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

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:

1. @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.
2. @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.
3. @takk/krikos manages agent identity through Ed25519 signatures, enabling non-human identity governance within large-scale agent fleets.
4. @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:

• davccavalcante/modelchain (2026-05-30): https://github.com/davccavalcante/modelchain
• David C Cavalcante davccavalcante - GitHub: https://github.com/davccavalcante
• dcavalcante (Daniel Cavalcante) · GitHub: https://github.com/dcavalcante
• Leopoldo Cavalcante Poldo11 - GitHub: https://github.com/Poldo11
• README.md: https://github.com/davccavalcante/racs/blob/main/README.md
• davccavalcante/noeticos: https://github.com/davccavalcante/noeticos
• davccavalcante/behavioralai: https://github.com/davccavalcante/behavioralai
• davccavalcante/bayesroute: https://github.com/davccavalcante/bayesroute