There is a seductive demo you have probably seen: one agent, one prompt, and it plans, codes, tests, and opens a pull request while you watch. It is genuinely impressive, and it is the architecture most teams reach for first, because it is the one that fits in a tweet.

Then you try to run it on real work, repeatedly, and it starts to fray. Not dramatically. It just gets unpredictable in ways you cannot pin down, and you find yourself unable to say why it did well on Tuesday and badly on Wednesday. That unpredictability is not a tuning problem. It is the architecture telling you something.

Why the mega-agent frays

A single agent doing everything in one long context has three problems that get worse with scale, not better.

It is hard to debug. When a fifteen-step autonomous run produces a bad result, which step was wrong? The context is a single blur of reasoning, tool calls, and self-correction. You cannot isolate the failure because there is no seam to isolate it at.

It is hard to test. You can evaluate the final output, but you cannot easily evaluate the parts, because there are no parts. Every improvement is a guess against an opaque whole, and every regression is a mystery.

And its blast radius is the whole task. One early misjudgment, a misread of the requirement, a wrong assumption about the codebase, propagates silently through every later step. The agent builds confidently on top of its own mistake, and by the time you see the output, the error is ten steps deep.

What decomposition buys you

Breaking the work into narrow agents, each with one job and a defined input and output, changes the economics of everything that comes after the demo.

Each agent becomes independently testable, because it has a contract: given this input, produce this output. You can build an evaluation for "does the planner produce a sound plan" separately from "does the implementer write correct code." When quality drops, you know which stage moved.

Failures become contained and legible. A bad output from one stage is caught at that stage's boundary, checked before it feeds the next one, instead of silently poisoning the rest of the run. The seam between agents is exactly where you get to inspect and reject.

And you get to match the model to the job. A narrow judgment step can run on a small, cheap model; a hard reasoning step can use your best one. The monolith forces you to run everything at the level of its most demanding moment.

The part nobody markets: use less AI

The strongest version of this argument goes further than "many agents instead of one." It is that a lot of the work should not be an agent at all.

Sorting a list by a fixed rule, validating a payload against a schema, deduplicating records, retrying a timed-out request: these are deterministic problems with correct answers. Wrapping them in a language model makes them slower, more expensive, and less reliable, in exchange for nothing. The mature move is to use agents for judgment and language, and plain code for everything that has a right answer. The interesting engineering is drawing that line well.

The honest limitation

Decomposition is not free. More agents means more coordination, more handoffs to get right, and more surface area to design. A single agent really is simpler to stand up, and for a prototype or a low-stakes task, simpler may be the right call. The trade only pays off when reliability starts to matter more than time-to-first-demo, which for anything that ships to production is early.

That inflection is where we landed with Loopsfinity. The work is split into focused steps with real boundaries between them, judgment handled by models and the deterministic parts handled by ordinary code, because the goal was never the most autonomous agent. It was the most trustworthy result, and those turn out to be different targets. This is one piece of the broader picture in AI agent architecture.