Architecting invariant tool-selection protocols, production reliability layers for MCP agent runtimes, custom vLLM inference engines, and autonomous multi-agent cognitive platforms across GCP, AWS & Kubernetes.
Primary engineered systems spanning protocol invariance research, production MCP reliability substrates, deterministic agent evaluation, and custom inference serving.
First formal empirical benchmark measuring semantic and syntactic tool-selection invariance in LLM agents. Evaluated on 480 live API trials across NVIDIA NIM & Groq, proving +3.0% stability recovery with deterministic canonicalization.
High-performance speculative tool-execution runtime with conservative dependency and effect verification. Hides tool invocation latency with Python AST verification and transactional rollback guarantees.
Enterprise multi-agent decision intelligence platform powered by LangGraph, clause-aware hybrid RAG (Milvus dense + BM25 sparse + BGE reranking), and MCP NL2SQL with AST-level SQLGlot safety guardrails.
Dynamic Agent-to-Agent (A2A) task graph generation and parallel multi-agent orchestration runtime with LLM-to-DAG compilation, semaphore-bounded scheduling, and failure-localized dynamic replanning.
Enterprise security auditing, semantic LLM inspection, and sub-millisecond runtime defense platform for the Model Context Protocol. Defends against prompt injection, privilege escalation, and tool abuse.
Production reliability, benchmarking, and evaluation layer for Model Context Protocol (MCP) agent runtimes — protocol negotiation, A2A interop, distributed circuit breakers, adaptive streaming, and autoscaling.
Production technologies and frameworks leveraged across Agentic AI engineering, cloud infrastructure (GCP / AWS / Kubernetes), and high-throughput systems.
Empirical benchmarks reflecting tool-selection stability (Tool-Trust Lab), MCP runtime latency & circuit breakers, and custom inference token throughput (nano-VLLM).
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