Topic hub / AI operations
AI agents for operators
A practical map for operators who want agents to remove real bottlenecks: SOPs, workflows, inboxes, QA, reporting, and follow-up loops.
Short answer
The useful agent starts as an operating loop.
Start with repeatable work, not a flashy demo. The agent needs a narrow job, a source of truth, a human owner, and a visible exception path before it deserves more autonomy.
Curated links
Start here
Dive
What is agentic coding?
The technical framing for agents that can actually do work inside a system.
Radar
Radar evidence feed
Signals, examples, and new agent patterns worth tracking before they become consensus.
Thought
Agentic SDR stack
How AI should support outbound without removing human judgment from the sales motion.
Venture
AI Operator
The venture lens: operator-first AI that fits the workflow before it claims autonomy.
Stack
ChatGPT and Gemini
The current tools I reach for when shaping agent workflows and research loops.
Feed
Recent operator notes
Fresh mentions, drafts, and observations that do not need to become full essays yet.
Operating checklist
Pilot → permissions → measurement
Set the baseline, owner, weekly gates, and stop rule with a bounded 30-day pilot.
Before execution, map read, draft, write, send, spend, delete, and administer actions by risk.
Once live, measure task success, accepted-output quality, tool behavior, latency, cost, and failure severity.
Keep a human escalation point until the failure modes are boring.
Review agent output like a process metric, not like a magic trick.
