You’re reading The Steady Beat, a weekly pulse of must-reads for anyone orchestrating teams, people, and agents across the modern digital workplace – whether you’re managing sprints, driving roadmaps, leading departments, or just making sure the right work gets done. Curated by the team at Steady.
The productive struggle
Office hours are emptying out at MIT. So are study groups. Students get through problem sets faster than ever yet learn less doing them, and an ad hoc committee spent a year figuring out what to do about it. Their report and findings, out this month, center on the reality that education is “necessarily a productive struggle,” and AI’s whole pitch is to remove the struggle. So the recommendations run against the grain of efficiency: oral exams and in-class conversation over take-home work, semester-long projects you have to build and defend in person, structured collaboration in every subject, and instructors who disclose their own AI use. The idea is not to police the tech itself, but to purposefully embrace the friction that fuels tech innovation like AI in the first place. (Read President Kornbluth’s excellent cover letter for the short version.)
— MIT, 45m, #ai, #learning, #leadership
It’s just software
Every vendor deck has a different definition of “agent,” and most of them are marketing. Ours is simpler: an agent is a harness plus a model. The harness is ordinary software that shuttles words in and out of the model, takes a goal from a human, and hands back a result. The only unusual part is the model, which won’t give the same answer twice. Once you see it that way, you can sort agents on two axes: how big the scope of work is (a single task, or a whole job made of recurring routines) and how much autonomy it has (on-demand chat on your laptop, or running unattended on a server). Single-player versus multiplayer falls out of the same picture. We also untangle context, memory, skills, and routines in plain language, and draw a distinction between agent orchestration (deployment), governance (policing), and coordination (working with a team).
— Steady, 8m, #agents, #ai, #coordination
Trust, then verify everything
DORA found AI adoption correlated with less stable delivery, more than a third of developers don’t trust what the tools produce, and a study of over 100 models found them introducing known security flaws about 45% of the time – a rate that stayed flat even as the models got much better at producing runnable code. The METR result is worse: developers using AI took 19% longer than expected on tasks and believed they’d gone faster. Having a second model review the first doesn’t fix it, either; models trained on the same data share the same blind spots, so you get one opinion twice, not two independent checks. The prescription includes layering the filters (static analysis, tests, human review, production monitoring), sizing the scrutiny to what a failure would cost, and running three loops: agents improving their own output before a human sees it, CI with real quality gates, and a background loop where agents chip away at technical debt.
— ByteByteGo, 13m, #engineering, #ai, #quality
Losing the race
“The real divide in 2026 is not between believers and skeptics. It is between organizations that understand AI as a total system shift, and those still treating it like a clever app.” That’s Gleb Tsipursky, and his argument is simple: AI is moving faster than the organizations trying to use it. McKinsey finds AI in use nearly everywhere and absorbed almost nowhere. The gains are real in some spots, like the 14% productivity lift an NBER study measured for customer support reps, but they come with shrinking entry-level roles that erode future team competency. Then there’s the physical bill nobody budgeted for: chips, cooling, power, and water. Government isn’t catching up either. Europe wrote a sprawling risk-based law, the US is blocking even state-level rules, and the public trusts none of it. The system shift means rewriting how you hire, train, pay, and evaluate people. Bolting a chatbot onto the intranet doesn’t count.
— Gleb Tsipursky, 7m, #ai, #strategy, #organization
Project OT
At his annual leadership retreat in Hawaii this January, Mark Zuckerberg hatched a plan to make Meta “AI native”: rebuild team structures around agents and cut as much as 60% of headcount from many existing teams, in two waves, May and November. Reuters reviewed scores of internal documents and talked to more than 20 people about how it fell apart. Wave one happened on May 20th: roughly 10% laid off, a similar number moved to new AI initiatives. The night before, Zuckerberg called off planning for wave two. Two things had gone wrong at once. First, employees figured out the transformation was aimed partly at replacing them and revolted on internal channels. Second, the agents simply weren’t ready: as early as March, infrastructure teams warned that they were taking unpredictable, disruptive actions, and the flood of AI-generated code was overwhelming the people who had to review it.
— Reuters, 3m, #ai, #organization, #leadership
Echo of the week
Echoes are AI agents in Steady that automatically gather and deliver work context to teams on a schedule – answering recurring questions about progress, capacity, and coordination so you stop burning hours assembling the same information manually.
Blocker trends shows you which blockers are really the same blocker. Blockers get cleared one at a time and forgotten, so nobody notices the same wait for design review, the same flaky staging environment, the same missing decision, showing up week after week.
Once a month, this Echo reads every blocker the team filed in the past 30 days, groups them into the top five themes, and delivers each with a count and examples. So now you can address the theme instead of playing blocker whack-a-mole.
The human-agent teamwork OS
Teams rely on two coordination loops to function: a big-picture loop connecting plans to progress, and a ground-level loop keeping teammates in sync.
Running those loops was already a marathon of meetings, chat threads, dashboards, and manual toil. Pile on flatter orgs, exponential output, and AI agents shipping 24/7 — the old way can’t keep up.
Steady removes the coordination bottleneck by running both loops for you. Working in the background, Steady distills updates and activity into targeted context for everyone on the team — human and agent alike. Full visibility, tight alignment, zero overhead.
The outcome: high-performing teams that deliver the right work, not just more of it.
Learn more at runsteady.com.