About

Jigar Joshi — Agentic AI Architect & Founder

Agentic AI consultant, trainer & architect - helping teams ship production-grade systems.

Jigar trains development teams and consults with IT companies to build production-grade agentic AI systems. Every engagement is customised to the client's tech stack and produces working deliverables. He runs this through Wan Buffer, his agentic AI consulting and training firm (jigarjoshi.in is his personal site) — so when you read “we”, that is the Wan Buffer team of 20 people plus 150 agents in production.

Jigar Joshi, agentic AI consultant and founder of WanBuffer
15+
years in software, ERP & AI
8+
IT services teams trained
150+
agents in production
40%
faster delivery after training
Speaking & featured
Roles & leadership

Companies, ventures & how Jigar works with the industry

  • CEO & Founder of Wan Buffer Services
  • Agentic AI Architecture & Consultant
  • Agentic AI Trainer and Builder
  • Founder of ScanDoc Solutions
  • CEO & Founder of House of Aakrutii
Career timeline

15+ years, from IT services to agentic systems.

2007–2010
MCA, Computer Engineering
University of Mysore
2010–2018
Software & ERP / Odoo engineering
Roles across India and Kuwait
2018
Founded Wan Buffer Services
Ahmedabad, Gujarat
Today
CEO & Founder · Agentic AI architect, consultant & trainer
Wan Buffer Services — 20 people + 150 agents in production
The Work

Training teams. Building systems. Teaching builders.

Jigar runs WanBuffer, an Agentic AI consulting and training firm. He delivers corporate training to development teams, tech leads, and IT company leaders.

His training is known for being customised to the client's tech stack, using real client code, and producing working deliverables in every session. He has trained teams at Bytes Technolab and Metizsoft.

Founder, WanBuffer
Corporate Training for IT Companies
Agentic AI Implementation Consulting
Daily Content on AgenticAi Farming
Areas of Expertise
Agentic AI ArchitectureClaude API & Claude CodeModel Context Protocol (MCP)Multi-Agent SystemsRAG & CAGCursor IDEPrompt EngineeringProduction DeploymentTool Design PatternsLLM Observability
The Three Principles
Simplicity first
Every concept must be explainable without jargon. If it requires jargon to explain, the explanation is not ready.
Authenticity over polish
Write and teach from what has been built and observed. Specific details beat broad statements every time.
Build in every session
80% hands-on. 20% concept. Never reverse this. Every session ends with working code.
New on the blog: tool registry design for agentic AI
I cut a 47-tool registry to 19, raised average selection confidence from 0.31 to 0.74, and dropped wrong-tool rate from 22 to 7 percent. The model never changed. Here is the seven-step audit pattern.
Read the post
New on the blog: MCP governance just became a product
Databricks Unity AI Gateway ships the four primitives every enterprise MCP deployment has been hand-rolling. What it changes, where the gaps are, and the migration I would run for a Databricks shop.
Read the post

Want to work with Jigar?

Training, consulting, or a discovery call to figure out what your team needs.

Get in touch