If you think agents should do more than demos, this episode will hit close to home. Watch/listen:

3⃣ Adoption is blocked by workflows, not models. Agents won’t scale beyond engineering until we have GitHub-style systems for staging, approval, and tracking work.

Stories for the Industry, not the Consumer * Original Polls & Research * In-Depth Analysis & Op Eds * Breaking Updates Follow @CardRatesNews For Daily Coverage

2⃣ Harnesses lower the bar to building agents. Frameworks like Cloud Agent SDK and LangChain Deep Agents bundle the basics so more people can ship agents without weeks of setup.

And why enterprise infrastructure is lagging behind the ambition. A few takeaways: 1⃣Sandboxes make agents usable. Isolated compute lowers risk and finally lets agents use real tools. If the computer is the most powerful tool we have, keeping it away from agents makes no sense.

AI agents aren’t hitting limits because of models. They’re hitting limits because of bad environments. @jonathantwall joins @Dpbrinkm on the MLOps Podcast to talk about agent sandboxes, why agents are starting to behave more like coworkers than scripts,

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@mlopscommunity Really cool stuff being discussed.. super happy to be one of the panelists and share my experience with building AI agents and also vibe coding

If you’re building agents and still treating context as an afterthought, this episode will probably make you uncomfortable, in a good way. Listen here: https://t.co/2ZJBt2v0z2!

3⃣ Redis is betting on agents With @FeatureformML now inside @Redisinc, the focus is shifting toward agent-native infrastructure: context engines, richer MCP layers, and tooling built for systems that reason, not just query.

2⃣ Feature stores aren’t dying The “feature stores are obsolete” narrative doesn’t hold up. Demand is growing, especially where money is on the line, fraud, recommendations, and real-time decisions. The ROI is still very real.

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Here are 3 takes that might ruffle some feathers: 1⃣ Context ≠ RAG Context isn’t a retrieval hack. Simba frames it as a system-level concern: memory, structured data, and messy real-world data all working together. If your agent only knows how to fetch docs, it’s underpowered.

Just dropped: a spicy episode of the MLOps Community podcast, Context Engineering 2.0 with @simba_khadder. This convo pokes at some assumptions people keep repeating about agents, feature stores, and “context” in MLOps—and calls a few of them out as flat-out wrong.

Podcast link: https://t.co/9zDTRTPy4E Curious how you’re cutting down hallucinations and making these systems actually dependable. What’s been working for you?

If unreliable outputs are driving you up the wall or if you’re over the “just fine-tune it” crowd, this conversation hits the spot. No silver bullets, but the folks mixing creativity with disciplined frameworks are steadily pulling ahead.

Waiting for Santa like...

Recall metrics and LLM-driven feedback loops are becoming the unsung workhorses here, surfacing all the flaws most teams don’t notice until it’s too late.

Turns out solid structure beats model size more often than people care to acknowledge. 3⃣ Evaluating RAG Gets Messy Fast: You’re testing retrieval, prompt construction, and model output, all at once.

Tighter roles, clearer logic, fewer “why did it say that?” moments. 2⃣ Dynamic Prompts Matter More Than Anyone Wants to Admit: These agents build prompts on the fly based on the actual question and data. When the context is dense and specific, hallucinations drop fast.

Here are the three points that really stuck: 1⃣ RAG Without the Fluff: RAG is basically an open-book exam models grab what they need instead of hallucinating. Agentic RAG stretches this idea by splitting the workload across smaller, focused agents.

Worship God

Recently on the MLOps Community Podcast, @Dpbrinkm sat down with @SatishBhambri to crack open what’s going on with Agentic RAG. If you’ve been watching AI/ML systems evolve and feel like the whole space is getting a little… unhinged, this episode lands hard.

@mlopscommunity @Dpbrinkm @Airia_AI Structured AI integration is essential. Guardrails and access control keep agents from turning into 3am chaos.

The conversation hits a nerve: scaling AI isn’t a technical puzzle or a business puzzle, it’s both, at the same time. https://t.co/SLwxiABJOM Curious how much of this would fly (or fail) inside your org?