---
title: "Passing Tests, Failing Humans"
newsletter: "MLOps Community"
date: 2025-12-11
source: https://aaif.live/newsletters/mlopscommunity/2025-12-11-passing-tests-failing-humans
---

# Passing Tests, Failing Humans

*Plus… safer agent rollout patterns, NVIDIA’s real bottlenecks, and this week’s hidden gems*

*MLOps Community — Agentic AI Foundation, 2025-12-11*

Putting the Anthropic into philanthropic [https://go.mlops.community/NL_Intro_Dec11].

Maybe they're after one of the awards we're planning for 2025. Hit reply to let me know your ideas for categories and nominees. Can be serious, like best technical blog, or funny, like most overhyped term.

## The Standardization Standoff

Handing MCP to a foundation won’t fix fragmentation if vendors keep shipping their own tool APIs.

So what shapes the future - Standards or Silos?

## A clear signal

This could signal a shift away from scaling alone, with most choosing quality information to guide behaviour.

## Hidden Gems

## Job of the Week

[https://go.mlops.community/NL_Jobs_Board](https://go.mlops.community/NL_Jobs_Board)

## How Sierra AI Does Context Engineering

Your CI can be green while an AI agent quietly fails the moment a real caller speaks - mishearing them, stalling, or even leaking data.

 * AI reverses old software trade-offs: slower, pricier, non-deterministic, so tests become repeated simulations, not single unit runs.
 * Each scenario runs 5-15 times with three agents - user, agent, evaluator - plus LLM-as-judge checking task-specific checklists.
 * Voice adds noisy environments and accents, while a model constellation runs tools and retrieval in parallel to stay responsive.

Wire these critical simulations into CI/CD and you catch the failure modes before customers ever reach support.

Video [https://go.mlops.community/NL_Pod1_G_Dec11] || Spotify [https://go.mlops.community/NL_Pod1_S_Dec11] || Apple [https://go.mlops.community/NL_Pod1_A_Dec11]


GOVERNANCE FOR AI AGENT DEPLOYMENT

The sharpest risk in your AI stack is not hallucination - it is an unsupervised agent with an API key wired into production.

 * LLMs turn chaotic spreadsheets, tickets, and emails into unified customer profiles and incident timelines leaders can query in seconds instead of chasing people.
 * Productive agents behave like scoped interns, with clear instructions, tool governance, and spend limits rather than free rein over systems and data.
 * Robust red teaming, LLM-as-judge plus human review, and identity-aware tool access stop “Johnny Drop Tables” moments before they hit prod.

Treat those three as non-negotiables and agents shift from toy demos to real operational leverage.

Video [https://go.mlops.community/NL_Pod2_G_Dec11] || Spotify [https://go.mlops.community/NL_Pod2_S_Dec11] || Apple [https://go.mlops.community/NL_Pod2_A_Dec11]


MAPPING NVIDIA'S FULL GENAI TOOLCHAIN

A surprising pattern cuts through NVIDIA’s full GenAI stack: every layer hides a bottleneck that becomes unavoidable once models hit real production scale. This guide maps where those pressure points show up and what engineers can actually control.

 * Building and fine-tuning: NeMo, TAO, and CUDA-X libraries shape how far you can push large models before memory and parallelism limits bite.
 * Data and deployment: RAPIDS, DALI, TensorRT, and Triton define your latency floor and throughput ceiling.
 * Orchestration and hardware: GPU Operator, NIM, DGX, and Grace Hopper decide how smoothly you can scale or recover under load.

Together, these layers show how to turn a prototype LLM workflow into something that won’t buckle once real traffic arrives.

Read the blog [https://go.mlops.community/NL_Blog_Dec11]

[Video](https://go.mlops.community/NL_Pod1_G_Dec11)

## Successfully Incomplete

[https://go.mlops.community/NL_Confess](https://go.mlops.community/NL_Confess)

## Making the hard stuff simpler

Working on something tricky or planning ahead? Here’s how we can help - just hit reply:

 * Custom workshops tailored to your company’s needs
 * Hiring? I know some quality folks looking for a new adventure
 * Want to connect with someone tackling similar problems? I can introduce you

Thanks for reading, catch you next time!

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Source: https://aaif.live/newsletters/mlopscommunity/2025-12-11-passing-tests-failing-humans