@0xNoryxx: SENIOR ANTHROPIC ENGINEER MAKING $1.6M/YEAR PUBLISHED THE PLAYBOOK THAT REDUCED HIS WORKLOAD BY 95% you stop prompting …

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A senior Anthropic engineer published a playbook for building autonomous agentic loops with components like Generator, Evaluator, Memory, and Orchestrator, claiming it reduced his workload by 95% and doubled the firm's revenue.

SENIOR ANTHROPIC ENGINEER MAKING $1.6M/YEAR PUBLISHED THE PLAYBOOK THAT REDUCED HIS WORKLOAD BY 95% you stop prompting the agent - you build the system that prompts it instead - and that one shift changes everything Plan → Act → Evaluate → Learn → Adapt five steps, one cycle, runs forever without a human Generator: the agent that writes code - its only job is to produce - and it will always praise what it made Evaluator: a second agent told to assume the result is broken - the part that can say no - setting a skeptic is far easier than making a generator criticize its own work Memory: results written to disk, never left in a context window that gets flushed - the loop never forgets Orchestrator: spawns subagents for each task - nobody hands it a list - it decides what to do next on its own the same loop built by two people can produce opposite results - the parts are necessary but not sufficient - what makes the loop reliable is the mindset of the developer who built it this file doubled the firm's revenue and got his salary raised to $2.3M/year this playbook changed how I build agentic systems today
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SENIOR ANTHROPIC ENGINEER MAKING $1.6M/YEAR PUBLISHED THE PLAYBOOK THAT REDUCED HIS WORKLOAD BY 95%

you stop prompting the agent - you build the system that prompts it instead - and that one shift changes everything

Plan → Act → Evaluate → Learn → Adapt

five steps, one cycle, runs forever without a human

Generator: the agent that writes code - its only job is to produce - and it will always praise what it made Evaluator: a second agent told to assume the result is broken - the part that can say no - setting a skeptic is far easier than making a generator criticize its own work Memory: results written to disk, never left in a context window that gets flushed - the loop never forgets Orchestrator: spawns subagents for each task - nobody hands it a list - it decides what to do next on its own

the same loop built by two people can produce opposite results - the parts are necessary but not sufficient - what makes the loop reliable is the mindset of the developer who built it

this file doubled the firm’s revenue and got his salary raised to $2.3M/year

this playbook changed how I build agentic systems today

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