I design and ship production-grade agentic AI systems — multi-agent orchestration pipelines, Model Context Protocol (MCP) servers, enterprise RAG architectures, and LLM reliability frameworks built to operate at scale.
My focus is on the architecture problems that most teams hit in production and don't know how to solve: agents that fail silently, tool access with no governance, retrieval pipelines that hallucinate at the edges, and orchestration loops with no observable recovery path.
10+ years in software delivery. The last 3 years deep in the agentic layer.
| System | Result |
|---|---|
| Multi-agent orchestration platform (LangChain + ReAct + Plan-Execute DAG) | 50% reduction in agent failure-recovery time |
| Enterprise RAG pipeline (pgvector + BM25 + Cohere reranking + deduplication) | < 2% hallucination rate in production |
| Constraint-driven allocation agent (LangChain + Genetic Algorithms + OR solvers) | 3× recommendation quality improvement |
| 4× production MCP servers (SharePoint, SQL, workflow triggers, RBAC-governed) | Zero invalid resource allocations in production |
| LLM evaluation harness (OpenTelemetry-style tracing + regression checks) | Full observability across agent execution paths |
🔄 Agentic Orchestration Plan-Execute DAGs · ReAct reasoning loops · self-correction mechanisms · Human-in-the-Loop checkpoints · multi-agent conversation patterns · budget guards · deterministic verification layers
🔌 Model Context Protocol (MCP) Production MCP server design · typed JSON Schema contracts · RBAC allowlists · governed tool exposure · secure enterprise system integration
🔍 RAG & Retrieval Systems Hybrid search (dense + sparse) · query routing · contextual reranking · metadata enrichment · PII filtering · source citation grounding · context deduplication
🛡️ LLM Reliability & Evaluation LLM-as-Judge harnesses · adversarial prompt testing · prompt injection defense · structured output validation · circuit breakers · sandboxed tool execution
AI/ML: LangChain · LangGraph · AutoGen · CrewAI · LlamaIndex · MCP · RAG · ReAct · Pinecone · FAISS · pgvector · Cohere · OpenAI · Anthropic Claude
Production multi-agent execution protocol featuring a canonical nine-phase orchestration loop, self-validation mechanisms, deterministic budget guards, and goal versioning for sequential multi-goal execution. Drop-in configuration for agentic coding environments.
multi-agent·orchestration·execution-loops·agentic-ai·LLM
Research repository for LLM reliability engineering: adversarial testing frameworks, prompt injection defense mechanisms, and structured output validation schemas. Covers evaluation patterns for high-stakes production deployments.
prompt-engineering·LLM-reliability·adversarial-testing·guardrails·evals
🔌 plugLLM
Unified API abstraction layer for hot-swapping between OpenAI, Anthropic Claude, and local LLM providers. Automatic fallback orchestration and load balancing across providers with a single interface.
LLM·openai·anthropic·provider-abstraction·python-sdk
Experimental framework for autonomous agent workflows implementing persistent memory architectures, structured tool registry patterns, and hierarchical execution loops built on LangChain and AutoGen.
autonomous-agents·memory-architecture·tool-registry·LangChain·AutoGen
Technical pieces on agentic architecture, LLM reliability, and production orchestration patterns:
- How I Built a 9-Phase Orchestration Loop for Coding Agents — the engineering behind deterministic multi-agent execution
- MCP in Production: Governing Tool Access for Enterprise LLM Agents — lessons from deploying 4 MCP servers at scale
- RAG That Doesn't Hallucinate: Hybrid Retrieval in Practice — pgvector + BM25 + Cohere reranking, what actually worked
Lead AI Engineer · Agentic AI Architect · Principal GenAI Engineer · AI Systems Architect
Companies building serious agent infrastructure — enterprise, MNC, product-led. Based in Kerala, India. Open to remote-first roles globally.
→ View full portfolio & case studies
"The difference between an agent demo and a production agent is everything that happens when the plan fails."