Model-Based Agentic Software Engineering

How should we engineer software when implementation becomes abundant but engineering judgment remains scarce?

The MAGE method — scale creates the enduring reasoning problem; commodity intelligence changes its economics; Modeling and Alignment turn engineering effort into capital rather than churn.
A single centre spine reads top to bottom: Scale and Finite reasoning combined in one node, a New engineering imbalance, the Modeling Principle, the Alignment Principle, the Governed Engineering Environment, and Engineering Capital. Scale is the enduring source of a finite reasoning horizon: large systems exceed any one reasoner, so relevant state must be abstracted or reconstructed, which makes judgment scarce at scale. Commodity intelligence enters laterally at the New Engineering Imbalance, making implementation abundant while judgment, representation, evidence, and authority stay scarce; it changes the economics of the imbalance but does not cause the reasoning horizon. The Modeling Principle answers the imbalance by making consequential knowledge and intent explicit at useful scales, which exposes those properties for analysis and reaches the Alignment Principle, where selected obligations are given authority through constraints, sensors, validators, and gates. Passing through Alignment the spine enters the Governed Engineering Environment — the synthesis of both principles — and effort compounds as Engineering Capital when future value exceeds its carrying cost. A single subordinate governance-conversion loop feeds recurring failures and judgment back to adapt the models and controls, strengthening the environment. Two dashed failure paths lead to Engineering Churn: weak representation is repaid as reconstruction, weak authority as repeated adjudication. A light dotted long-range rail marks capital reopening the reasoning problem as scope grows.

Capable agents have made it much cheaper to produce working code. They have not made it cheaper to decide what should be built, which obligations govern it, how to read the resulting evidence, or whether the system is acceptable to ship.

MAGE studies the engineering structures that make delegated implementation governable: making consequential knowledge explicit as models, giving selected obligations authority over what agents produce, validating realizations against those models, and turning recurring human judgment into structures that later work can reuse.

The engineering problem

Capable agents have made producing working code much cheaper. They have not made it cheaper to decide what should be built, which obligations govern it, or whether the result is acceptable.

Systems and studies behind MAGE

MAGE did not begin as an abstract framework. It grew partly from building and studying agentic software systems and confronting the engineering problems that appeared when implementation became cheap. The projects below supplied worked examples, empirical evidence, and recurring problems that informed the framework; several also illustrate principles that MAGE later makes explicit.

Implications beyond conventional engineering

MAGE’s implications extend beyond the engineering team. Institutions that acquire software face the same changing economics: producing a capability has become cheaper, while deciding what to acquire, which obligations govern it, and what evidence justifies acceptance has not. We are beginning to study what this shift means for organisations that acquire and field software rather than build it themselves.

Funding and support

This work has been supported by:

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