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

Your agent passed every test Monday. Same prompts Tuesday, three failed. Nothing changed.

Agents take a different path each run, so grade the trace, run 50+ cases per task, and report cost next to accuracy.

www.autolearningagents.com/ai-agent-eva...

#AIAgents #LLMOps

MaxKB's strongest signal is not model support. It is workflow engine plus MCP tool use. That is where enterprise agents stop looking like chat and start looking like software that can fit a business process. Process control is the costly capability. #LLMOps #Automation

One expensive integration shouldn’t consume the team’s quota. Put it behind its own virtual key, set a quota, and track cost by key—without restricting everyone else. #BYOK #LLMOps https://temprhq.io/gateway

Which workflow is burning through quota first? Compare virtual-key usage with daily cost by model in Tempr Gateway to isolate the client behind budget drift. #BYOK #LLMOps https://temprhq.io/gateway

That is why we built batch jobs to resume from the exact line they left off—no babysitting, no restarting from zero. #batchinference #selfhosting #gpuoptimization #llmops #localai #automation 2/2

Split-brain: when multi-agent memory consensus quietly fails

In distributed systems, split-brain is what happens when a partition convinces two halves of a cluster that each is…

https://loopandretry.github.io/posts/multi-agent-memory-split-brain/?ref=bluesky

#AIagents #LLMOps #AIengineering #LLM

Traced your LLM app? The same spans that debug a bad run are the raw material for an evaluation system. We just pointed our tracing post at the follow-up. go.upgradejs.com/3zn #LLMOps

Agents don't care which vendor logo is on the box.

They care about latency, cost, and whether the model actually runs.

DemonRoute — 560+ models, one API.

→ https://demonroute.com

#Inference #LLMOps #DemonRoute #CryptoBilling #SaaS

Not everything deserves a span. LLM calls, tool calls, retrieval, prompt version: yes. Generic infra spans: no, they bury the four things you actually need.
go.upgradejs.com/don #LLMOps

High throughput for the heavy lifting, zero lag for the creative work. Get started for free at wideareaai.com. #batchinference #selfhosting #gpuoptimization #llmops #throughput #aiinfrastructure 2/2

Would you accept fewer errors if p95 latency doubled? Compare p50 and p95 by model after changing a fallback chain. A lower error rate can still hide a painful user experience. #LLMOps #AIEngineering https://temprhq.io/gateway

What changed after the provider switch? Compare daily cost by model for each virtual key, and quotas can flag one team’s spend drift before it becomes a budget surprise. #BYOK #LLMOps https://temprhq.io/gateway

Which AI model fits the use case and budget? TemprHQ brings model listings and pricing together so development teams can compare options before implementation. #AIEngineering #LLMOps https://temprhq.io/changelog

Need AI usage visibility without exposing every request? Pair gateway usage tracking with request redaction in TemprHQ to make model traffic easier to review with less sensitive data.
#LLMOps #AIOps https://temprhq.io/changelog

Agents don't care which vendor logo is on the box.

They care about latency, cost, and whether the model actually runs.

DemonRoute — 560+ models, one API.

→ https://demonroute.com

#Inference #LLMOps #DemonRoute #CryptoBilling #SaaS

That is why we built batch jobs to resume from the exact line they left off—no babysitting, no restarting from zero. #batchinference #selfhosting #gpuoptimization #llmops #localai #automation 2/2

Prompts sind Code – also sollten sie getestet werden wie Code. Ein systematisches Framework fängt Regressionen ab, bevor sie in Produktion landen. #LLMOps

Mehr von mir: linkedin.com/in/maurice-putinas

What happens after your model eval? Write down the provider, model, task, price, fit, and key-storage notes before the result disappears into a doc. Tempr helps you gather the provider details. #AIDev #LLMOps https://temprhq.io/providers