I Build Autonomous
AI Agents That Run
Your Business
I architect
Agentic AI Systems Architect & Research Fellow at IIIT Hyderabad. I design multi-agent systems, RAG pipelines, MCP/A2A protocol integrations, and inference-optimized AI that automate complex enterprise workflows — reducing costs by 10X while maintaining human oversight.
class AgenticSystem:
def __init__(self):
self.architect = "R Nishanth"
self.protocols = ["MCP", "A2A"]
self.agents = {
"planner": PlannerAgent(),
"executor": ExecutorAgent(),
"validator": HITLValidator(),
}
async def orchestrate(self, task):
plan = await self.agents["planner"].plan(task)
result = await self.agents["executor"].run(plan)
return await self.agents["validator"].verify(result)
# Output: 10X Efficiency Achieved ✓Why Enterprises Are Struggling
With Agentic
AI
75% of organizations are piloting AI agents — but most fail at production. Here are the barriers I eliminate.
Legacy System Paralysis
Your CRM, ERP, and databases weren't built for autonomous agents. I create MCP/A2A-compatible middleware that bridges old infrastructure with new AI capabilities — no rip-and-replace needed.
Trust & Governance Gap
Agents that act autonomously without guardrails are a liability. I build HITL governance frameworks with audit trails, RBAC, escalation paths, and explainability baked in.
Inference Cost Explosion
Unoptimized LLM calls drain budgets fast. I implement model quantization, vLLM, intelligent routing, and batching strategies that cut inference costs by 60–80% while maintaining quality.
6 Core Service Lines
Engineered for Scale
From multi-agent orchestration to drug discovery — I own the full lifecycle from research to production deployment.
Multi-Agent Orchestration
Autonomous agent systems with Planner → Executor → Validator chains. Built with tool-calling, memory, and structured outputs for enterprise-grade reliability.
- CrewAI / LangGraph architectures
- Pydantic-enforced agent boundaries
- State management & persistent memory
- HITL governance checkpoints
RAG Pipelines & LLM Ops
End-to-end retrieval-augmented generation with hybrid search (dense + sparse), sentence window retrieval, and multi-provider LLM routing.
- pgvector + BM25 + RRF fusion
- Citation-backed answers (100% coverage)
- Multi-provider failover (Gemini, OpenAI)
- 20% higher retrieval precision
MCP & A2A Protocol Integration
Build standards-compliant agent communication using Anthropic's Model Context Protocol and Google's Agent-to-Agent protocol for interoperable AI ecosystems.
- MCP server & client implementation
- A2A task delegation & discovery
- Cross-platform agent interop
- Enterprise security & auth layers
AI Inference Optimization
Slash latency and costs with model quantization, vLLM serving, intelligent routing, and batching. Get production-grade speed without sacrificing accuracy.
- vLLM & TensorRT-LLM deployment
- GPTQ/AWQ model quantization
- Smart model routing & caching
- 5s → 2s latency reduction (proven)
Distributed Systems & Microservices
High-throughput enterprise infrastructure using event-driven architectures, container orchestration, and fault-tolerant design patterns.
- Kubernetes & Docker orchestration
- RabbitMQ / Redis event pipelines
- JWT-based multi-tenant gateways
- Auto-scaling & zero-downtime deploys
AI Research & Drug Discovery
Cutting-edge molecular generation, graph neural networks, and diffusion models applied to pharmaceutical R&D. Published researcher with production deployment capability.
- Molecular conformer generation (E(3)-equivariant)
- CIGIN / Graph Transformer architectures
- Diffusion-based drug design pipelines
- ICMED 2025 published research
Production Systems I've Deployed
Not mockups. Not prototypes. These are real systems handling real workloads in production.
DocKnowledge — AI Document Q&A System DEPLOYED ON AWS
End-to-end RAG system enabling natural language Q&A over massive PDF/TXT datasets. Features hybrid search (dense + sparse) fused via Reciprocal Rank Fusion for 20% higher retrieval precision than baseline vector search.
Synergex Med — Multi-Agent AI Call QA 39+ CLINICS
Advanced agentic workflow that replaces manual human QA across 39+ clinic locations. Leverages two-phase Chain-of-Thought (CoT) prompting for evidence-anchored analysis and multi-key API rotation for 99.9% system availability.
Secure Finance Engine — Enterprise Dashboard 41+ TESTS
Production-grade financial backend designed for high-integrity data processing. Implements Zero-Trust RBAC via a data-driven permission matrix, stateless JWT authentication, and audit-compliant soft-delete mechanisms.
ClinicalDischarge AI — Patient Intelligence AWS EC2
Multi-agent AI system for hospital discharge management. 4 specialized agents handle medication reconciliation, risk scoring, and follow-up scheduling — HIPAA-compliant with full audit trails.
The Unfair Advantage
I'm not a "prompt engineer" or chatbot builder. I'm a systems architect who builds cognitive infrastructure at the intersection of research and production.
Live Demos & Open Source
Don't just take my word for it. These systems are live and the code is open. Verify every claim.
DocKnowledge RAG Demo
Upload your PDF and ask questions. See hybrid search + citation-backed answers in real-time.
72+ Open Source Repos
Browse my complete portfolio of AI, distributed systems, and backend projects. All code is public.
Open Source Contributions
Verified contributions to Scikit-learn and PyTorch. I build at the framework level, not just consume APIs.
Start with Zero Risk
Every engagement begins with a free audit. No commitment required until you see the ROI map.
- Workflow bottleneck analysis
- AI automation opportunity map
- Custom ROI scorecard
- Implementation roadmap
- No commitment required
- Single-agent workflow automation
- RAG pipeline for one data source
- Production deployment (Docker)
- 2 weeks of post-launch support
- Full source code ownership
- Multi-agent orchestration system
- MCP/A2A protocol integration
- HITL governance & compliance
- Ongoing retainer & optimization
- Dedicated architecture reviews
Let's Build Your
AI Agent System
I build the autonomous infrastructure that lets your team focus on what humans do best. Whether you're an MNC looking for AI transformation or a startup needing your first agent system — let's talk.
-
LinkedIn r-nishanth-
-
GitHub Nishanth-nishu