Forward deployed engineering

Gloqo AI

Specialists in building · operationalising · scaling agentic AI systems to millions of people.

Globally present inSingapore · Hong Kong · Sydney · Melbourne · Warsaw · London · San Francisco

Background equationparticles follow −∇f

\frac{d x}{d t} = - \nabla f (x), f (x, y) = \sin x \cos y + \frac{x^{2} + y^{2}}{8}

Each point is a parameter state. Arrows sample the vector field −∇f. Trajectories descend the potential, the same geometry behind training dynamics and iterative agent improvement.

From the library

Notes & visuals

01 Note Loop Engineering, Graph Engineering, and the Hill-Climb Agentic AI 02 Visual Softmax Model Mechanics 03 Note The Agent Harness Is the Product Agentic AI 04 Visual Gradient Descent Optimization 05 Note When Agents Fail, Debug the Trajectory Evaluation 06 Visual Tokenization Embedding Representation