Whether it’s existing consulting firms, new ones that emerge, FDEs from agent vendors, or new internal agent engineering roles, the amount of work that is going to be created to implement agents in enterprises will exceed anything we imagine today.
The complexity of implementing agents in any existing organizations is very real. When I talk to large enterprises, as you move from a chat paradigm to agents that participate in meaningful workflows, there are a number of things they need to do.
First, you have to get agents to be able to talk to your data securely across your systems. In many cases, enterprises have decades of legacy infrastructure that contain the valuable context for AI agents. That’s going to take a ton of work to go modernize and move to systems that work well with agents.
Then, you need to ensure that you’ve implemented agents with the right access controls and entitlements, the right scopes to be safely used, and have ways of monitoring, logging, and securing the work that they do.
Next, you need to actually document the processes in the organization in a way that agents can utilize for doing the work. You also need to figure out what the new workflow looks like when agents and people are working together on a process, and who steps in where. Just replicating the old workflow will mute the gains. Oh and you likely need to create evals for your top new end-state processes.
Finally, you have to keep up with a rapidly changing set of best practices and architectural shifts happening in the agent space. While it’s fun for people to change their personal productivity tools on a dime, it’s 100X harder to do this in a business process. The speed of change is a blessing and a curse right now for anyone trying to keep a stable system design.
All of this means that individuals and companies that develop expertise on the above set of components (and more) are going to be needed to help organizations actually implement agents at scale. This is also the rationale for vertical AI agents right now that can go in deep on a business domain and help bring automation to it.
This is a huge opportunity right now whether you’re doing this internally or as an external business provider.
I've been using @yutori_ai Delegate for several tasks (and I am sure I need to learn more ways to give up control more than I am used to 🙃;there are many other use cases), but I love it for
1) Daily brief on people I'm meeting: context on them, which of my current projects (based on Gmail / Granola integrations) the meeting is most relevant to, a talk track, and something from their social that ties to what I'm working on
2) Re-organizing my Linkedin connections based on categories / subcategories, and relationship contexts I am interested in. I suspect this might make LinkedIn connections more 'actionable' in that that I might actually be able to contact the right people at the right time rather than just scrolling through the feed (and I love staying in touch with people for both personal and professional reasons)
3) Follow-on draft memo post meeting (pulled from Granola / Gmail)
4) Re-creating the email labelling plan of my inbox :) [still WIP]
[All of this with full disclosure that I love the founders and trust them to get this right 🙂]
P.S. I dislike doing much of the above admin so this has been valuable!
Some notes on what I think *might* be worth getting good when may people are asking - “what’s worth learning?” “what is worth creating?” “what would schools teach?”, with no guarantees on whether these may be right and recognizing that the paths to career and educational “success” are subjective and meandering with no right answers.
Plus some readings on AI risk measurement, regulations, standards.
https://t.co/KQVuT8oZWe
I care about this quite a bit! Making space to focus on the things that are meaningful to you -- that you care about -- makes no comment on what it is that you care about, or how many things you care about, or how much time you want to spend on said things.
This is also partly why the phrase "work-life balance" has always given me pause. (1) Why do we pull work out of "life" (why don't we talk about friends-life balance or family-life balance)? and (2) Why do we talk about "balance"? It should be about designing your life in whatever way matters most to you, given what you're working with. It may be balanced, it may be lopsided.
“X is dead.”
“It’s all about Y.”
“Now more than ever, we need Z.”
“All the best As are doing B.”
“It’s never been more C.”
So many blanket takes with such supreme confidence...
Congratulations @MLCommons crew @seanmcgregor@LamaSaouma@deepaknathani11 on Agentic Product Maturity Ladder > collection of benchmarks measuring the ability of agentic products to reliably support specific real world tasks
https://t.co/HZ3SdcHrud
https://t.co/ucG49jzFFY
Don’t miss #MLCommons Endpoints in San Diego, Dec 1–2!
Learn, connect, and shape the future of AI with top experts at @Qualcomm Hall.
🗓 Dec 1–2 | 🎟 Free tickets available now!
https://t.co/HoLgXEawi3
#AI#MachineLearning#SanDiego
In a world where everyone is chasing momentum, I'm reminded of a Buffett gem: “You can’t produce a baby in one month by getting nine women pregnant.”
Some things just take time: compounding, craftsmanship, and good judgment.
A huge barrier to scaling AI adoption has been application-specific reliability tools and evals!
I just completed the 4-week course "AI Evals For Engineers & PMs" taught by @sh_reya and @HamelHusain and learned enormously!
Join next cohort!! https://t.co/UAUUVGHkvs
Discovering latest international documentaries on topics I care about, beyond the usual Netflix, Amazon recommendations, on other platforms thanks to scouts by @yutori_ai Super effortless to expand my world and exposure to art from other parts of the world :)