I've built 12+ production AI agent systems across development, DevOps, and data operations. Here's why the current hype around autonomous agents is mathematically impossible and what actually works in production.
TL;DR: Three Hard Truths About AI Agents
After building 12+ production systems, here’s what I’ve learned:
-Error rates compound exponentially in multi-step workflows. 95% reliability per step = 36% success over 20 steps. Production needs 99.9%+.
Context windows create quadratic token costs. -Long conversations become prohibitively expensive at scale.
-The real challenge isn’t AI capabilities, it’s designing tools and feedback systems that agents can actually use effectively.
The TL;DR of the TL;DR is compounding expensive, error-prone results.
It sounds like one should be building deliberate AI workflows with extra checks (automated or human in the loop) that make careful and cost efficient incremental progress toward a measurable goal.
Sounds like hard work… when we could just build 1,000,000 MCP servers instead. (raises pinkie to corner of mouth)
The TL;DR of the TL;DR is compounding expensive, error-prone results.
It sounds like one should be building deliberate AI workflows with extra checks (automated or human in the loop) that make careful and cost efficient incremental progress toward a measurable goal.
Sounds like hard work… when we could just build 1,000,000 MCP servers instead. (raises pinkie to corner of mouth)
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