AI Agents

A proof-of-concept that layers authentication, encryption, and multi-agent coordination onto MCP — with four security tiers from API keys to enterprise OAuth2 with audit trails.

A set of Rust-backed extensions for the Robyn web framework — Pydantic v2 validation, JWT/OAuth2 auth, token-bucket rate limiting, and auto-generated OpenAPI docs. Sub-microsecond overhead.

An open-source skill that teaches any AI coding agent — Cortex Code, Claude Code, or others — to automate Google Slides, Sheets, Docs, Drive, and Forms using natural language prompts.

An open-source demo that coordinates multiple AI agents using only PostgreSQL — pgmq for task queuing, LISTEN/NOTIFY for event-driven coordination, ltree for lineage tracking. No Redis, no Kafka, no vector database.

An open-source semantic layer that lets you query PostgreSQL in plain English using local LLM inference via Ollama. No API keys, no cloud — just your database and a local model.

Generic agents can do anything — which is exactly the problem. Custom agents in containers behind an inference proxy create a production architecture where security is structural, not aspirational.

The agentic enterprise is real. But getting there requires a shift from generic protocol-based integrations to purpose-built agents with native tooling, embedded security, and domain-specific intelligence.

A high-performance Rust proxy that lets Claude Code, Continue.dev, ZeroClaw, Mistral Vibe — or any AI coding agent — use Snowflake Cortex, OpenAI, Anthropic, or Ollama as their backend. Optional prompt policy enforcement included.

A defense-in-depth security architecture for AI agents and inference workloads — covering network isolation, identity propagation, authorization, data protection, and auditing under the EU AI Act, DORA, and NIS2.

How a 1,600-line Python file can teach you everything about the Cortex Agent API