Architecting Resilient Multi-Agent Swarms: Beyond Simple ReAct Loops
A deep dive into building deterministic multi-agent state machines with hierarchical supervisor routing, episodic vector memory, and self-healing error recovery.
Designing low-latency multimodal AI pipelines, autonomous multi-agent swarms, and enterprise-grade distributed web applications. Passionate about marrying modern frontend UX with deep neural backend infrastructure.

Iduwara Nisal
AI Architect
A high-performance technical stack forged across production AI pipelines, distributed cloud systems, and cutting-edge web applications.
Specialized in constructing multi-agent swarms, low-latency RAG systems, and high-throughput model serving runtimes.
Peak Performance
3,200 tok/sec Throughput
Evaluation Benchmark
94.8% Accuracy
Building reactive, 60fps applications with Next.js 16 App Router, TypeScript, and micro-interactions.
Containerized microservices orchestration, auto-scaling Kubernetes topologies, and automated zero-downtime CI/CD.
High-dimensional vector stores with HNSW scalar quantization, PostgreSQL relational modeling, and Redis caching layers.
Real-world systems engineered for low latency, autonomous intelligence, and seamless user experiences.


A high-throughput orchestration engine allowing autonomous agents to dynamically decompose tasks, cross-validate hypotheses, and query vector memory stores with sub-50ms latency.
Token Throughput
3.2k tok/sec
Task Success Rate
94.8%
In-depth architectural breakdowns on autonomous multi-agent swarms, zero-latency frontend engineering, and distributed vector infrastructure.
A deep dive into building deterministic multi-agent state machines with hierarchical supervisor routing, episodic vector memory, and self-healing error recovery.
Exploiting Partial Prerendering (PPR), streaming Suspense boundaries, and Edge Data Caching to build responsive interfaces with instant initial loads.
Benchmarking HNSW, Scalar Quantization (SQ8), and sparse BM25 indices to achieve balanced recall and lightning-fast query execution.
Tracing distributed GPU kernel memory bottlenecks and network socket contention directly inside the Linux kernel without injecting code into userland.
Whether you are exploring autonomous agent workflows, scaling a high-throughput Next.js application, or seeking technical advisory, I'm ready to architect the solution.
Direct Email
nisal.dev.ai@gmail.com
Primary Location
San Francisco, CA & Remote
Updated PDF covering full career trajectory & patents.