Ben Carr @BenCarrAI
Learning Partner at Farther. I help people learn to work with AI: adoption, training, and managing AI agents. Research-backed L&D takes, minus the hype. Orlando, FL Joined December 2025-
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We’re opening a research preview of Claude Duet, a new app where Claude works as a proactive agent. It comes to you with what needs your attention, completes tasks you ask of it, keeps working when you step away, and checks with you before it acts on your behalf.
Before handing a task to an AI agent, write down what a good result looks like. What must it include? Which tools can it use? What should it flag for review? The brief is part of the work. So is checking the result.
AI practice can help build fluency. Watching skilled people handle the unexpected gives learners something else to study: judgment. I'd build room for both in a training program. My guide is attached.
For your next AI training follow-up, ask: What do you use AI for? What work takes less time? Where does that freed time go? Usage tells you people tried it. The last question starts a conversation about impact.
If you're using AI mostly to draft text, try the next level: connect it to one tool you use every day. Pick a small task with an output you can verify. My 4-level framework is attached.
What would you want to see before trusting an AI agent's work? For me: the finished output, enough evidence to check it, and a clear flag for anything uncertain. Delegating the task still leaves me responsible for the result.
This video edit was created in a single prompt with Opus 5.5 and blender. I just recorded the video and uploaded it to claude and it used a skill i've been working on for a day. It cut out the mistakes, added the music, sound effects all rotating around a single scene in blender. I think it's about 3% as good as it could be. Will be iterating on it to make it 1% better every day. Just posted it on IG, let's see how it does.
My training recording workflow crossed Drive, video editing, and Notion before notifying attendees. One agent coordinated it. Connecting AI to the tools where work lives changes what you can delegate. My workflow guide is attached.
Here's a training exercise I'd use: watch an experienced seller handle an unexpected objection, talk through their choices, then practice a similar situation with AI. Give people a standard to learn from before adding more repetitions.
Every customer-facing AI agent changes the work humans handle. More exceptions. More judgment. Different training needs. My Decagon/OpenAI marketplace guide covers the L&D implications. It is attached.
@swyx The same idea applies to training materials. If agents rewrite workflows weekly, your job aids have to be cheap to update. Teach teams to refresh one aid after each process change instead of shipping a permanent PDF.
@bschlenker Enthusiasts will find the tools. The engine room needs practice on the three tasks they already touch every week, with a manager nearby for the first few tries.
@degreed @davidblake Outlearning only sticks when practice is tied to the job. Pick one task that changed this month, build a short practice loop around it, and measure whether people can do it differently next week.
@OpenAI Parallel agents raise a training question first. Who owns the merge, and what does good look like when three drafts land at once? Teach the review step before you celebrate the speed.
@OfficialLoganK Teams need the same habit for people using AI. A simple pass fail check on real work beats another feature tour. Benchmarks for models. Rubrics for humans.
@Josh_Bersin @satyanadella Specialist agents mirror how good L&D already designs roles. Narrow the scope, write a clear definition of done, and train the human owner to check that one domain well before you expand.
@rowancheung Speed without a learning loop just burns people out. The teams that keep up usually protect a weekly block to redesign one workflow, then ship the change instead of collecting more tools.
@conorgrennan Treating AI like a silent teammate is why adoption stalls in meetings. I would put one standing question on every agenda. What did the agent try this week, and what still needs a human call?
@karpathy That funnel is an L&D problem hiding in plain sight. Most companies train for the derpy chatbot tier. The useful move is a short path that gets people from search-engine habits to verified multi-step work on real tasks.
@paulroetzer When agents outpace headcount, the training problem flips. Junior roles used to be how people learned the craft. Now you need deliberate practice paths for judgment work that agents cannot own yet.
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