9 min read
Choosing an AI Agent Platform as a Solo Founder
Stop asking which AI agent platform is best. Use the axes that actually decide it for a solo founder, then map each category to where it fits.
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Practical posts on turning recurring work into controlled Task Machine workflows with approvals, checks, agents on your own machines, and a history you can read.
9 min read
Stop asking which AI agent platform is best. Use the axes that actually decide it for a solo founder, then map each category to where it fits.
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11 min read
Running a company with AI works as a controlled operating model, not a black box. Here is the model: workflows, one inbox, autonomy levels, budgets, verifiers.
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8 min read
A step-by-step operating model for running a coding agent through your existing tracker, repo, and review step, gated by planning and risk scoring.
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10 min read
Why an AI-native company is a team where humans and agents share the work, not an org chart of role-bots you approve from above.
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7 min read
When an agent's work fails a check, it needs an owner, the evidence, and a next action. That is inbox work, not another alert to scroll past.
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8 min read
Most work you hand an agent has no automatic pass or fail. Here is how to decide it is good enough without redoing it yourself.
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7 min read
Autonomous agents can run up real costs through retry loops and runaway work. Set budgets, retry limits, and stop conditions before the spend, not after.
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7 min read
The same work comes back every week. Here is an operating model for turning recurring jobs into repeatable workflows you can hand off and trust.
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7 min read
Why reliable agent work depends on verification, context, approvals, and durable workflow history, not just better agent interfaces.
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6 min read
Why approvals, questions, failures, and exceptions need a shared inbox before agent work becomes trustworthy at company scale.
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7 min read
Why boards help visibility but fail to provide the approvals, verification, history, and control recurring agent work actually needs.
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7 min read
How small teams can assign work to agents without losing review quality, context, ownership, or accountability.
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