AI Agents That Actually
Ship to Production.
Most AI demos never leave the notebook. I build multi-agent systems with LangGraph, CrewAI, RAG, and MCP that run autonomously in production — handling real workflows, making real decisions, and saving real money. Not prototypes. Production systems with guardrails, observability, and cost controls.
What I Build With
LangGraph — Stateful Agent Workflows
For complex, multi-step reasoning where agents need to branch, loop, and self-correct. I use LangGraph when the workflow has conditional logic, human-in-the-loop gates, or needs persistent state across long-running tasks. Not every problem needs this — but the ones that do, nothing else comes close.
CrewAI — Multi-Agent Teams
When you need specialized agents collaborating as a team — a researcher agent, a writer agent, a QA agent, each with their own tools and expertise. I have built CrewAI systems for enterprise HR automation, financial reporting, and sales pipeline management that run 24/7 without human intervention.
RAG — Your Data, Your Answers
Retrieval-Augmented Generation that actually works at scale. Chunking strategies that preserve context, hybrid search (semantic + keyword), re-ranking pipelines, and citation tracking. I build RAG systems that handle 100K+ documents with sub-second response times and verifiable source attribution.
MCP — Tool Integration for Agents
Model Context Protocol servers that give your AI agents access to databases, APIs, browsers, code execution, and any tool they need. I have built MCP tool servers for weather, search, code execution, browser automation, and database access — the connective tissue that makes agents useful in the real world.
Who This Is For
Companies With Repetitive Knowledge Work
If your team spends hours on report generation, data extraction, document processing, or customer communication, those workflows can be decomposed into agent teams. I identify the highest-ROI processes and build agents that handle 80% of the work autonomously.
SaaS Products Adding AI Features
You have a working product and want to add AI-powered features — smart search, content generation, automated analysis, intelligent routing. I design the AI architecture that integrates cleanly with your existing stack without creating a second system to maintain.
AI Startups Going from Demo to Production
Your prototype works in a notebook. Now you need it to handle 1000 concurrent users, cost under $0.05 per request, and not hallucinate on edge cases. I take agent systems from demo to production-grade with proper error handling, cost optimization, and observability.
Enterprises Evaluating Agentic AI
Your leadership wants an AI strategy but your team lacks experience with agent architectures. I run discovery workshops, build proof-of-concepts on real company data, and deliver a clear build-or-buy recommendation with cost projections.
What You Get
Agent Architecture Design
Production Implementation
Ongoing Optimization
Open Source Agent Projects
CrewAI Enterprise Agents
Multi-agent workflows for automated HR onboarding, financial reporting, customer support triage, and sales pipeline management. Each agent has domain-specific tools, memory, and escalation protocols.
LangGraph Multi-Agent Pipeline
Graph-based agent orchestration with conditional routing, parallel execution, and self-correction loops. Demonstrates stateful workflows where agents negotiate, delegate, and verify each other's work.
MCP Tools Server
Model Context Protocol server collection providing AI agents with real-world capabilities: database queries, web browsing, code execution, search, and file system access.
RAG Knowledge Assistant
Production RAG system that ingests documents, builds vector embeddings, runs hybrid search with re-ranking, and answers questions with verifiable source citations.
Let's Build Your Agent System
Tell me about the workflow you want to automate. In 30 minutes, I will tell you whether an agentic approach makes sense, which tools fit, and what a realistic timeline and cost look like.
FAQ
What is agentic AI consulting?
Agentic AI consulting is the design and implementation of AI agent systems that work autonomously — planning, executing, and self-correcting without constant human supervision. Virendra Vaishnav builds these systems with LangGraph for stateful workflows, CrewAI for multi-agent teams, RAG for knowledge retrieval, and MCP for tool integration.
When should a company use LangGraph versus CrewAI?
Use LangGraph when the workflow has complex conditional logic, needs persistent state across long-running tasks, or requires human-in-the-loop gates. Use CrewAI when you need specialized agents collaborating as a team — like a researcher, writer, and QA agent each with their own tools. Many production systems combine both.
How do you take an AI agent prototype to production?
Production readiness requires four layers most demos skip: guardrails (confidence gates and blast-radius limits), observability (tracing every agent decision), cost controls (model routing and token budgets), and evaluation frameworks (measuring accuracy on real data). Virendra has shipped agent systems handling enterprise workflows across regulated industries.