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David C Cavalcante
David C Cavalcante Subscriber

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Engineering production AI infrastructure requires moving beyond heuristic guesswork toward deterministic, verifiable logic

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 mathematical and architectural substrate for this transition. Every module in the @takk and @teleologyhi-sdk ecosystem adheres to strict TypeScript, dual ESM/CJS, and Apache-2.0 licensing, ensuring that the logic powering your agent fleet remains as defensible as your core application code.

@takk/mcpcustoms functions as a semantic firewall for agent tool calls. Validated by 158 tests across 19 suites, it implements seven default detectors to intercept command injection and secret exfiltration. It maintains a hash-chained, tamper-evident audit trail, ensuring every verdict—allow, block, or ask—is recorded with cryptographic integrity.

@takk/gaptime addresses memory volatility through bi-temporal knowledge graph modeling. By tracking both valid time and transaction time across 13 Allen interval relations, it enables agents to resolve historical contradictions. This architecture provides the record-keeping primitive required for EU AI Act Article 12 and ISO/IEC 42001 control A.6.2.8.

@takk/krikos and @takk/alkaline manage the operational lifecycle. Krikos establishes Ed25519-based non-human identity governance, while Alkaline offers durable execution without external sidecars, persisting state via swappable cells for SQLite and Postgres.

@takk/tokenforecast provides predictive cost intelligence, utilizing Holt-Winters and Bayesian cold-start methods to forecast LLM spend and detect drift via Page-Hinkley analysis. This grounds cost economics in statistical reality rather than heuristic vibes.

Inspect the 11 repositories and full test suites here: https://github.com/davccavalcante

Which of these architectural constraints is currently the primary bottleneck in your production agent pipeline?


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