Quantaiko AI Library Agent

Generic AI Agent to automate building libraries

The mother of all

Applications

The agent serves a broad range of engagements:

• Building to specification. The client sets the objective and the specification; the agent delivers against them, and delivers fast. The CUDA CPU Simulator is one example — a comparatively straightforward case, since existing simulators, over‑complicated though they were, already defined the target.
• Research. Automation lets a hypothesis be formulated, tested, and kept or discarded at a speed no manual process can match. Time Series Predictor is one example, and the Portfolio Constructor built on top of it is another — turning those predictions into an optimal dynamical portfolio that hedges, optimizes a market view, and raises the Sharpe ratio; see also Optimization below.
• Re‑implementing large existing quantitative libraries. We have drawn on our years of experience as front‑office quants at major investment banks to optimize the agent for rewriting these libraries into clean, well‑formed, thoroughly tested, and extensively documented code, while preserving their original interface, making adoption seamless and reducing support overhead. As a result, valuable resources no longer need to be spent supporting legacy code, and can be reallocated to more revenue‑generating projects. NumpyPandas C++ is one such re‑implementation.

Workflow

The agent combines an extensive Claude CLI prompting library with a toolkit of proprietary and external tools to drive dynamic planning‑and‑execution workflows. The prompting library has been calibrated to deliver an optimal result.

User input remains fundamental — but it is concentrated on the flow infrastructure, not on the coding itself. This is the clearest departure from the old‑school developer role.

Optimization

One of the most significant consequences of this approach is that libraries can now be optimized to a degree that was previously out of reach. Established practice pushes towards code that is as generic as possible, so that it can be reused across projects with minimal — ideally no — modification. The price of that generality is optimization: a generic library can never be tuned as far as we would like. The obstacle was never confined to specialization over primitive types (integer, floating‑point, and the rest); it sat at the infrastructure level, where full optimization would have meant rewriting the library outright.

That constraint no longer applies. The agent makes fully optimized libraries practical to build, and Time Series Predictor is the clearest illustration: we produced several specialized, optimized libraries — specialized LLM libraries among them — each shaped precisely around the way it is used. The gains have been tremendous.

Advantages

In practice:

• The mundane part of the coding process is handled by AI, under the supervision of the specialized AI Agent, with checks at every level.
• Tests are written and run automatically, with coverage tracked at every step.
• Debugging is carried out by the specialized AI Agent.
• A marked reduction in the number of bugs.
• Support and new development are straightforward. The purpose‑built documentation produced by the specialized AI Agent lets an agent “understand” the library and make the right connections across it, so support is far faster and is never blocked by unfamiliarity with the codebase.
• Compliance and model validation are far easier: any newly required tests and documentation are delivered in hours.
• A much shorter cycle from development to production.
• Full library optimization.
• Language agnostic (C++, Java, C#, Python, …).