From the field.
What Jigar learns building and training, shared as posts. Specifics over slogans.
The June 15 Claude billing change: Agent SDK credits, model retirement, and the checklist I run before anything breaks
Two Anthropic changes land on the same day: programmatic Claude usage moves to a separate monthly credit pool, and claude-opus-4-20250514 plus claude-sonnet-4-20250514 stop answering on the API. Interactive Claude Code is fine. Cron jobs and CI agents are not. Here is how I audit auth paths, claim credits, and grep for retiring model IDs before the first failed run.
Governing agent autonomy in 2026: Auto-review, permissions.json, and pre-push review
Cursor Auto-review uses permissions.json autoRun.allow_instructions and block_instructions so low-stakes shell/MCP calls flow and high-stakes actions pause. Here is how I wire that pattern into local agents, SDK headless runs, and CI — without mistaking a classifier for a hard security boundary.
Claude Fable 5 for agent builders: when the frontier model is worth the routing change
Anthropic shipped Claude Fable 5 on June 9: a Mythos-class model with tiered safeguards, mandatory 30-day retention on traffic, and $10/$50 per-million pricing. Days later access was suspended globally pending export-control review. Even if you never touched Fable, the launch tells you how frontier routing, retention policy, and governance will work for agent builders in the second half of 2026.
Agentic RAG vs vanilla RAG: why a Sufficient Context Agent beats retrieve-then-pray
Google Research shipped Agentic RAG on Gemini Enterprise with a Sufficient Context Agent that refuses to answer when retrieval is incomplete. On factuality benchmarks they report up to 34% higher accuracy versus standard RAG. Here is when one-shot RAG is still enough, when you need iterative retrieval, and how I wire the pattern without blowing latency budgets.
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.