Enterprise AI teams are hitting a wall — not because their models can’t reason, but because the workflows underneath them were never built for agents. Tasks fail, handoffs break, and the problem compounds as organizations push agents deeper into back-office systems. A new architectural layer is emerging to address it: workflow execution control planes that impose deterministic structure on processes agents are expected to run.
One of the companies bringing this to the forefront is Salesforce, with a new workflow platform that turns back-office workflows into a set of tasks for specialized agents to complete. Users can upload their processes or use one of the set Blueprints provided by Salesforce, and Agentforce Operations will break it down for agents. — Read More
Daily Archives: May 4, 2026
Agent Skills
The default behaviour of any AI coding agent is to take the shortest path to “done.” Ask for a feature and it writes the feature. It does not ask whether you have a spec, write a test before the implementation, consider whether the change crosses a trust boundary, or check what the PR will look like to a reviewer. It produces code, declares victory, and moves on.
This is the same failure mode every senior engineer has spent their career learning to avoid. The senior version of any task includes work that doesn’t show up in the diff: surfacing assumptions, writing the spec, breaking the work into reviewable chunks, choosing the boring design, leaving evidence that the result is correct, sizing the change so a human can actually review it. Those steps are most of what separates engineers who ship reliable software at scale from people who push code that breaks.
Agents skip those steps for the same reason any junior would. They’re invisible. The reward signal points at “task complete” not “task complete and the design doc exists.” So we have to bolt the senior-engineer scaffolding back on.
Agent Skills is my attempt at that scaffolding. It just crossed 26K stars, so apparently I’m not alone in wanting it. This post is the part the README doesn’t quite cover: why each design choice exists, how it maps onto standard SDLC and Google’s published engineering practices, and what you should steal from the project even if you never install a single skill. — Read More
How We Built an AI Second Brain for 60K Knowledge Workers
Knowledge workers at Meta routinely contend with workflow fragmentation, where critical information — including meeting notes, tasks, key decisions, and code context — is siloed across disparate platforms. Each new AI conversation starts cold: the same explanations, the same links, the same ten minutes of context-setting before any real work begins.
So we tested a simple hypothesis: what if an AI agent had persistent, structured access to everything a person is working on, and carried that context across every interaction? Not a chatbot that answers questions, but a working partner that tracks projects, reads meeting notes, surfaces connections, and builds on prior conversations.
<brthat ai="" second="" brain="" experiment,="" born="" in="" the="" analytics="" org,="" has="" since="" been="" adopted="" by="" over="" 60,000="" people="" across="" meta:="" engineers,="" pms,="" designers,="" legal,="" finance,="" communications,="" and="" sales.="" this="" post="" covers="" how="" it="" was="" built,="" grew,="" what="" we="" learned.="" –="" Read More
AI Outperforms Doctors in Emergency Room Tasks, New Harvard Study Shows
An advanced AI agent has outperformed human physicians on a series of demanding tests that assess the ability to correctly diagnose patient illnesses in clinical settings, a Harvard-led study found. OpenAI’s “o1 preview,” the company’s first model capable of step-by-step reasoning, proved that it could conduct real world triage in emergency rooms, recommend appropriate diagnostic tests, and perform case management tasks at a level that matched or surpassed the ability of even well-trained human doctors.
The study, led by Harvard researchers with collaborators at Stanford and published today in Science, suggests an urgent need for controlled trials of the technology, the authors say, to determine how it can be most effectively deployed. — Read More
Rick's Cafe AI 