AI Builder Notes - May 2026

AI Builder Notes - May 2026 AI-assisted notes from my liked-tweets feed, organized around agent workflows, browser traces, model loops, and guardrails. Practical takeaways Start with the workflow, not the agent. A useful agent task has a source of truth, a narrow action, a verifier, and a stop condition. “Review this repo” is vague. “Find auth bugs in these routes, cite file lines, run the relevant tests, and stop after the first credible exploit path” is a workflow. Use dynamic workflows in claude code - to do the vibe bits for thinking through a workflow. Think of it like this - you can describe in natural language an entire workflow consisting of multiple agents at various steps - I want the docs updated, tests passed, security review done and also playwright tests done. Dynamic workflows figures out which parts can be divided in parallel and what should be done sequentially. Creates a flowchart - and writes JS code for it. Its a JS script that can execute subagents at scale and deterministically [1] Planner/executor split is the way to go. Spend the expensive model on taste, decomposition, and risk discovery. Use cheaper or narrower models for repeatable implementation once the task has tests, rubrics, logs, or examples. [2] Do not judge an agent workflow by the model name alone. If the loop has repo access, a rubric, a way to inspect tool calls, and a verifier, a less fashionable model can still do useful work. The Letta Code / GLM 5.1 review-bot example is interesting for that reason, not because “someone used X instead of Y” is interesting by itself. [3] Prefer small interfaces to giant tool menus. MCP tool call definitions are rotting your context! The monday.com GraphQL example was the clearest cost warning: one task used 15k tokens through SDK/code-mode and 158k tokens through a real MCP server. MCP is useful, but a menu of tools is not automatically an efficient interface. [4] [5] For browser work, save the trace. Run the workflow once, inspect wasted act