AI Agent Management · Rick Wong
I help people and teams manage agents as operating capability: company AI workflows with owners and scorecards, personal agents with durable context and safety boundaries, and AI-native companies built through agent collaboration.
The problem is not just model quality. It is the missing management layer around agents: source of truth, permissions, workflows, approval gates, review cadence, and measurable outcomes.
Turn pilots, agents, workflows, data, owners, decision rights, and scorecards into a 90-day operating map.
Explore →Install a LifeOS-style operating partner with durable context, Telegram access, safety gates, and setup prompts.
Explore →Watch personal agents collaborate to design, staff, and operate a company while the humans improve the agents.
Explore →Expensive failure pattern
Core framework
AI enablement is an intervention into how work moves, who decides, what becomes visible, and which incentives change. Agent management only works when the company maps the workflow, locks the official KPIs, names the hidden incentive risks, defines the approval boundaries, and reviews consequences before scaling.
New article: read the full intervention playbook.
Latest operating playbook
A practical scorecard for AI pilots: intended outcome, affected workflows, second-order effects, approval gates, rollback conditions, stop rules, and review cadence before production pressure arrives.
Why these paths connect
A personal agent proves whether durable memory, approval gates, and source-of-truth boundaries work for one operator. A company diagnostic applies the same primitives to teams, workflows, data, and measurable outcomes. The AI-native company journey tests what happens when multiple personal agents collaborate around a shared company operating system.
Featured field guide
A flagship essay on why AI tools and agents fail without an operating system: workflow wedges, KPI-locked scorecards, owners, approval gates, pilot consequence review, and weekly cadence.
Newest insight
A practical scorecard for AI pilots: intended outcome, affected workflows, second-order effects, approval gates, rollback conditions, stop rules, and review cadence before production pressure arrives.
Latest operator note
A LifeOS operator note on why recurring AI work needs owners, durable routines, decision logs, drift checks, and a weekly operating review—not just more automations.
New to the operating lab behind these notes? Start with What Is LifeOS?
Ready for an operating map?
If a company already has pilots, agents, workflows, and data moving faster than ownership and measurement, the diagnostic maps the workflow, KPI-locked incentive alignment, decision rights, risks, and 90-day consequence review.
