Alex Lieberman

Alex LiebermanSep 22, 2026

I'm so excited to announce Tenex Agents! Off-the-shelf, enterprise-grade AI agents focused on tackling recurring work across functions like Finance, Sales, Marketing, HR, and IT. Everyone & their mother is building/launching agents. So how are ours different? • < 2 weeks to deployment • Built for responsible oversight (permissioning, approvals, audit trails) • Optimized performance layer (model routing, evals, context management) • Custom configured for your processes & systems of record • Tied to agreed upon outcomes Our agents JUST WORK because they’re a labor of love, 18 months in the making. We started Tenex to help companies turn frontier AI research into real business results. Since then, we’ve built hundreds of custom solutions for some of the biggest companies in the world. Along the way, clients keep asking: “What are you seeing on the ground, and what should we be doing next?” We’ve studied the market and spoken with hundreds of executives. Three priorities kept coming up: 1) Faster time to value. See meaningful results quickly and cheaply. 2) A clearer starting point. Know which workflows to tackle and how to measure success. 3) A fit with the business. Work within existing systems, with the right access controls and human oversight. That’s when Tenex Agents said “hold my beer.” We’ve been quietly turned hair on fire customer processes problems into a suite of composable and battle-tested agents. They give you a ready starting point, with Tenex handling the implementation: • Up and running quickly. Most deployments targeted for under two weeks. • Configured for your business. Adapted to your processes and connected to your systems of record. • Built for oversight. Defined permissions, human approvals, and an audit trail. • Measured against an agreed outcome. A clear definition of success for the first workflow. We’re slowly rolling out Tenex Agents, so please fill out this interest form if you’d like to learn more: https://lnkd.in/e8J_bXHW

Alex LiebermanSep 23, 2026

25 mistakes companies make with AI transformation: 1) Not investing in your data foundation/not having a data “clean-up” strategy. 2) Starting with “we need AI” instead of a real problem. 3) Underresourced AI center of excellence that serves every part of the organization. Backlog builds up, employees get disenfranchised, shadow AI explodes. 4) Trying to automate the same workflow vs rethinking from scratch. 5) Over-engineering. Sometimes you don't need a full agentic system and traditional software works just fine. 6) Obsessing over cost before proving feasibility of a use case. 7) Telling your people that AI won’t impact jobs. 8) Not having places to go to ask questions / knowledge share. Whether that be a skills library, shared repo, or internal AI office hours. 9) Failing to solve the last mile. Everyone’s so focused on models, but successful applied AI is a complex last mile problem: governance, data, observability, context management, people, process, etc. 10) Shipping it and call it done. Lack of discipline to go beyond the shiny demo and ensure sustained adoption. 11) Slop is tolerated. 12) No governed way to build for non-technical people. No Citizen SDLC to empower SMEs to build and share production apps. 13) CEO is not a driving force. Leadership enforcement without the leaders actually knowing how or what to enforce. 14) Not getting the buy in of the “bad guys.” Bring Legal, Finance, and IT along for the ride early. 15) Not investing in / underestimating change management. Easy to get the folks who are excited on board, but it's a long process to make others feel comfortable. 16) Not measuring baselines before any adoption. What are the metrics pre-AI tool to post AI tool? No baseline = no roi story. 17) Inventing new KPIs for AI instead of focusing on having AI accelerate existing functional KPIs. 18) Being driven by FOMO and not having the patience to treat AI transformation as the multi-year migration it actually is. 19) Not anticipating the complexity of getting systems to work nicely together (a kind of scope creep as the reality blows up work required). 20) Locking in to a single provider ecosystem. 21) Not providing employees access to the underlying systems needed to make AI useful to take action, not just chat. 22) One functional department thinking they should own AI transformation. It treats AI as a vertical solution vs. horizontal capability that’s more than just technology. 23) Executing on AI initiatives before anchoring your work in a clear strategy that’s tied to business goals, a map of key processes, understanding of your technology and data reality, and clarity around how to meet your people where they are. 24) Not solving data permissioning and RBAC considerations before rolling out agentic tools firmwide. 25) Neglecting internal evals to constantly test and evaluate how new models/harnesses perform company tasks on a $ per successful task basis. What's missing?

Alex LiebermanSep 25, 2026

Just spoke to one of the big data labeling businesses. Few interesting insights: - They predict the majority of their revenue will come from Fortune 1000 enterprises, not labs in a few years - They believe every company will want to own their intelligence, but owning intelligence does not necessarily mean using open source models - A company’s evals will become their main proprietary IP given the improvement in agent performance after properly setting up & running internal eval environments - Most enterprises haven’t graduated from coding agents and it’s largely due to not having the proper eval infrastructure to make non-Eng agents performant