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Notes on building products end to end, the AI workflows that speed the work up, and the tooling in between. Written from production, not from a tutorial.
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A deep dive into the manager/worker pattern in multi-agent AI systems: how a manager agent decomposes a goal, delegates subtasks to worker agents, coordinates sequential vs parallel execution, handles failures, and integrates results.
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After a year of building MCP servers for real workflows, the tool surface matters more than the model behind it. Seven rules I now apply before a tool ships.
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