HELM library
The operating vocabulary of AI-native operations, argued honestly.
Field notes by Sam M. Sweilem and research from LockedIn Labs product engineering. Practical guidance on AI control planes, observability, governance and the decisions operators need to make. Earlier articles and recorded views retain the AI Ops identity used when published.
- October 2, 20267 min readLockedIn Labs product engineering
The funnel said 100 percent. Two people were paged.
Report the rate that reached a person, per million, beside the counts it came from. A rounded reduction percentage is accurate until it becomes persuasive, and in operations it becomes persuasive at exactly the scale where it stops being true.
- October 2, 20267 min readLockedIn Labs product engineering
What changed since Monday is a better question than any dashboard
Show what moved since a fixed point, against the same days a week earlier, filtered by a materiality line per metric, with every sentence built only from two computed values. A leader's first question is about change, and change is where dashboards are weakest.
- October 2, 20268 min readLockedIn Labs product engineering
Claude in the control room: admitted evidence, not prompts
Never send it a prompt. Send it a fixed, server-built packet of admitted facts for a registered surface, accept only a structured interpretation that cites those facts, and refuse everything else at the boundary. The model explains; it never decides, executes or invents a source.
- September 8, 20264 min readSam M. Sweilem
The enterprise AI control plane: make authority visible
An AI control plane should connect each agent run to its purpose, evidence, permissions, decision owner and measured result. Start with one consequential workflow and prove those connections before extending autonomy across the enterprise.
- September 8, 20264 min readSam M. Sweilem
Connect network health to the customer conversation
Trace a customer journey through its queue, application, network dependencies and AI assistance. Correlate signals within a shared time window, show the evidence behind each hypothesis, and verify the effect on the customer before calling an incident resolved.
- September 6, 20269 min readLockedIn Labs product engineering
The operating loop: signal to verified outcome
An operating loop is not the dashboard where operators watch things happen. It is a chain of six specific handoffs, from a raw signal to a verified outcome, and most operations platforms don't publish what they show you at every one of those places.
- September 6, 20268 min readLockedIn Labs product engineering
Operations metrics that do not drift
Nine metrics dominate operations dashboards, and most of them get quietly redefined the moment a vendor needs a better story to tell. This piece gives the formula for each, the edge case that breaks it, and the question to ask when a number looks too good.
- September 6, 20267 min readLockedIn Labs product engineering
Evaluating an AI operations platform: the buyer's checklist nobody hands you
Most AI operations demos are optimized to be watched, not operated. This checklist is built around the questions worth demanding a demo prove in your own hands, before a contract gets signed.
- September 6, 20267 min readLockedIn Labs product engineering
AI-native compliance and audit: what an auditor should ask an AI vendor
An auditor evaluating an AI operations vendor is really asking one question in six disguises: can you prove, after the fact, exactly who or what did this, under what authority, and can that proof be tampered with. This piece works through the six disguises and what evidence should look like for each.
- September 6, 20267 min readLockedIn Labs product engineering
Agentic operations, honestly
Most of what's marketed as agentic autonomy in operations today is closer to a fast, well-organized recommendation engine than to an actor making its own consequential decisions, and that gap between the word and the practice matters before adopting any of it. This piece works through where autonomy is safe today, where it isn't, and the four-stage trust model that separates the two.
Two more open references
To see the product these pieces describe, start in the Proof room.