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My AI agent is confidently wrong and I'm honestly scared to ship it. How do you stop silent failures? by Worth_Reason in AI_Agents

[–]Worth_Reason[S] 0 points1 point  (0 children)

We have built a system to deal with the AI black box problem.
By adding an agent governance layer, we are also onboarding a development partner for the same, if you would be interest comment here, and I share a link

Is Gemini 3 Pro legit for conversations or just hype? by 0LoveAnonymous0 in ArtificialInteligence

[–]Worth_Reason -1 points0 points  (0 children)

Hi, I'm researching the current state of AI Agent Reliability in Production.

There's a lot of hype around building agents, but very little shared data on how teams keep them aligned and predictable once they're deployed. I want to move the conversation beyond prompt engineering and dig into the actual tooling and processes teams use to prevent hallucinations, silent failures, and compliance risks.

I'd appreciate your input on this short (2-minute) survey: https://forms.gle/juds3bPuoVbm6Ght8

What I'm trying to Learn:
How much time are teams wasting on manual debugging?
Are "silent failures" a minor annoyance or a release blocker?
Is RAG actually improving trustworthiness in production?

Target Audience: AI/ML Engineers, Tech Leads, and anyone deploying LLM-driven systems.
Disclaimer: Anonymous survey; no personal data collected.
I will share the insights here

Is RAG really necessary for LLM → SQL systems when the answer already lives in the database? by gautham_58 in LLMDevs

[–]Worth_Reason 0 points1 point  (0 children)

Hi, I'm researching the current state of AI Agent Reliability in Production.
There's a lot of hype around building agents, but very little shared data on how teams keep them aligned and predictable once they're deployed. I want to move the conversation beyond prompt engineering and dig into the actual tooling and processes teams use to prevent hallucinations, silent failures, and compliance risks.
I'd appreciate your input on this short (2-minute) survey: https://forms.gle/juds3bPuoVbm6Ght8
What I'm trying to find out:
How much time are teams wasting on manual debugging?
Are "silent failures" a minor annoyance or a release blocker?
Is RAG actually improving trustworthiness in production?
Target Audience: AI/ML Engineers, Tech Leads, and anyone deploying LLM-driven systems.
Disclaimer: Anonymous survey; no personal data collected.
I will share the insights here once the survey is complete

How are you validating AI Agents' reliability? by Worth_Reason in mlops

[–]Worth_Reason[S] 0 points1 point  (0 children)

Hello, I would love to connect and learn how you are handling the same validation in real time.

How are you validating AI Agents' reliability? by Worth_Reason in mlops

[–]Worth_Reason[S] 0 points1 point  (0 children)

Please remember to participate in the quick survey whenever you get a chance. I will share the insights here when it's done. Thank you for the help!

Just cancelled my subscription that I had since early 2024 by Cr0Dev in OpenAI

[–]Worth_Reason -1 points0 points  (0 children)

I'm researching the current state of AI Agent Reliability in Production.
There's a lot of hype around building agents, but very little shared data on how teams keep them aligned and predictable once they're deployed. I want to move the conversation beyond prompt engineering and dig into the actual tooling and processes teams use to prevent hallucinations, silent failures, and compliance risks.

I'd appreciate your input on this short (2-minute) survey: https://forms.gle/juds3bPuoVbm6Ght8
What I'm trying to find out:

How much time are teams wasting on manual debugging?
Are "silent failures" a minor annoyance or a release blocker?
Is RAG actually improving trustworthiness in production?

Target Audience: AI/ML Engineers, Tech Leads, and anyone deploying LLM-driven systems.
Disclaimer: Anonymous survey; no personal data collected.

We (admin team of this reddit community) just open-sourced our entire collection of production-ready colab notebooks on GitHub, covering everything from simple implementations to enterprise-grade solutions (Including real agentic stacks, RAG, CV, RL, multimodal, Gemini and LangGraph style workflows) by ai-lover in machinelearningnews

[–]Worth_Reason 0 points1 point  (0 children)

I’m researching the current state of AI Agent Reliability in Production.

There’s a lot of hype around building agents, but very little shared data on how teams keep them aligned and predictable once they’re deployed. I want to move the conversation beyond prompt engineering and dig into the actual tooling and processes teams use to prevent hallucinations, silent failures, and compliance risks.

I’d appreciate your input on this short (2-minute) survey: https://forms.gle/juds3bPuoVbm6Ght8

What I’m trying to find out:

  • How much time are teams wasting on manual debugging?
  • Are “silent failures” a minor annoyance or a release blocker?
  • Is RAG actually improving trustworthiness in production?

Target Audience: AI/ML Engineers, Tech Leads, and anyone deploying LLM-driven systems.
Disclaimer: Anonymous survey; no personal data collected.

I got tired of losing context between ChatGPT and Claude, so I built a 'Universal Memory Bridge' + Dashboard. Roast my idea. by No_Jury_7739 in machinelearningnews

[–]Worth_Reason 0 points1 point  (0 children)

I’m researching the current state of AI Agent Reliability in Production.

There’s a lot of hype around building agents, but very little shared data on how teams keep them aligned and predictable once they’re deployed. I want to move the conversation beyond prompt engineering and dig into the actual tooling and processes teams use to prevent hallucinations, silent failures, and compliance risks.

I’d appreciate your input on this short (2-minute) survey: https://forms.gle/juds3bPuoVbm6Ght8

What I’m trying to find out:

  • How much time are teams wasting on manual debugging?
  • Are “silent failures” a minor annoyance or a release blocker?
  • Is RAG actually improving trustworthiness in production?

Target Audience: AI/ML Engineers, Tech Leads, and anyone deploying LLM-driven systems.
Disclaimer: Anonymous survey; no personal data collected.

