How People Use AI Agents

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TL;DR

A study analyzed how people use AI agents, finding they are adopted for cognitive work, not just simple tasks.
57% of AI agent activity focuses on cognitive tasks like productivity, workflow, learning, and research.
Users' AI agent usage evolves from low-stakes queries to complex problem-solving and professional workflows.
AI agents are becoming indispensable, similar to how spreadsheets and word processing made personal computers essential.
Six core occupations drive 70% of AI agent activity, with knowledge-intensive fields showing high 'stickiness'.
Context-specific usage dominates, with finance professionals focusing on productivity and students on learning/research.
The study indicates a move towards a hybrid intelligence economy where AI agents scale human cognitive work.

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I can't remember the last time I was truly stuck on a coding problem. AI agents changed this. Going from "sometimes blocked" to "always moving" is wild.

Addy Osmani

Tip: Figure out your personal ceiling for running multiple agents in parallel.

We need to accept that more agents running doesn't mean more of _you_ available. The narrative is still mostly about throughput and parallelism, but almost nobody's talking about what it actually costs the human in the loop.

You're holding multiple problem contexts in your head at once, making judgment calls continuously, and absorbing the anxiety of not knowing what any one agent might be quietly getting wrong.

That's a new kind of cognitive labor we don't have good language for yet.

I've started treating long agentic sessions the way I'd treat deep focus work: time-boxed and tighter scopes per agent dramatically change how much mental overhead each thread carries.

Finding your personal ceiling with these tools is itself a skill and most of us are going to learn it the hard way before we learn it intentionally.

Addy Osmani reposted

Charly Wargnier

🚨 You need to see this.

@addyosmani from Google just dropped his new Agent Skills and it's incredible.

It brings 19 engineering skills + 7 commands to AI coding agents, all inspired by Google best practices 🤯

AI coding agents are powerful, but left alone, they take shortcuts.

They skip specs, tests, and security reviews, optimizing for "done" over "correct." Addy built this to fix that.

Each skill encodes the workflows and quality gates that senior engineers actually use: spec before code, test before merge, measure before optimize.

The full lifecycle is covered:

→ Define - refine ideas, write specs before a single line of code

→ Plan - decompose into small, verifiable tasks

→ Build - incremental implementation, context engineering, clean API design

→ Verify - TDD, browser testing with DevTools, systematic debugging

→ Review - code quality, security hardening, performance optimization

→ Ship - git workflow, CI/CD, ADRs, pre-launch checklists

Features 7 slash commands: (/spec, /plan, /build, /test, /review, /code-simplify, /ship) that map to this lifecycle.

It works with:

✦ Claude Code

✦ Cursor

✦ Antigravity

✦ ... and any agent accepting Markdown. Baking in Google-tier engineering culture (Shift Left, Chesterton's Fence, Hyrum's Law) directly into your agent's step-by-step workflow!

`npx skills add addyosmani/agent-skills`

Free and open-source.

Repo link in 🧵↓

Addy Osmani reposted

Addy Osmani

Tip: Figure out your personal ceiling for running multiple agents in parallel.

You're holding multiple problem contexts in your head at once, making judgment calls continuously, and absorbing the anxiety of not knowing what any one agent might be quietly getting wrong.

That's a new kind of cognitive labor we don't have good language for yet.

I've started treating long agentic sessions the way I'd treat deep focus work: time-boxed and tighter scopes per agent dramatically change how much mental overhead each thread carries.

Finding your personal ceiling with these tools is itself a skill and most of us are going to learn it the hard way before we learn it intentionally.

Addy Osmani

Memory makes your agent smarter over time.

The agent harness is key to the memory layer. You can't bolt one onto the other after the fact. Every decision the harness makes - what goes in context, what survives compaction, how skills get surfaced, how the working directory is exposed etc - is a memory decision.

A well written write-up by @hwchase17

Addy Osmani reposted

Richard Seroter

Have you installed these production-grade engineering skills for AI coding agents, courtesy of @addyosmani?

https://t.co/t38B2QZ1bZ

The repo is trending on GitHub this week (https://t.co/FFtTldC7Z7) with 16k stars.

I just added them to my @geminicli. https://t.co/bAQmchTY3U

Addy Osmani reposted

Aaron Levie

The more enterprises I talk to about AI agent transformation, the more it’s clear that there is going to be a new type of role in most enterprises going forward. The job is to be the agent deployer and manager in teams. Here’s the rough JD:

This person will need to figure out what are the highest leverage set of workflows on a team are (either existing or new ones) where agents can actually drive significantly more value for the team and company.

In general, it’s going to be in areas where if you threw compute (in the form of agents) at a task you could either execute it 100X faster or do it 100X more times than before. Examples would be processing orders of magnitude more leads to hand them off to reps with extra customer signal, automating a contracting review and intake process, streamlining a client onboarding process to reduce as many straps as possible, setting up knowledge bases than the whole company taps into, and so on.

This person’s job is to figure out what the future state workflow needs to look like to drive this new form of automation, and how to connect up the various existing or new systems in such a way that this can be fulfilled. The gnarly part of the work is mapping structured and unstructured data flows, figuring out the ideal workflow, getting the agent the context it needs to do the work properly, figuring out where the human interfaces with the agent and at what steps, manages evals and reviews after any major model or data change, and runs and manages the agents on an ongoing basis tracking KPIs, and so on.

The person must be good at mapping the process and understanding where the value could be unlocked and be relatively technical, and has full autonomy to connect up business systems and drive automation. This means they’re comfortable with skills, MCP, CLIs, and so on, and the company believes it’s safe for them to do so. But also great operationally and at business.

It may be an existing person repositioned, or a totally net new person in the company. There will likely need to be one or more of these people on every team, so it’s not a centralized role per se. It may rile up into IT or an AI team, or live in the function and just have checkpoints with a central function.

This would also be a fantastic job for next gen hires who are leaning into AI, and are technical, to be able to go into. And for anyone concerned about engineers in the future, this will be an obvious area for these skills as well.

Satya Nadella

Great example of what Foundry enables: durable, stateful agents that run across time boundaries, orchestrate tools and models, and close the loop with evaluation and improvement over long-running workflows. @jeffhollan https://t.co/v0yWTDIcn3

Addy Osmani

Software engineers before vs. after agents :)

If this is you: try Cloud agents (they're pretty good!) or at least Amphetamine to keep your Mac from sleeping when the lid is closed.

Yann LeCun reposted

NYU Center for Data Science

AI agents often struggle to plan movements because their internal representations of the physical world can be overly tangled.

CDS PhD student Ying Wang (@yingwww_) shows how straightening these pathways improves AI navigation.

Accepted to ICML 2026.

https://t.co/BOJCXWwWS8

Aravind Srinivas reposted

Perplexity

We've published our internal manual for building agent skills.

Skills require a new way of thinking for developers.

https://t.co/Rw88kBYXqR

Aravind Srinivas

A research blog on how Perplexity builds agents and agent skills for products like Computer.