AI Agent Loops Work Like Managed Teams
The useful work in agent automation is defining the goals, operating rules and handoffs that let specialised agents coordinate without constant instruction.
Read the snack ↗AI Loop Engineering
The useful work in agent automation is defining the goals, operating rules and handoffs that let specialised agents coordinate without constant instruction. Fast agent workflows create more room for iteration, but human judgement still has to redirect them before small choices compound into drift.
With Pete Winn and Andy David
7 ideas developed from this conversation
About this conversation
The useful work in agent automation is defining the goals, operating rules and handoffs that let specialised agents coordinate without constant instruction. Fast agent workflows create more room for iteration, but human judgement still has to redirect them before small choices compound into drift.
Ideas from Episode 61
Better AI Application Beats More Model Intelligence
When technical experimentation outruns the task, simpler systems can create more value from capabilities that already exist.
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Compression Cuts AI Agent Context Costs
Filtering repetitive tool output before it reaches an AI agent can cut token use while preserving the context that matters.
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Human Checkpoints Keep AI Agent Work Aligned
Fast agent workflows create more room for iteration, but human judgement still has to redirect them before small choices compound into drift.
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Judge AI Spending by Its Return
When AI turns a predictable software bill into variable consumption, businesses need to measure the extra value each increment of spending buys.
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Training AI Agents Reduces Key-Person Risk
Managing AI agents like new workers turns operational know-how into reusable business systems that improve their output and reduce dependence on individuals.
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Why More AI Output Isn't More Value
Agent loops can multiply activity at remarkable speed, but the work only matters when it produces something specific, useful and wanted.
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