1. Decomposition Shortens the Inference Path
LLMs operate within a limited working window with bounded curvature tolerance. Long, unsegmented prompts stretch that window until errors accumulate:
- drift increases,
- contradictions compress,
- unsupported claims appear,
- later reasoning relies on degraded structure.
Breaking a task into steps does not make the model “smarter.” It simply reduces the distance between anchor points, giving the model a tractable local neighborhood to operate within. Shorter paths mean less curvature and fewer collapses of local structure.
2. Each Step Re-Anchors the Context
Every segmented step forces the model to start from a simplified slice of the problem. This resets vocabulary, assumptions, causal orientation, and structural commitments.
Re-anchoring is not cognition—it is constraint relief. The model sees a smaller, cleaner manifold segment and can therefore produce a more stable continuation. When this works well, it resembles planning, but the improvement arises from repeated re-anchoring rather than agentic reasoning.
3. Segmentation Reveals Quality; It Does Not Manufacture It
A system only benefits from decomposition if each step is internally sound. If a step produces malformed content, downstream structure cannot correct it.
Segmentation improves visibility of epistemic quality, but the quality of each individual step determines correctness. When a step is well-formed, segmentation preserves it. When a step is poorly formed, segmentation preserves that too.
This makes the geometry of each step as important as the geometry between steps.
4. Two Independent Geometries Must Be Aligned
Multi-step inference involves two distinct geometries:
- Local geometry — inside a single step: how evidence is interpreted, boundaries maintained, and drift controlled.
- Global geometry — between steps: how information is passed, how decomposition is shaped, and how context is reintroduced.
In well-structured agent systems, workflows are described in agentic terms and the inference steps inside them use generative models because that is the preferred geometry for inference. In practice, however, prompts inside those steps often drift toward imperative control patterns, especially when designers attempt to handle edge cases. This shifts the overall geometry away from the configuration that decomposition relies on for stability.
Aligning both geometries—local and global—is required for reliable behavior. Optimizing only one produces partial stability: workflows become easier to reason about, but the epistemic quality of the content inside each step remains variable.
5. Why Improvements Are Often Misattributed to “Agentic Reasoning”
When segmentation improves stability, it is tempting to attribute the gain to planning structure or agentic decision-making. In practice, the dominant contribution is geometric:
- shorter inference arcs,
- lower curvature exposure,
- controlled drift,
- isolated assumptions,
- re-established local neighborhoods.
These effects arise from the geometry of segmentation, not from agentic cognition.
6. The Role of a Runtime Contract
The runtime contract strengthens the local geometry: admissibility, void preservation, conflict handling, causal discipline, uncertainty structure, and single-pass determinism. When inserted into each step, it ensures that segmentation does not simply create well-structured sequences of unstable content.
Good decomposition shapes the global structure. A runtime contract stabilizes each local segment. Together they produce improvements that neither can achieve alone.
Closing
Multi-step inference works because it reshapes the geometry of the problem. It shortens arcs, stabilizes context, and isolates reasoning. But these benefits only hold when each step is internally stable. Decomposition cannot fix malformed epistemics; it can only make them more visible.
Understanding this distinction allows systems to evolve intentionally rather than by accident. It makes agentic workflows more reliable, single-step inference more precise, and the entire system more predictable.
The structure matters—and the consequences of that structure determine everything that follows.