AI Agent Management · Rick Wong
Turn scattered AI work into managed memory, clear ownership, and better operating decisions.
AIAM builds two practical operating layers: company brains for revenue lifecycle work and personal agents with durable context and safety boundaries. The point is not more agents. It is work that can remember, explain, and improve itself.
START
One workflow
MAKE
One artifact
LEARN
One review loop
Choose the operating layer
Start where the context loss is most expensive. The company path coordinates a team around revenue work. The personal path gives one operator a durable working partner.
Map where account context breaks across qualification, proposals, SOWs, forecasts, handoffs, renewals, and expansion—then install the owner, gate, artifact, and review loop that keep it intact.
Set up a practical operating partner with durable context, a clear source of truth, Telegram access, human approval gates, and one maintenance routine before adding more automation.
PUBLIC LAB / SUPPORTING PATH
Follow the real stack choices, operating agreements, failed assumptions, and human approvals behind the AI-native company experiment.
Read the lab notes ↗The expensive failure
The facts needed to qualify an account, shape a proposal, defend a forecast, or hand work to delivery are split across calls, CRM records, documents, Slack, support history, and a few people's heads. AI can make that fragmentation move faster. It cannot make it coherent by itself.
A company brain is not a chatbot over documents. It is managed memory around important work.
Where the durable facts, account history, and decisions live.
Who is accountable for quality, action, and escalation.
Where human judgment is required before the system acts.
The brief, proposal, handoff, or decision record the work produces.
The scorecard and cadence that turn outcomes into better behavior.
From the operating edge
The path from prompting AI to embedding intelligence in product workflows, engineering repeatable loops, and building personal agents and company brains.
Use an Agent Operating Record to connect each AI agent to a bounded job, an accountable owner, trusted evidence, and clear stop rules.
Use append-only events and attributed corrections so a company brain can show both what happened and what is true now.
NEXT MOVE / ONE WORKFLOW
Start with a few active opportunities, one recent artifact, and the place where account memory breaks. The first output is a map—not a platform mandate.
