From the field.
What Jigar learns building and training, shared as posts. Specifics over slogans.
Agentic transformation is an operating-model problem, not a model problem
Microsoft published a 6-step playbook for rolling agents out across an enterprise, and the line that matters is "you do not need a bigger model, you need a better operating model." That matches what I see in consulting: the pilots that die do not die on model quality, they die on ownership, evals, and governance. Here is how I read the playbook for IT services teams, and the operating-model gaps that actually stall agent rollouts.
The anatomy of an AI agent: memory, tools, the loop, and guardrails
Strip the hype off an AI agent and four parts are left: a memory, a set of tools, a loop that decides what to do next, and a guardrail that vets every action before it runs. Here is what each part is for, the order they fail in, and where I have written about fixing each one.
Your coding agent has amnesia. Persistent memory is the fix.
Claude Code forgets your architecture, your decisions, and why you ruled things out the moment a session ends. The reliability tax is not tokens, it is re-establishing context every morning. Here is what persistent agent memory actually is, how an open-source engine like Cortex implements it, and how to evaluate a memory layer for your own agents.
Your agent's supply chain is the attack surface now
A poisoned VS Code extension spent eighteen minutes on the marketplace and walked off with Claude Code credentials and MCP configs. The model was never the target. Your agent's supply chain is: the extensions, skills, MCP servers, tool definitions, and keys it is allowed to touch. Here is how I harden all four layers, and the checklist I run on every deployment.
MCP just went stateless: what the 2026 spec release candidate changes for your servers
The biggest revision of MCP since 1.0 locked as a release candidate on May 21. The protocol goes stateless, extensions move out of the core, and authorization finally speaks OAuth properly. Most of your servers keep working. Here is what actually changes, what breaks, and the migration I would run in the ten weeks before the final spec lands.
Your agents aren't broken, your tools are: three questions to ask before you build one
When an agent misbehaves, almost everyone reaches for the prompt or the model. The fault is usually further down, in a tool that does too much, lies when it fails, or buries the answer in a wall of raw data. An AI tool is not a function. It is a contract the model has to trust. Here are the three questions I run before writing a single line of any tool.
Inside Recruiting Atelier: a runnable reference for the primitives of an agentic system
A working open studio that vets duplicates, plans the run, screens, scores, shortlists, and notifies. The whole pipeline lives in roughly ninety lines of supervisor code and a tool registry you can read in one sitting. Here is what is inside, why every piece is there, and what you can copy into your own stack.
How an agentic studio screens, scores and shortlists candidates for your hiring team
Open Recruiting Atelier and you do not see a generic AI dashboard. You see five named specialists doing the work a screening team would do: catching duplicates, checking the brief, scoring on four dimensions, ranking, drafting the dispatch. Drop one CV or fifty. Click any candidate to see exactly why they landed where they did. This is what AI for recruitment looks like when it respects your judgment instead of replacing it.
Code agents vs skill agents: when to give an agent the keyboard and when to give it the toolbox
Two ways to let an agent act in the world. Code agents write fresh code into a sandbox. Skill agents pick from a curated menu. The choice should be made in the kickoff, not the postmortem. Here is the framing I use with clients, the four axes where they diverge, and the hybrid pattern most production systems become.