Adaline (@tryadaline) / X

Adaline

569 posts

Adaline

@tryadaline

Iterate, evaluate, deploy, and monitor LLMs.

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Joined January 2024

Adaline reposted
Arsh Shah Dilbagi
@arshdilbagi
Jun 13
Introducing Adaline 2.0 - The Agent Self-Improvement Layer Adaline turns Traces into Behaviors, Behaviors surface Issues, Issues become auto-generated Evals + Data, Adaline then generates new agent candidates and tests them. You review the winners and ship!
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742K
Adaline
@tryadaline
Jun 12
Replying to @tryadaline
Where tool calls actually break
Why AI Agents Call the Wrong Tool — and How to Fix It
From labs.adaline.ai
83
Adaline
@tryadaline
Jun 12
Replying to @tryadaline
Two reads worth pairing on this: Agent observability, end-to-end
AI Agent Observability: Why Latency Charts Miss The Failures That Matter
From adaline.ai
124
Adaline
@tryadaline
Jun 12
A failed tool-calling agent rarely fails at the model. It fails in the span. Four checks that find the cause faster than any model debugging: 1. Look at the step, not the output. 2. Check the tool contract. 3. Replay the state at the failed step. 4. Look at retries.
107
Adaline
@tryadaline
Jun 11
Replying to @tryadaline
Link to the entire blog:
What Is Prompt Engineering? A Complete Guide From Definition To Production
From adaline.ai
47
Adaline
@tryadaline
Jun 11
Prompt engineering without a success criterion is not engineering. It is guesswork with a GPU attached. Most teams iterate on prompts by feel, deploy without a regression test, and discover production failures from user complaints rather than from evals. When something breaks,
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Adaline
@tryadaline
Jun 10
Replying to @tryadaline
Why AI Agents Call the Wrong Tool — and How to Fix It
From labs.adaline.ai
52
Adaline
@tryadaline
Jun 10
Your agent called the wrong tool. To resolve the issue, you rewrote the system prompt, reran the test, and watched it call the wrong tool again. The system prompt was never the problem. The model selects its tool before writing a single word, and that decision is based entirely
77
Adaline
@tryadaline
Jun 9
Replying to @tryadaline
Observability vs. monitoring for agentic AI — what the distinction actually means in production:
Observability vs Monitoring for Agentic AI Products
From labs.adaline.ai
51
Adaline
@tryadaline
Jun 9
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
88
Adaline
@tryadaline
Jun 8
Replying to @tryadaline
Read the full blog here:
LLM-as-a-Judge: Why Frontier Models Fail 50%+ Bias Tests
From adaline.ai
30
Adaline
@tryadaline
Jun 8
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
47
Adaline
@tryadaline
Jun 5
Replying to @tryadaline
And the coding-agent specific version covers what changes when the agent’s job is to ship a PR. The piece walks through how to score traces, what production noise to seed into the eval set, and how to read merge rate versus pass rate.
How to Evaluate Coding Agents in Production | Adaline Labs
From labs.adaline.ai
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