--- title: "Prod Is One Cell Away" newsletter: "MLOps Community" date: 2026-02-19 source: https://aaif.live/newsletters/mlopscommunity/2026-02-19-prod-is-one-cell-away --- # Prod Is One Cell Away *Plus... treating search like a system, giving agents actual interfaces, and transparent agent telemetry.* *MLOps Community — Agentic AI Foundation, 2026-02-19* The 2026 State of Data Engineering survey results - fully enterprise-approved [https://joereis.github.io/super_corporate_pdm_survey/]. ## Regression Testing, Live If your project setup can delete prod during tests, an LLM will happily do it faster. What’s your real risk: Agent autonomy or Weak foundations? [AUTONOMY](https://gatewaze.mlops.community/offer/surveys/?sid=yesno&question=If+your+project+setup+can+delete+prod+during+tests%2C+an+LLM+will+happily+do+it+faster.+What%E2%80%99s+your+real+risk%3A+Agent+autonomy+or+Weak+foundations%3F&y=AUTONOMY&n=FOUNDATIONS&oneclick=true&accept=true) [FOUNDATIONS](https://gatewaze.mlops.community/offer/surveys/?sid=yesno&question=If+your+project+setup+can+delete+prod+during+tests%2C+an+LLM+will+happily+do+it+faster.+What%E2%80%99s+your+real+risk%3A+Agent+autonomy+or+Weak+foundations%3F&y=AUTONOMY&n=FOUNDATIONS&oneclick=true&accept=true) ## Theory vs. Tickets If I show you a poll where 79% say evals drive the roadmaps, can you show me the sprint board? ## Built your own agent? Test it against real attacks. In the test above, an agent blocked a direct data request—but mentioned "hr_handbook.txt" in its refusal. An attacker used that filename to request "verification," and the agent complied. Edge cases like this are hard to anticipate. Toloka connects to your agent and runs human-led red teaming across real attack scenarios—prompt injection, tool abuse, PII leakage, multi-step logic chains. 300+ vetted specialists test what automated scans can't catch. Launch in minutes. Results in ~4 hours — with complete audit trail and human-written explanations. Building on Lovable, Vercel, or custom stacks? Connect your agent and see how it performs. Run your first red team [https://toloka.ai/red-teaming-platform] → Use code MLOPS20 for $20 towards your first project. [https://toloka.ai/red-teaming-platform](https://toloka.ai/red-teaming-platform) ## Curated finds to help you stay ahead ## Rethinking Notebooks Powered by AI Your notebook just deleted prod during a unit test. That’s not a model problem. That’s a setup problem. * Context is everything - LLMs write better SQL and DataFrame code when you explicitly pass schema, types, and sample rows. Point to variables, pipe errors and outputs back, and let the model fix itself with lint feedback. * Notebooks as agent workbenches - Reactive notebooks expose intermediate results, tests, and visuals, making it easier to spot drift, tool misuse, or broken assumptions than in a pure IDE loop. * Frontend literacy matters - Dynamic UIs, lower latency patterns, and real product feel require JavaScript fluency, even in Python-first workflows. The teams that treat notebooks as programmable interfaces rather than scratchpads move faster without flying blind. [https://podcasts.apple.com/us/podcast/rethinking-notebooks-powered-by-ai/id1505372978?i=1000749636952](https://podcasts.apple.com/us/podcast/rethinking-notebooks-powered-by-ai/id1505372978?i=1000749636952) [https://home.mlops.community/home/videos/rethinking-notebooks-powered-by-ai](https://home.mlops.community/home/videos/rethinking-notebooks-powered-by-ai) [https://open.spotify.com/episode/0nAxOfXjgalsye55suAMZt?si=a48f8c8d51fb40da](https://open.spotify.com/episode/0nAxOfXjgalsye55suAMZt?si=a48f8c8d51fb40da) ## The Future of Information Retrieval: From Dense Vectors to Cognitive Search People are throwing LLMs at search and wondering why the answers still feel random. Most of