The Qualixar Suite
10 research-backed tools for making AI agents reliable, safe, and production-ready. Every tool is backed by published research.
Design-by-Contract for AI Agents
Formal specification and runtime enforcement of behavioral contracts for autonomous AI agents. Prevents drift, ensures compliance, enables composition.
Supply Chain Security for AI Agent Skills
Static analysis, behavioral sandboxing, and cryptographic attestation for the AI agent skill ecosystem. Detects malicious skills before they execute.
Information-Geometric Memory for AI Agents
Local-first AI agent memory with mathematical foundations. 74.8% on LoCoMo without cloud dependency — highest local-first score reported. Fisher-Rao retrieval, sheaf cohomology, Langevin lifecycle. EU AI Act compliant.
Peer-to-peer communication layer for AI coding agents
P2P agent communication with 8 MCP tools. Agents discover each other, send messages, share state, and lock files. Works with any MCP-compatible agent. SQLite + UDS for <100ms delivery. 480 tests, 100% coverage.
The World's First MCP Gateway That Learns
One hub process, every MCP server, every AI client. 430+ tools exposed through 3 meta-tools — 79% fewer processes, 150K tokens saved per session. Federated tool discovery with shared cache, cost telemetry, and retrieval learning.
AI Reliability Engineering — The Universal Runtime for AI Agents
Orchestration runtime of the Qualixar AI Reliability Engineering platform. 13 execution topologies (sequential, parallel, debate, mesh, hybrid, and more), Forge AI auto-design, adversarial judge pipeline, cost-quality-latency routing. 2,936 tests passing. Paper on arXiv.
Regression Testing for Non-Deterministic AI Agents
Token-efficient stochastic behavioral testing framework purpose-built for non-deterministic AI agent workflows. Part of the Qualixar AI Reliability Engineering platform.
Project Echo
Multi-agent communication degradation benchmarking.
Project Sentinel
Reliability analysis for AI-generated code.
Project Rewind
Time-travel debugging for autonomous agents.
Project Aurora
Chaos engineering principles applied to AI agent systems.
Project Bridge
Migration engineering across agent frameworks.
Project Nexus
Composition testing for agent pipelines.
Building Philosophy
Every tool I build follows the same principle: research first, then ship. Each product is backed by a formal paper with mathematical proofs and real experiments.
I build for production. Years of enterprise architecture taught me that tools need to work at scale, handle edge cases, and integrate with existing workflows. If it can't be pip installed and used in 5 minutes, it's not ready.