5 AI Tools That Transform AI Overwork Into Automation Power
Yesterday, we showed you Harvard’s research proving AI makes work harder. More tasks, blurrier boundaries, constant context-switching.
Today: it’s time to learn the toolkit that flips the script. If you’re just getting started with AI or still copying and pasting content directly in the web app, it’s time to upgrade your stack. Each tool below maps to a compound engineering principle from yesterday’s story.
- Cowork (Anthropic, free for Pro users, now on Windows) plans and executes file tasks autonomously. Drop in meeting notes, tell it “summarize action items by team member and draft follow-ups.” 45 minutes of work in 90 seconds. It just added plug-ins for marketing, legal, and sales workflows.
- Codex App (OpenAI, temporarily free) runs multiple agents in parallel on a schedule. Set one to monitor competitors, another to summarize GitHub activity, a third to draft your weekly report every Friday at 8 AM. A 4-person team built Sora for Android in 28 days with it.
- Claude in Chrome (Anthropic, paid subscribers) sees your browser and learns your routines. Record yourself submitting an expense report once; next time, Claude replays it. Boring tasks should only require a human once.
- Tasklet turns plain English into scheduled automations. “Every morning at 6 AM, check my support inbox and post an urgent ticket digest to Slack.” One sentence. Runs forever. This is the easiest way to make safe, simple automations for yourself or your workflows… do not sleep on this tool.
- MCP (open standard) is the plumbing connecting AI to your actual tools. Without it, every AI interaction starts from scratch (this is more under the hood, but when you use “connectors”, this is how other apps connect to your AI).
Here’s the thing most people skip, though: building the agent is the easy part. Knowing if it’s doing a good job is the hard part.
That’s where LLM-as-a-Judge comes in. You use a separate AI to grade your agent’s outputs against criteria you define (e.g., accuracy, relevance, and hallucination checks). Research shows that these judges match human evaluators about 80% of the time, roughly the same as how often humans agree with each other.
For anything with a provably correct answer (math, code, data), LLM-as-a-Verifier goes further: it doesn’t just say “this looks right”; it runs the code, checks the database, and proves it.
Editor’s note: This content originally ran in the newsletter of our sister publication, The Neuron. To read more from The Neuron, sign up for its newsletter here.
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