Agent Builder

The only genuinely non-negotiable prerequisite in the entire Atlas.

The specific mathematics that appears in practice, and nothing beyond it.

Derivatives, the chain rule, and what it means to walk downhill in a loss landscape.

Systems whose behaviour is learned from data rather than written as rules.

The optimisation procedure underneath essentially every model in this Atlas.

Many-layered neural networks that learn their own features instead of being handed them.

Layers, weights and activations. The unit of construction for everything downstream.

Letting every position look at every other position. The hinge the modern field turns on.

Attention, feed-forward layers, residuals and normalisation. The block that ate the field.

Transformers trained on enough text to become general-purpose reasoning surfaces.

Specifying a task precisely enough that a probabilistic system does it reliably.

Models that decide what to do next, call tools, and act in a loop rather than answering once.

Function calling, schemas, and validating what the model asks you to run.

Distributions, expectation, and why every evaluation number needs an error bar.

Learning a mapping from labelled examples. The workhorse of applied ML.

The skill that separates people who ship models from people who publish notebooks.

Measuring quality when there is no single correct output. The hardest unsolved problem in shipping.

Judging a trajectory, not an answer. Compounding error is the thing to measure.

Decomposition, reflection, and letting the model check its own work.

Several specialised agents coordinating. Powerful, and usually premature.