Monday 30 June – Tuesday 1 July 2026 · Ahmedabad
Agentic AI Foundation Program — Essentials
Program 01 · 2-day corporate cohort
View recapIntensive, 80%-hands-on programs that take dev teams from AI-assisted coding to shipping production multi-agent systems. Working code every session.
See the programs 02Design and build your first production agent system — architecture, implementation, and a clean handoff to your team.
How it works 03Reusable agentic patterns mapped to real business outcomes: support triage, document automation, internal copilots, and more.
Browse solutions 04What shipped in agentic AI today — curated and condensed with implementation notes for builders. One email on Thursdays.
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Not theory from a slide deck — patterns proven in production at Wan Buffer and on real client stacks.
Keynotes and hands-on workshops on agentic AI for engineering teams, software CEOs, and founder communities. Practical, demo-driven, and tailored to your audience, not generic AI hype.
The MCP 2026-07-28 final specification publishes July 28. You already did inventory, header security, and handle migration. These four days are cutover discipline: freeze pins, delete sticky sessions only after round-robin passes, confirm cacheScope with two identities, and staff an on-call window for clients still sending Mcp-Session-Id.
ReadA straight-line agent is a degenerate graph. Claude Code dynamic workflows move orchestration into JavaScript so subagents fan out, verify, and converge without stuffing every intermediate result into one context window. Here is the 14-step roadmap I map onto production work, with topology diagrams, contracts, and the patterns I already ship.
ReadOn July 21, 2026 Google released Gemini 3.6 Flash ($1.50/$7.50 per million), 3.5 Flash-Lite ($0.30/$2.50, ~350 tok/s), and a limited-access 3.5 Flash Cyber via CodeMender. Gemini 3.5 Pro stays in partner testing. Here is how I re-benchmark cost per completed task against Sonnet 5 and Fable without betting the roadmap on Pro.
ReadIt is hands-on help designing and shipping AI systems that take actions, not just answer questions. In practice that means scoping the right workflow, building the agent with reliable tools, evals, and guardrails, and handing your team a system they can run. I work mostly with IT services teams putting their first or second agent into production.
A practical path from prompt to production agent: the agent loop, tool design, the Model Context Protocol, multi-agent orchestration, evals, and observability. Teams build real agents during the program rather than watching slides. Format and length are tailored to your team, from a focused two-day workshop to a multi-week cohort.
Whatever fits the job, but the stack I reach for most is the Claude API, the Model Context Protocol for tools, Python and FastAPI, pgvector for retrieval, and an observability layer like Langfuse or OpenTelemetry. The point is matching the tool to the problem, not standardising on a framework for its own sake.
For a bounded, well-scoped workflow, a working agent in front of real users in about 90 days is realistic, with the agent and its operating layer of evals, observability, and guardrails built together. What stretches timelines is rarely the model. It is unclear scope, messy data, and a security review that arrives late.
Yes. I am based in Ahmedabad and work with teams across India and remotely worldwide. Consulting and training are delivered in person or remotely depending on what suits your team.
Tell me about your team and what you want to ship. Or email jigar@wanbuffer.com.