Exactly. ‘Dull in chats’ is a feature when the goal is don’t invent facts. Love the auditor mindset here.
@ai21labs
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Boring AI Agents: A Force for Business
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Boring AI Agents? They aren't players, they are workflow slayers: – Oliver: Humor in beta. Grounded in data.
– Nancy: Dull in chats. Doesn't invent facts.
– Karen: Cold and distant. Highly consistent. In enterprise AI, boring is a feature not a flaw. See how we build -
Agent Swarms: Why Parallel Agents Break in Practice
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“Parallel agents are easy when they’re read-only. It gets complicated the moment they change files, send emails, or touch the real world.”
— AI21 Labs (@AI21Labs) 20 janvier 2026
This week’s Yet Another AI Podcast unpacks the Agent Swarm Fallacy with Or Dagan (CPO, @AI21Labs): why “just add agents” breaks in practice,… pic.twitter.com/gxmgiTR1gq“Parallel agents are easy when they’re read-only. It gets complicated the moment they change files, send emails, or touch the real world.” This week’s Yet Another AI Podcast unpacks the Agent Swarm Fallacy with Or Dagan (CPO, @AI21Labs
): why “just add agents” breaks in practice, -
Enterprise AI Reliability: Moving Beyond Demos to Production
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“If your AI is wrong 2% of the time, you’re not in the game.”
— AI21 Labs (@AI21Labs) 19 janvier 2026
In his interview with @vladdoes, our Co-Founder & Co-CEO @origoshen breaks down a simple truth about enterprise AI: getting a demo to work isn’t the hard part, making it reliable in production is.
▶️ Watch the… pic.twitter.com/aH3I3Crc2D“If your AI is wrong 2% of the time, you’re not in the game.” In his interview with @vladdoes
, our Co-Founder & Co-CEO @origoshen breaks down a simple truth about enterprise AI: getting a demo to work isn’t the hard part, making it reliable in production is. Watch the -
Jamba2 Achieves Competitive Hallucination Rate on HHEM Leaderboard
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New benchmark signal: Jamba2 places high on @vectara
's HHEM leaderboard for hallucination rate competitive with open models in its size class. Check out the leaderboard: https://
huggingface.co/spaces/vectara
/leaderboard
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AI21Labs’ Boring AI Agents for Enterprise
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Meet our boring AI agents.
— AI21 Labs (@AI21Labs) 13 janvier 2026
They don’t invent facts.
They’re dull in chats. Cold. Distant. Consistent.
Built for enterprise workflows where accurate, auditable, repeatable output at scale isn’t a nice-to-have, it’s the requirement.
👉 Build boring agents:… pic.twitter.com/o9EAL7TpYMMeet our boring AI agents. They don't invent facts.
They're dull in chats. Cold. Distant. Consistent. Built for enterprise workflows where accurate, auditable, repeatable output at scale isn't a nice-to-have, it's the requirement. Build boring agents: -
AI21Labs celebrates partnership with AITinkerers in 2026
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Thank you for your partnership @AITinkerers
! Cheers to 2026! -
Optimizing AI Generation: Zero Failure Rate and Reduced Compute Costs
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5/5 What that unlocked: – Failure rate: 30% → 0
– Repo downloads: 8,000+ → 500
– Provisioning: per run → once per campaign
– And the “don’t burn compute” part: split Generation (patch) from Evaluation (tests). If evaluation fails, we retry tests, not re-generate tokens. -
Optimizing SWE-bench Performance Through Shared Resource Provisioning
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4/5 The fix was a mindset shift: stop provisioning per run. Provision per SWE-bench instance, then reuse it. Shared per instance: repo at the right commit, MCP server, dependencies. Unique per run: execution path, files changed, commands, patch. We keep runs isolated with
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SWE-bench Evaluation Challenges at Kubernetes Scale
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3/5 Trying to run SWE-bench eval as-is on k8s at large scale wasn't trivial: – Fresh pods have no cache. This means that everyone re-downloads the world (hello HF 429s.
– “docker run inside k8s” works on paper, then dies from contention, privileges, and overhead. It worked, but