Adaline

@tryadaline

Iterate, evaluate, deploy, and monitor LLMs.

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adaline.ai

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Adaline
@tryadaline
Aug 27
Your AI agent is either improving or decaying. And the dashboard you have does not show the difference. Observability shows what the agent did. Evals score it. Neither catches decay. The self-improvement layer catches it. It sits between your agent and production, turning
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Adaline
@tryadaline
Aug 26
A good agent works at launch. But a great agent improves after launch. That requires a complete self-improvement loop: • Capture traces: Record decisions, tool calls, outputs, and failures—not just success rates. • Map real behavior: Cluster traces by what the agent is
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Adaline
@tryadaline
Aug 25
Using the same model family to generate and judge your outputs isn’t evaluation. It’s self-grading. Three biases that don’t show up in aggregate agreement scores but consistently show up in practice: 1. Position bias: Give the model two responses, and it favors whichever
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Adaline
@tryadaline
Aug 24
Monitoring tells you that an agent failed, but observability tells you which step in the sequence caused it. For a single LLM call, the distinction barely matters. For a multi-step agent with tool calls, branching logic, and intermediate states, the distinction is the difference
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Adaline
@tryadaline
Aug 21
The model stopped being the variable. Frontier models commoditized between mid-2024 and late 2025. The difference between top-tier closed models on production tasks is compressed into the noise floor. Same model, same task, dramatically different outcomes depending on its
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