Large tasks rarely fail because of one big mistake. They fail because of hundreds of small ones. This idea sits behind a growing shift in how researchers think about artificial intelligence. Instead of asking a single model to shoulder an entire problem, new systems split the work into smaller pieces. A recent research effort offers a clear window into what comes next and how leaders can prepare.
Here’s the effort: a team of researchers released a study titled Solving a Million Step LLM Task with Zero Errors that explored whether a language model could complete a sequence of more than one million dependent actions. They chose the Towers of Hanoi puzzle because its solution requires 1,048,575 tightly linked moves. A single misstep breaks the entire chain. Models normally fail after a few hundred steps. The researchers designed a system called MAKER that reframed the problem. Instead of one model producing the entire solution, they created many small agents, each responsible for a tiny decision. These micro agents voted on each step and flagged any uncertain output. This blueprint shows how future AI systems will coordinate work at scale. It also hints at how companies will organize AI driven processes and how teams will design workflows that blend humans and machines.
Micro agent workflows create accuracy at extreme scale
The first insight from the study centers on decomposition. The system broke the larger challenge into very small tasks that a lighter model could complete with confidence. This mirrors how complex projects work in daily life.
For example, let’s explore an analogy with a tax preparation firm. Here’s how the team works:
- A junior staffer gathers receipts and organizes documents.
- A payroll specialist checks income records.
- A tax analyst applies deductions.
- A senior accountant reviews everything.
No single person handles the full load. Each step stays small and clear. The firm avoids major errors because every task stays manageable. MAKER applies the same idea to machine reasoning and shows how small steps allow a system to finish work that a single model cannot hold in its head.
This approach points to a new way leaders will think about AI adoption. Rather than search for one model that solves everything, teams will rely on networks of smaller agents that each play a focused role. Workflows built this way give analysts and managers a clear view of who did what and why.
Low cost models can outperform bigger systems when guided by structure
The second outcome challenges the assumption that only the largest models deliver the best performance. The MAKER system used compact models and still completed the million step sequence without error. The key came from structure. The system used voting to compare several answers and moved forward only when a clear result emerged. It also used a simple safeguard to stop progress when an agent felt unsure.
Let’s use a real world example to bring this to life. In a fast food kitchen, a highly trained chef can create a perfect meal, but restaurants that cook at massive scale rely on process. Standardized steps, clear handoffs and simple roles allow a team of entry level staff to serve thousands of meals with steady quality. The skills matter, but the system matters more. MAKER shows that the same principle applies to AI. Structure and repeatable steps allow smaller models to perform far beyond what many expect.
This shift suggests that companies will focus less on raw model size and more on how they shape the workflow around the model. Leaders who understand process design will gain an advantage as AI begins to support more of the operational load.
Transparent steps create safer and more inspectable AI
The third insight from the study highlights safety. Every micro agent produced a traceable decision. The system captured each vote and each moment of uncertainty. This created a long chain of explanations that humans could inspect and review. When something looked off, the process paused.
Again, a real world example helps illustrate this concept. Preflight procedures in aviation include pilots moving through a checklist that covers each subsystem. They record results and stop if something needs attention. The checklist does not replace expertise. It channels it. MAKER applies this same discipline to machine reasoning and creates a path for teams to audit decisions at a very fine level.
As more companies adopt AI, this type of traceability will matter. Leaders will ask not only whether the system arrived at the right answer but also how it reached that answer.
What comes next for complex tasks
These types of systems will feel increasingly familiar over the next few years. Teams will move beyond single model trials and start to build practical flows made up of small agents that hand work off from one step to the next.
- Analysts will sketch these processes the way they map customer journeys.
- Sales teams will tap into them to shorten response times.
- Marketing teams will shape content pipelines that follow the same pattern of clear, repeatable steps.
Managers will guide these systems much like they guide software projects, with checkpoints, reviews and a shared understanding of how the parts fit together.
This approach is mirrored in the design of Civic Nexus. The platform brings together smaller, focused components that connect to the data and tools organizations rely on every day. Nexus lets teams design sequences that feel clear, dependable and easy to observe. As agentic systems mature, leaders will look for ways to stay in control of how the work gets done. They will want clarity instead of a black box. Civic Nexus gives them a path to that future.
