Saturday, 20 June 2026 · Ahmedabad
AI Beyond Automation: Creating New Revenue, Markets & Business Models
One Agent Coordinates Your Entire ADLC
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.
Read the latest
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.
Artifacts in Claude Code beta publish self-contained HTML to claude.ai that republishes to the same URL as the session progresses, with version history and org-only sharing. Strict CSP, no external fetch, no backend. Requires Team or Enterprise and claude.ai login. Here is the workflow I use for PR walkthroughs and incident timelines without screenshot threads in Slack.
ReadEMA makes the organization IdP the decision-maker for which MCP servers a user can reach. Admins enable connectors once; clients exchange an Identity Assertion JWT for scoped tokens without redirecting every employee through OAuth per server. Anthropic ships it across Claude, Claude Code, and Cowork; VS Code supports it; Okta is the first IdP. Here is the pilot I run before July 28 stateless transport work lands.
ReadCursor 3.7 lets you spin subagents in cloud VMs with /in-cloud, iterate on a PR until merge-ready with /babysit, and hand off between local and cloud sessions. Cursor 3.8 adds /automate and five GitHub review triggers. Here is the workflow I use so parallel cloud work does not bypass Auto-review, environment snapshots, or pre-push /review.
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 [email protected].