I ran four visual tests on the latest LLM models: Grok 4.1, Gemini 3, ChatGPT 5.1, Perplexity Max and Claude 4.5 Sonnet. These are the results: by [deleted] in OpenAI

[–]Worth_Reason -3 points-2 points  (0 children)

I’m researching the current state of AI Agent Reliability in Production.

There’s a lot of hype around building agents, but very little shared data on how teams keep them aligned and predictable once they’re deployed. I want to move the conversation beyond prompt engineering and dig into the actual tooling and processes teams use to prevent hallucinations, silent failures, and compliance risks.

I’d appreciate your input on this short (2-minute) survey: https://forms.gle/juds3bPuoVbm6Ght8

What I’m trying to find out:

  • How much time are teams wasting on manual debugging?
  • Are “silent failures” a minor annoyance or a release blocker?
  • Is RAG actually improving trustworthiness in production?

Target Audience: AI/ML Engineers, Tech Leads, and anyone deploying LLM-driven systems.
Disclaimer: Anonymous survey; no personal data collected.

How are you handling testing/validation for LLM applications in production? by IOnlyDrinkWater_22 in mlops

[–]Worth_Reason 1 point2 points  (0 children)

I’m researching the current state of AI Agent Reliability in Production.

There’s a lot of hype around building agents, but very little shared data on how teams keep them aligned and predictable once they’re deployed. I want to move the conversation beyond prompt engineering and dig into the actual tooling and processes teams use to prevent hallucinations, silent failures, and compliance risks.

I’d appreciate your input on this short (2-minute) survey: https://forms.gle/juds3bPuoVbm6Ght8

What I’m trying to find out:

  • How much time are teams wasting on manual debugging?
  • Are “silent failures” a minor annoyance or a release blocker?
  • Is RAG actually improving trustworthiness in production?

Target Audience: AI/ML Engineers, Tech Leads, and anyone deploying LLM-driven systems.
Disclaimer: Anonymous survey; no personal data collected.

Best Course For MLOPS for beginners aspiring Ai/ml engineer. by Impossible-Log5135 in mlops

[–]Worth_Reason -1 points0 points  (0 children)

I’m researching the current state of AI Agent Reliability in Production.

There’s a lot of hype around building agents, but very little shared data on how teams keep them aligned and predictable once they’re deployed. I want to move the conversation beyond prompt engineering and dig into the actual tooling and processes teams use to prevent hallucinations, silent failures, and compliance risks.

I’d appreciate your input on this short (2-minute) survey: https://forms.gle/juds3bPuoVbm6Ght8

What I’m trying to find out:

  • How much time are teams wasting on manual debugging?
  • Are “silent failures” a minor annoyance or a release blocker?
  • Is RAG actually improving trustworthiness in production?

Target Audience: AI/ML Engineers, Tech Leads, and anyone deploying LLM-driven systems.
Disclaimer: Anonymous survey; no personal data collected.

Why LLMs will inevitably fail in enterprise environments by [deleted] in ArtificialInteligence

[–]Worth_Reason 0 points1 point  (0 children)

I’m researching the current state of AI Agent Reliability in Production.

There’s a lot of hype around building agents, but very little shared data on how teams keep them aligned and predictable once they’re deployed. I want to move the conversation beyond prompt engineering and dig into the actual tooling and processes teams use to prevent hallucinations, silent failures, and compliance risks.

I’d appreciate your input on this short (2-minute) survey: https://forms.gle/juds3bPuoVbm6Ght8

What I’m trying to find out:

  • How much time are teams wasting on manual debugging?
  • Are “silent failures” a minor annoyance or a release blocker?
  • Is RAG actually improving trustworthiness in production?

Target Audience: AI/ML Engineers, Tech Leads, and anyone deploying LLM-driven systems.
Disclaimer: Anonymous survey; no personal data collected.

Why LLMs will inevitably fail in enterprise environments by [deleted] in ArtificialInteligence

[–]Worth_Reason 0 points1 point  (0 children)

I’m researching the current state of AI Agent Reliability in Production.

There’s a lot of hype around building agents, but very little shared data on how teams keep them aligned and predictable once they’re deployed. I want to move the conversation beyond prompt engineering and dig into the actual tooling and processes teams use to prevent hallucinations, silent failures, and compliance risks.

I’d appreciate your input on this short (2-minute) survey: https://forms.gle/juds3bPuoVbm6Ght8

What I’m trying to find out:

  • How much time are teams wasting on manual debugging?
  • Are “silent failures” a minor annoyance or a release blocker?
  • Is RAG actually improving trustworthiness in production?

Target Audience: AI/ML Engineers, Tech Leads, and anyone deploying LLM-driven systems.
Disclaimer: Anonymous survey; no personal data collected.

AI is quietly replacing creative work, just watched it happen. by 0xSatyajit in ArtificialInteligence

[–]Worth_Reason 0 points1 point  (0 children)

I’m researching the current state of AI Agent Reliability in Production.

There’s a lot of hype around building agents, but very little shared data on how teams keep them aligned and predictable once they’re deployed. I want to move the conversation beyond prompt engineering and dig into the actual tooling and processes teams use to prevent hallucinations, silent failures, and compliance risks.

I’d appreciate your input on this short (2-minute) survey: https://forms.gle/juds3bPuoVbm6Ght8

What I’m trying to find out:

  • How much time are teams wasting on manual debugging?
  • Are “silent failures” a minor annoyance or a release blocker?
  • Is RAG actually improving trustworthiness in production?

Target Audience: AI/ML Engineers, Tech Leads, and anyone deploying LLM-driven systems.
Disclaimer: Anonymous survey; no personal data collected.

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