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valuationagentStop tuning LLM agents with live API calls: A simulation-based approach
<p>LLM agent configuration is a surprisingly large search space, including model choice, thinking depth, timeout, and context window. Most teams pick a setup once and never revisit it. Manual tuning with live API calls is slow and expensive, and usually only happens after something breaks.</p> <p>We explored a different approach: simulate first, then deploy. Instead of calling the model for every trial, we built a lightweight parametric simulator and replayed hundreds of configuration variants offline. A scoring function selects the lowest-cost configuration that still meets quality requirements.</p> <p>The full search completes in under 5 seconds.</p> <p>A few patterns stood out:</p> <ul> <li>Many agents are over-configured by default </li> <li>Token usage can often be reduced without imp
The home stretch
<p>I never thought I’d be someone who actually thought it was a good idea to download GitHub as an App on my phone, but here we are. Charting territory I honestly felt like I didn’t belong in. To be honest, even working IT, there were moments where I just didn’t think I met the “mark” of someone who was “passionate” enough to do something like this. </p> <p>For the first time in my career, I do feel that I’ve found enough “passion” to warrant having access to my coding agent at all times. Now that I've entered the testing phase of the homeschool site build out, I've been using GitHub Copilot to help me resolve the issues that pop up as I'm walking through the workflows for the site. </p> <p>First, I've begun to use the issues on the repository to assign work to Copilot. The past few weeks
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