These tools provide the engineering substrate required to meet the rigorous safety and economic constraints of production environments.

#github #npm #typescript

Engineering production AI infrastructure requires moving beyond heuristic guesswork toward deterministic, verifiable logic. My open-source portfolio of 11 TypeScript packages, published with SLSA provenance and zero runtime dependencies, provides the foundational primitives for high-stakes agent deployments.

I built the @takk ecosystem to solve specific, quantifiable bottlenecks in LLM systems engineering. We treat code as a mathematical artifact rather than a collection of features. Every module is strictly typed, dual ESM/CJS, Apache-2.0 licensed, and validated by extensive test suites designed to prove stability before runtime execution.

The efficacy of this architecture rests on objective technical benchmarks:

@takk/mcpcustoms provides a semantic firewall for agent tool calls with 158 tests across 19 suites. It implements a fail-closed, hash-chained audit trail to mitigate injection and capability overreach.
@takk/gaptime implements bi-temporal knowledge-graph memory. By tracking independent transaction and valid time axes, it satisfies record-keeping requirements for EU AI Act Article 12 and ISO/IEC 42001 control A.6.2.8.
@takk/krikos manages agent identity through Ed25519 signatures, enabling non-human identity governance within large-scale agent fleets.
@takk/tokenforecast delivers predictive cost intelligence via Bayesian cold-start and Holt-Winters methods, maintaining 95%+ test coverage to ensure reliable FinOps within the execution process.

These tools do not offer magic; they provide the engineering substrate required to meet the rigorous safety and economic constraints of production environments. Governance compliance is an organizational responsibility; these libraries simply provide the auditability and control mechanisms to make that compliance technically feasible.

Zero-dependency design remains non-negotiable to minimize the attack surface and ensure deterministic behavior across edge and server environments. By isolating business logic from external sidecars, I have optimized for performance and verifiable reliability.

Inspect the technical architecture, test coverage, and source code here:

https://github.com/davccavalcante/racs
https://github.com/davccavalcante/modelchain
https://github.com/davccavalcante/mcpcustoms
https://github.com/davccavalcante/gaptime
https://github.com/davccavalcante/krikos
https://github.com/davccavalcante/tokenforecast
https://github.com/davccavalcante/alkaline

The transition from prototype to industrial-grade infrastructure requires this level of discipline. Inspect the repositories to evaluate the implementation details. Constructive critique based on the codebase is welcome.

Sources: