The second fundamental rule for working with AI agents: breaking a large request into smaller granularity, with verification after each step, produces better results than one giant prompt.
If you're attempting a large new feature, it's better to split it into smaller tasks, for several reasons.
One of the most important things to mention is how software development cost behaves over time. The book Code Complete has a well-known chart showing how much more expensive a bug fix gets depending on which phase of development you catch it in: during analysis the cost is almost zero, but once the software is in production at the customer's site, the cost is an order of magnitude higher, or more. With the speed at which AI generates code today, you need to be even more careful about designing the architecture up front — because refactoring is exactly the thing AI doesn't handle particularly well. The best approach is to stick to the standards described in clean-code books and use established patterns for structure and business logic — books are one of the highest-quality sources an AI model learned correct practices from during training, so it understands them best. For AI it's also important to keep the code split into clear layers, even in a monolith — the agent then finds its way around the code far more easily.
Let's get back to how to actually split tasks correctly. Say the task is: build a new customer-satisfaction form. What phases will we need?
- UI — the form, its fields, and the submit event.
- Business logic — form validation, attaching the request to the customer account, request priority.
- Data structure — how the data travels to the BE and back to the FE.
- BE — validation and business logic again, now on the backend side.
- Data layer — mapping from the data structure to the database.
- Persisting the data, plus the matching structure that flows back from BE to FE.
For the AI it doesn't really matter where you start — what matters is describing each step correctly. For you as the user, it's most practical to start either from the database or from the frontend.
That covers all the tasks. The question now is: what's the difference between one big task and several smaller ones?
With a single task, the advantage is a shared context (cache) across steps — the agent never has to look anything up again. But across more turns, that advantage flips: the longer you stay in one context, the more tokens get re-sent and re-processed on every subsequent turn, so the cost per turn keeps growing with the size of the context — on a long, many-turn task that can add up to a noticeable amount.
If we split the tasks instead, the AI only ever does its one specific part, which can be tested independently at the end — catching mistakes before work even starts on the next part. We consume fewer tokens overall, and being able to verify each step independently lets us catch hallucinations or deviations from the requirement much sooner than after a single giant task finishes. Smaller tasks also don't require the model to hold its attention across every detail at once.
Why does this matter so much to AI models? They have a limited ability to maintain priority across a large number of instructions at once. If you write "add a new feature, refactor the old logic, and fix three related bugs" into a single prompt, the model assembles its own priority order — and it might not match yours. The result: part of the task gets done brilliantly, part gets shortchanged, and nobody notices until it breaks in production.
This is exactly why AITM splits large requests into chained tasks instead of letting them run as one continuous pass.
What this looks like in practice: a regular AI chat vs. AITM
A regular AI agent (chat/IDE copilot): you give it one long prompt, and the agent gets to work in one continuous context. If it gets a detail wrong 80% of the way through, you won't find out until you review the entire diff — meanwhile, work on later points kept going on top of the broken foundation.
AITM: the same request gets split in chat/brainstorming into separate chained task calls with dependencies — task B is only allowed to start once task A has gone through the entire pipeline and has been merged. If task A fails at review or test, task B does not start on top of a broken foundation — it waits. Meanwhile, the watchdog evaluates task A's failure itself: if it can analyze the error and propose a fix, the task automatically restarts with a prompt that describes exactly that error and the proposed fix; if the error is unclear or unsolvable without a human decision, the task stops and waits for the user to decide how it should proceed. On top of that, after the last task in the chain merges, a chain-audit step runs — a deterministic (not AI) final check that re-validates the contracts affected by the whole chain.
The difference the user sees: with a regular agent, you get one large, opaque diff at the end. With AITM, you get several smaller, named, independently checked tasks with their own history — and you know exactly where something went wrong if it didn't go according to plan.