a high-level controller that understands the objective, maps the dependencies, assigns the work, and keeps the outputs aligned. Then, underneath that, you get specialized agents handling their own lane, each focused on a part of the job while still feeding into the same overall system. And once that works, >> [music] >> the results start looking a lot closer to how real teams actually get things done. That is also why the AGI conversation starts creeping in around this kind of system. It still does not feel like full AGI. You are not looking at persistent learning across sessions, deep common sense understanding, or some all-powerful general intelligence that can do everything without limits. Though what you are seeing is something that matters a lot, intelligence emerging through coordination, through planning, through specialization, through systems that know how to divide work and bring it back [music] together cleanly. That may end up being the bigger shift. The road toward more general AI may have less to do with one model becoming magically complete and more to do with systems that know how to organize complexity well enough to produce outcomes that actually hold together. And across these demos, that capability is already covering a lot of ground. It can build a business platform with connected web and mobile apps. It can create a workspace product that stays coherent across devices. It can run an HR system with reporting automation. >> [music] >> It can coordinate parallel research and turn that into executive strategy. It can build a finance product with a consistent identity. It can ship a CRM with real integrations and a mobile layer that makes sense. That is a serious spread. So, yeah, agent swarms really does feel like a big deal. Not because it proves the finish line has already been crossed, and not because it solves every hard question people still have about AI. It feels like a big deal because it shows a version of progress that is practical, structured, and easy to imagine scaling from here. And honestly, that makes it more interesting than a lot of louder AI demos. Anyway, that's it for this one. Let me know what you think. Thanks for watching, and I'll catch you in the next one.