Mikyo
@mikeldking.bsky.social did:plc:nvwnb6g7kxgjnitgw2pqjyyi
Building the future of OSS AI observability ☕️🚲🧗🏻♂️🏂⛰🕴
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Mikyo (@mikeldking.bsky.social) é um usuário do Bluesky desde 12 julho 2023. Tem 54 seguidores, segue 29 contas e publicou 132 posts.
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One code-mode test I’d run: start a long query, interrupt after execution begins, then retry the same intent. PASS = cancellation and retry are separate traces, no duplicate side effects, and the agent can distinguish transport vs tool vs code errors.
Ver postAs agents gain capabilities due to skills, MCPs, and codemode, evaluating agents becomes increasingly more difficult because you need to test across multiple environments and harnesses. Arize Phoenix your benchmarking Harbor runs so pinpointing improvements and regressions becomes tractable.
Ver postWe put PXI, Claude Code, and Codex through the same tasks with Harbor + Phoenix Even when agents succeeded, their traces told different stories: tool calls, latency, and efficiency. Join Oct 8 at 11am PT / 2pm ET to learn how to compare agents and inspect ATIF traces. luma.com/arizeai-ben...
Ver postWe’re going live in 30 minutes for our webinar on Benchmarking AI Agents & Tool Use with Harbor and Arize Phoenix! Join here: luma.com/arizeai-ben...
Ver postnew baselining capabilities for experiments Now when you have a baseline set on your experiments or when you care comparing two experiments, you can see the deltas of your evals, tokens, latency and cost so you can quickly identify regressions and improvements.
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