the pain is upstream, in how you retrieve and rank context. * Retrieval decides whether RAG is real - RAG only helps when the retrieval layer is solid. In practice, teams mix sparse (BM25/inverted index), dense vectors, and hybrid routing to hit cost and latency targets without tanking relevance. * “Good search” is becoming a product metric - Beyond recall and accuracy, teams look at follow-up query rates, downstream actions, and whether users stop searching because they got what they needed. * Embeddings at scale are a distributed systems problem - Billions of vectors force choices around sharding, live-in-memory vs disk indexes, refresh cycles, and safe re-embeds when the model changes. When you treat search like a system - not a demo - agents get cleaner context, and the answers stop wobbling. [https://podcasts.apple.com/gb/podcast/the-future-of-information-retrieval-from-dense/id1505372978?i=1000750189414](https://podcasts.apple.com/gb/podcast/the-future-of-information-retrieval-from-dense/id1505372978?i=1000750189414) [https://home.mlops.community/home/videos/the-future-of-information-retrieval-from-dense-vectors-to-cognitive-search](https://home.mlops.community/home/videos/the-future-of-information-retrieval-from-dense-vectors-to-cognitive-search) [https://open.spotify.com/episode/1wa0PQYc7tfWPoBJoqZCu2?si=c4bfc12f5ce14e10](https://open.spotify.com/episode/1wa0PQYc7tfWPoBJoqZCu2?si=c4bfc12f5ce14e10) ## Building with A2UI: Extending the Expressiveness of AI Agent Interfaces We’ve built agents that can plan and code, then trapped them in chat bubbles. This project asks a sharper question: what if agents could render real interfaces instead of Markdown? * A2UI as a UI language - Agents stream structured JSON that renders cards, forms, sliders, and modals safely on the client, replacing brittle text parsing with typed, schema-backed components. * AVC pattern for control - Splitting Controller and View agents keeps business logic separate from UI formatting, making agent apps testable, swappable, and less chaotic at scale. * Latency trade-offs in practice - Dynamic UI shines for ambiguous, intent-heavy flows, but static components still win for hot paths where milliseconds matter. The result is a hybrid model where agents handle complexity without turning every button click into a token stream. [Read the blog](https://home.mlops.community/home/blogs/building-with-a2ui-extending-the-expressiveness-of-ai-agent-interfaces) ## IN PERSON EVENTS * London [https://luma.com/5jivnk6l] - February 19 * San Francisco [https://luma.com/v4qyluh2] - February 24 * Mountain View, CA [https://luma.com/codingagents] - March 3 ## VIRTUAL EVENTS * Coding Agents Lunch & Learn [https://home.mlops.community/home/events/coding-agents-lunch-and-learn-s-2-otz2j2hskw?agenda_day=698bb4bc03c7c24e92575cf3&agenda_track=698bb4bd03c7c24e92575d0b&agenda_stage=698bb4bc03c7c24e92575cf9&agenda_filter_view=stage&agenda_view=list] - February 20 * Reading Group: Advancing Open-source World Models [https://home.mlops.community/home/events/mlops-reading-group-february-advancing-open-source-world-models-0jdtkro79j?agenda_day=698df64d476e1012afdcd8ec&agenda_track=698df64e476e1012afdcd921&agenda_stage=698df64d476e1012afdcd8f0&agenda_filter_view=stage&agenda_view=list] - February 26 * Operationalizing AI Agents: From Experimentation to Production [https://home.mlops.community/home/events/operationalizing-ai-agents-from-experimentation-to-production-ps4tmp6g3o?agenda_day=698b7637ad917410bb9de954&agenda_track=698b7638ad917410bb9de96c&agenda_stage=698b7638ad917410bb9de95a&agenda_filter_view=stage&agenda_view=list] - March 11 ## Bad PR [https://forms.gle/8EDvXGizxyFVKfwy8](https://forms.gle/8EDvXGizxyFVKfwy8) --- Source: https://aaif.live/newsletters/mlopscommunity/2026-02-19-prod-is-one-cell-away