Faraday @Use_Faraday

Your to-do list isn't usually the problem. It's all the little things that come after it. Find the right info. Open the doc. Send the email. Update the sheet. Follow up. Check what happened. Figure out what's next. None of these takes very long on its own.But somehow, they eat up half the day.That's what we're trying to take off your plate with Faraday. Give it the goal, and let it help carry the work forward. 👉 t.co/MnMFzTaLIe

@luciaverseai This is why autonomy without guardrails is risky. An agent being able to act on its own is only useful if it also knows what it’s allowed to do, when to stop, and when a human needs to step in. Capability is only half the equation. Trust matters just as much.

@0xcibukxbt 100%. An agent quietly failing is much worse than an agent stopping and telling you what went wrong. If you're asking AI to handle real work, you need to know what it did, where it failed, and what needs your attention.

@soonlaii This is the part of multi-agent systems that's easy to overlook. Adding more agents can solve a capability problem while creating a coordination problem. The goal isn't to use more agents. It's to get the work done with the least overhead.

This is exactly the problem we think about with Faraday. If using an AI agent requires you to become good at delegation, prompting, briefing, and supervision, you've added another job. The better experience is: give it the outcome you want, and let the system handle the work in between.

@mariohercules @Joi2James This is the part of running multiple agents that doesn't get talked about enough: eventually, you need to manage the agents themselves. 😅

Your work doesn't stop when you close your laptop. Faraday keeps it moving. GTM. Ops. Research. Support. One AI agent, built to get the work done. 👉 tinyurl.com/runfaraday

You can delegate tasks. The hard part is delegating the follow-through. Someone sends the email, but forgets to follow up. Someone starts the research, but never finishes it. Someone makes the plan, but the next steps get lost. Faraday is built to handle the work after the first step, so things don't just get started—they keep moving. 👉 t.co/sgfCa4TUKj

This is exactly why autonomy can't be the only goal. An agent that can act without oversight also needs clear boundaries around what it can do, when it should stop, and when a human needs to approve the next step. The hard part isn't just making agents capable. It's making them trustworthy.

@seeconvm This is a good distinction. More agents don't automatically mean better outcomes. Sometimes the best workflow is the simplest one that reliably gets the job done. The real challenge is knowing when to add another layer of orchestration—and when you're just adding overhead.

@soonlaii This is the part that gets missed in a lot of agent workflows. More orchestration can look impressive on paper, but if the coordination overhead is bigger than the work itself, you've made the workflow slower. The goal should be less work to manage, not more agents to manage.

@robdogeth This is interesting. The shift from “one agent owns the whole task” to delegation makes a lot of sense for long-running work. The real challenge seems to be making sure that delegation doesn't create another coordination problem of its own.

@RaulJuncoV This is a great way to frame it. A lot of the infrastructure isn't new—the interesting part is what changes when the system can decide the next step itself. That's where reliability, observability, and guardrails become much more than nice-to-haves.

The hardest part of running a small team isn't doing the work. It's keeping track of everything that needs to happen next. Who needs a follow-up. What still needs approval. Which customer needs a response. What got pushed to tomorrow. Faraday helps take care of those moving pieces, so you don't have to keep them all in your head. Less remembering. More doing. 👉 t.co/sgfCa4TUKj

You don't always notice when work starts slipping. A lead doesn't get followed up with. A customer question sits unanswered. Someone forgets to update the doc. A task gets pushed to tomorrow. Nothing feels urgent enough to fix. Until a few weeks later, you realize how much time and revenue got lost in the gaps. Faraday helps catch the work that would otherwise fall through the cracks. 👉 t.co/sgfCa4TUKj

@AskMichaelTaiwo I think there's another side to this. AI doesn't just amplify individual expertise. It can also amplify how well a team executes. The advantage isn't only knowing how to use the tool. It's building workflows where that capability actually turns into consistent output.

@vasuman The “you actually use and understand AI” point is underrated. The best marketing for AI products probably comes from people who have actually experienced the problem themselves, not people who just know how to talk about AI.

@bindureddy Model routing solves the intelligence layer. The interesting part is what happens after the model responds.Choosing the right model is great, but if the workflow still needs someone to carry the output across tools and next steps, there's still a lot of work left.

@dan__rosenthal The hard part isn't having all these pieces. It's keeping them connected. Every handoff creates another place for context to get lost, follow-ups to slip, or work to stall. The GTM stack is getting bigger. The execution layer matters just as much.

@nico_laqua The model race is getting interesting, but I think the bigger question is what you actually build around the model. A better model doesn't automatically mean better outcomes if the workflow still needs someone to babysit it.