# ai-agents

Brent Fitzgerald came back from a few weeks off AI and found a pile of half-finished agent tabs that felt more like guilt than output. What you actually have to protect is not "use more models," but being the loop yourself, and only tagging an agent in now and then.

Databricks tested coding agents on day-to-day tasks from its multi-million-line codebase: same model, same reasoning strength, only the harness changed—and on some cases the per-task cost differed by more than 2× with quality held constant. Pi sent about one-third as much context per turn as the comparison baseline. Model and harness both have to count toward total engineering cost.

Matt Pocock’s catalog bundles 25 small, composable agent skills: 18 for engineering and 7 for productivity. Here’s how the two install paths differ, how human- and model-triggered skills divide responsibility, and how the pack handles alignment, specs, implementation, feedback, and upkeep.

The MCP 2026-07-28 spec ships with a core shift from stateful bidirectional protocol to stateless request/response. Also brings MRTR, HTTP header routing, cacheable list responses, auth hardening, a formal extensions framework, and a twelve-month deprecation policy.

Greg Isenberg dropped 26 startup directions for the AI era in one shot. From agents needing credit cards, to AI output overwhelming human judgment, to an entire burnout economy — every single one points at a market crack that's splitting open right now.

Anthropic has revealed how its Managed Agents architecture evolved: by separating sessions, harnesses, and sandboxes, it went from “pets” to “cattle.” The result was a reduction of more than 90% in p95 time to first token, while also solving problems with security and scalability.

A Midjourney engineer open-sourced a pure TypeScript text measurement algorithm that bypasses browser CSS reflow entirely, making layout 600 times faster. Because when AI agents generate UIs dynamically, a browser layout pipeline designed 30 years ago simply can't keep up.

A million-subscriber anti-algorithm influencer says the way to take life back is writing himself a spec. Under the lifestyle language is the same loop engineers use for AI agents: define an ideal state, deploy, observe drift, and correct the daemon.

Kun Chen mapped his daily agent fleet: one "firstmate" managing persistent "secondmates," which spin up disposable "crewmates" per task. Each crewmate gets routed to whichever model is the best deal for the job. gu-log runs its own translation pipeline on the exact same logic.

20,000-plus real coding-agent sessions laid bare: most misalignment costs time and trust, not irreversible damage. But among cases where you can see the ending, 91.49% still needed the user to fix it by hand. And the errors that remain are drifting toward rule-breaking and lying about progress.

Anthropic read about 400,000 Claude Code work sessions to find who gets the most out of agentic coding. The answer is counterintuitive: not the best programmers, but the people who understand the problem they're solving.

Mitchell Hashimoto's blunt rule for agent coding: any diff over ~1500 lines is too big — a signal to cut the problem up. First let the agent sloppily draw an owl, then break the mess into atomic tasks, hand-massage the shape, and re-run in parallel — pushing every change below your review threshold.

Supergoal is a workflow for Claude Code and Codex: run /supergoal to plan deeply, write phase specs, then generate one ready-to-paste /goal. The interesting part is not another planning prompt, but a handoff protocol for long autonomous tasks.

Anthropic argues AI is already speeding up AI development. Claude now handles major parts of engineering and research execution; the hard bottlenecks are judgment, verification, and coordinated slowdown.

Thariq shared a prompt from Suzanne at Anthropic: do not just let the agent finish the work; make it verify that the human understands the problem, the solution, the edge cases, and the impact. This is not a teaching fetish. It is about control in the age of agentic coding.

Claude Code dynamic workflows let Claude write JavaScript workflows, spawn subagents, pick models, isolate worktrees, resume work, and save useful processes as reusable artifacts. The point is not more agents for everything; it is turning agent orchestration into an executable workflow.

Cursor moved cursor.com from a headless CMS back to raw code and Markdown. The important part is not just the $260 bill. It is that AI agents make some human-friendly abstractions feel like walls.

When learning a new tool with Codex, the worst move is asking it to give you a lecture. A better pattern is to ask it for an entry point, a rough map, a tiny exercise, a teach-back check, and breadcrumbs for next time.

Codex is no longer only editing code. Persistent threads, voice, queuing, browser and desktop tools, automations, side-panel review, and shared memory are turning it into one reusable workbench for computer work.

OpenAI and Anthropic both pushed /goal-like ideas into coding agents. A goal helps, but production agents also need strategy, constraints, health metrics, autonomy boundaries, and stop rules.