Goal drift: how a multi-agent crew ends up solving a different problem

Loop drift is one agent staying busy without getting closer to done.

https://loopandretry.github.io/posts/multi-agent-goal-drift/?ref=bluesky

#AIagents #LLMOps #AIengineering #LLM

Is your provider slow, or is one workflow spiking? Compare p50 with p95: a high p50 means broad slowness; a wide gap means occasional tail latency. #LLMOps #AIInfrastructure https://temprhq.io/gateway

Which model is actually cheapest after failures? Compare cost per successful response by model using Gateway logs, then weigh errors and latency before changing providers. #LLMOps #BYOK https://temprhq.io/gateway

How long has your model shortlist stayed accurate? Make it a quarterly checklist: recheck reference cost, context window, and required capabilities for every workflow, then replace models that drift out of fit. #AIModels #LLMOps https://temprhq.io/models

Every AI output: approved. Three batches, green across the board. I should have felt relieved. Instead I felt the floor tilt. A gate that never closes is not a gate. https://praveenlavu.com/dispatch/the-test-that-has-to-fail/ #LLMOps

RAG運用の難所「評価自動化」を達成！アイレットが生成AI評価基盤を構築。

・Ragasで回答精度判定を自動化
・Datadog LLM Observabilityで可視化
・サポデ業務の工数を大幅削減

自動評価×可視化で定量改善を回すLLMOpsの模範解答！

#生成AI #LLMOps

What will your teammate need to reproduce your model choice? Record the task, model, provider, price, and key-storage notes while evaluating in Tempr. #AIDevelopment #LLMOps https://temprhq.io/providers

Which matters more for this task: a quicker answer or a costly failure? Score latency tolerance and task risk separately before choosing a default model. Tempr helps you browse models and pricing across IDE, terminal, and API. #AIDev #LLMOps https://temprhq.io/providers

Built an orchestrator. Every metric was green. Then I read the routing traces carefully and saw it had been routing commercial work through models that explicitly prohibit it. Quietly. Confidently. The whole time. https://praveenlavu.com/dispatch/the-router-didn-t-know-the-rules/ #MLOps #LLMOps

Which virtual keys burn tokens and still fail? Compare token usage with error rates to find costly, unreliable integrations before you optimize the wrong workflow. #BYOK #LLMOps https://temprhq.io/gateway

Which virtual key drove this month’s provider bill? Reconcile Gateway token and cost metrics by key and model before the billing cycle closes, while mismatches are still traceable. #BYOK #LLMOps https://temprhq.io/gateway

Which fallback events are intentional? Compare chain logs by model and virtual key: repeatable paths suggest planned failover; error and latency bursts suggest provider instability. #LLMOps #BYOK https://temprhq.io/gateway

A virtual key named team-app-env turns a usage audit into a filter, not a scavenger hunt. Keep the pattern consistent, then compare logs by team, model, cost, latency, and errors. #BYOK #LLMOps https://temprhq.io/gateway

Before deactivating an “unused” virtual key, check its request-log patterns. Recent models, timing, and error bursts can reveal a live workflow. #BYOK #LLMOps https://temprhq.io/gateway

Ever see a quota jump and wonder when it started? Compare request logs by day across matching weekdays, then trace the spike to its virtual key or model. #BYOK #LLMOps https://temprhq.io/gateway

Are fallback calls hiding avoidable spend? Compare fallback-chain traffic with cached responses in Tempr Gateway, then inspect logs by key and model to see where repeated work still reaches a provider. #LLMOps #BYOK https://temprhq.io/gateway

Which model works across your IDE, terminal, and API? A compact matrix for provider, model ID, auth, streaming, and pricing can expose gaps early. TemprHQ helps you gather those details. #AIDev #LLMOps https://temprhq.io/providers

A weekly cache audit by virtual key shows which workflows keep paying for repeated provider calls. Use Gateway logs to compare patterns, cost, and latency. #TemprHQGateway #LLMOps https://temprhq.io/gateway

jev-harness wraps the TypeSafe Jev API to ship LLM decisions in production. Adds policy mapping, confidence gates, shadow mode, and offline evals. Devs report 1.3s vs 48.9s for Claude CLI on a row filtering task. Supports custom backends. #llmops #tool https://github.com/AntonioCoppe/jev-harness