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.
Govern the Swarm
Everyone’s racing to deploy AI agents. Almost nobody’s asking who governs them once there are thousands. Jessica Peltz-Zatulove argues the next durable infrastructure is connective tissue: the systems that keep autonomous agents accountable to humans. A better model won’t be the moat. She names three categories worth watching. First, Systems of Control govern agent identities at scale, the way Okta governs human access, except now you might be issuing credentials to a million non-human actors. Second, Systems of Trust make probabilistic decisions auditable, because “the agent did it” is not an answer your board accepts. And third, Systems of Ambition translate strategy into objectives agents can actually execute, not just plan around. The throughline for anyone running a team: your job is shifting from directing work to overseeing and auditing it, whether the worker is a person or a process. The tools that last will own the outcome a workflow produces, not just run its steps. Build the oversight layer before you scale the agents, not after they’ve made a mess you can’t trace.
— Hannah Grey VC, 6m, #agents, #governance, #systems
Sequence First
MIT looked at 300 enterprise AI deployments and found 5% delivered measurable returns. Five percent, against $30 to $40 billion spent. RAMP-d’s diagnosis: the pilots fail because companies launch them onto rotten foundations. Three gaps show up before every flop. Infrastructure deficits, dirty data, undocumented workflows, processes nobody actually owns. Weak business cases, vague goals with no number attached. And rushed timelines that skip straight to “let’s pilot it” before the groundwork exists. The fix is fewer, better-qualified pilots. Every win shares three traits: a narrow wedge of a use case, a named human owner, and an outcome tied to a metric someone’s accountable for. As Seneca wisely advises, if you don’t know which port you’re sailing to, no wind is favorable.
— RAMP-d, 8m, #ai, #transformation, #management
Four Questions
Most “AI strategy” decks are a list of tools nobody asked for. Gopi Kallayil offers a better starting point: stop cataloging features, start interrogating your industry. AI, he argues, is a cognitive amplifier that works everywhere because every business runs on ideas. So ask four questions. Where’s the friction, the weeks of waiting and handoffs you could compress into hours? Which “luxury” service do only premium customers get, and could you encode that expertise for everyone? Where could orchestration across vendors and touchpoints replace the manual chasing your people do all day? And what specialized knowledge lives in your experts’ heads that you’ve never captured? Notice that two of the four, orchestration and encoded expertise, are squarely about coordination, the connective work of getting people, systems, and now agents pointed at the same outcome. The real unlock comes from combining all four into something that didn’t exist before, the way Airbnb fused several technologies into a new category. Don’t ask what AI can automate. Ask what it lets you build that you couldn’t.
— Forward Future, 6m, #ai, #leadership, #strategy
QA FTW
We keep arguing about whether AI writes good code. Salvatore Sanfilippo says we’re aiming the tool at the wrong target. Let the model generate, sure, but the place LLMs are unambiguously better than the old way is QA. Testing has always leaked: coverage numbers lie, integration tests choke on timing and visual checks, and manual passes miss the weird states nobody thought to try. An AI agent doesn’t get bored. He points it at DwarfStar to hunt speed regressions across a rack of MacBooks, or hands Redis Arrays an agent that builds a real app, simulates a crowd of users for hours, and surfaces undocumented behavior the team forgot existed. The simple method: markdown files feed the agent context, commits, endpoints, config, plus a focused directive scoped to what actually changed. For anyone shipping fast with AI help, this is the move: velocity buys you a quality debt, and automated QA is how you pay it down without slowing the team.
— antirez, 7m, #ai, #engineering, #productivity
Hockney & Tech
David Hockney died last week at 88, having spent a career grabbing every new tool within reach: Polaroids, photocopiers, fax machines, an iPhone he filled with finger-drawn paintings, an iPad that produced 220 works for a single retrospective. He wasn’t chasing novelty, he was after process and distribution. He’d found that a photocopier let him “put something down, evaluate it, alter it, revise it, all in a matter of seconds,” the closest thing to painting he’d ever printed. However, he flatly turned down one technology, virtual reality, because it would “seal the viewer off.” Every other tool helped him make work and share it with people in the same room. VR isolated, so it was out. Hockney didn’t just find new ways of making pictures, his collaborator said, “He found new ways of sharing them with people.”
— The New York Times, 5m, #ai, #leadership, #creativity
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.
Early warning solves the lagging-indicator problem. By the time a goal officially flips to “at risk,” you’ve already lost the runway to do anything about it. This Echo reads the quiet signals instead: repeated intentions that never resolve, blockers that linger, progress updates that get vaguer each week. Every Wednesday morning it names the goals most likely to slip, with the evidence behind each call. You step in while course correction is still cheap, not after the deadline’s already gone. Catch the drift before it becomes a miss.
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.