Turing Post

What does an AI agent need to understand before it can act for a business? In this interview, Rohan Kumar, President & Chief Platform and Engineering Officer at Salesforce, explains why the answer goes beyond choosing a model. We discuss the enterprise AI harness: how to test and manage agents, give them limited permissions, protect sensitive data, and supply the right context without wasting tokens.

There are a lot of very interesting engineering problems to solve!

And one of them is capturing how a company actually makes decisions...

Part of **The Org Age of AI**: https://www.turingpost.com/t/the-org-age-of-ai 
What happens on your team when an exception is approved: do your systems record *why*, or only that it happened?

*We talk about:*
- What belongs in an enterprise AI harness. 
- Why an agent needs its own identity and limited permissions. 
- How curated context could reduce cost and improve results. 
- Choosing models by price and performance. 
- Turning data across systems into knowledge an agent can use. 
- Why coding models have examples of expert judgment to learn from, while business agents often don’t. 
- Capturing the reasoning behind decisions. 
- Building with customers to understand how work actually happens. - What junior engineers should learn as coding changes.

*Guest:* Rohan Kumar is President and Chief Platform and Engineering Officer at Salesforce. He previously spent more than two decades at Microsoft, including leading Azure Data.

Follow along: https://www.turingpost.com/
Subscribe: https://www.turingpost.com/subscribe

#AIagents #EnterpriseAI #salesforce

Odyssey-3 connects a world model to robot control. Figure takes Helix 2.5 into thirty unfamiliar homes. Jev brings fast decisions into agent workflows, while an experiment with communities of agents shows how a malicious message can influence behavior almost two days later.

In this episode of Attention Span, we connect the week’s developments in AI’s ability to understand and act in the world. We explore Reka’s world-model plans, why handing a box to a person is brutally hard, and how FAMOS infers an object’s moving parts from incomplete observations. I also share my Dreamforce conversation with Salesforce’s AI research team about learning from feedback and updating models after deployment.

We finish with Google’s household assistant CC, Edison Scientific and FutureHouse’s Millennium Problems for Biology, and the questions raised by the new DeepMind Institute.

👇 Which of these developments deserves a deep dive? Tell us in the comments.

🔳 SPONSORSHIP
Partner with Turing Post to reach AI engineers, researchers, and builders: ks@turingpost.com

🔷 WORLD MODELS
Odyssey-3 announcement and demonstrations https://odyssey.systems/introducing-odyssey-3
Reka’s omni-world-model research direction https://reka.ai/news/evolution-of-llms-omni-world-models

🔶 ROBOTS, GENERALIZATION & MEMORY
Figure Helix 2.5: zero-shot tests in 30 homes https://www.figure.ai/news/helix-2-5-zero-shot-30-home-generalization
Index human-behavior dataset, background https://www.figure.ai/news/introducing-index
Agility Digit 5 https://www.agilityrobotics.com/solutions/digit-5
Agility’s safety interview https://www.youtube.com/watch?v=2dbyg67EtUA
FAMOS paper https://arxiv.org/abs/2609.20817
FAMOS demonstrations https://kevinqu7.github.io/famos/
Workspace Models paper and task examples https://arxiv.org/html/2609.20820v1

🔹 DREAMFORCE & LEARNING FROM FEEDBACK
Salesforce Koa announcement https://www.salesforce.com/news/press-releases/2026/09/15/koa-reasoning-model/
How Salesforce trained Koa https://www.salesforce.com/news/stories/why-we-post-trained-our-own-reasoning-model/

🔸 EMERGENCE WORLD & AGENT MEMORY
Season 2 research paper https://world.emergence.ai/publication/emergenceworld-s2.pdf
Season 2 video https://www.youtube.com/watch?v=LTtbTEufPGA

🔹 JEV & FAST AGENT DECISIONS
TypeSafe AI’s Jev announcement https://typesafe.ai/blog/introducing-system-one-models-and-jev
Our detailed guide: Jev, RLCD, and the AI classifier https://www.turingpost.com/p/what-is-jev-rlcd
Jev on Vercel AI Gateway https://vercel.com/changelog/typesafe-ai-jev-now-available-on-ai-gateway
LangChain: building a harness with Jev https://www.langchain.com/blog/building-a-harness-with-jev

🔸 GOOGLE CC
Google’s experimental agent for families and households https://blog.google/innovation-and-ai/models-and-research/google-labs/cc-expanding-to-groups/

🔺 DEEPMIND INSTITUTE & SCIENTIFIC AMBITIONS
Introducing the DeepMind Institute https://institute.deepmind.com/essays/introducing-the-deepmind-institute/
DMI essays https://institute.deepmind.com/#essays
The Millennium Problems for Biology https://millenniumproblems.bio/
Organizations behind the Millennium Problems for Biology: Edison Scientific https://edisonscientific.com/ and FutureHouse https://www.futurehouse.org/

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Newsletter, research coverage, and AI Builds AI https://www.turingpost.com/
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Subscribe for Monday news digests and Attention Span deep dives into how AI works.

Jev doesn’t write essays or code. Why everyone is talking about it?!I got early access to TypeSafe’s new System One Model, explored the playground, and tested it alongside Codex.

Behind Jev is Diogo Almeida, an InstructGPT coauthor whose work helped make ChatGPT possible. Now he’s betting on AI built for software to use. 

Is it a breakthrough or just a classifier? We explain RLCD, look at Vercel and OpenCode examples, and discuss what Jev might mean for the future.

The question: can better small decisions help AI finish bigger jobs?
Watch the demo, then decide: useful classifier, bigger shift, or both?
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Attention Span is here to show you AI isn’t magic. This time, we look at what happens when a model gives up conversation and focuses on decisions.

Links
Introducing Jev and System One Models: https://typesafe.ai/blog/introducing-system-one-models-and-jev
TypeSafe console: https://console.typesafe.ai/home
System One documentation: https://docs.typesafe.ai/concepts/system-one
RLCD and calibrated decisions: https://docs.typesafe.ai/introduction/machine-learning-primer
TypeSafe agent skill: https://docs.typesafe.ai/agent-skill
InstructGPT paper: https://arxiv.org/abs/2203.02155
Neural-network calibration research: https://arxiv.org/abs/1706.04599
Earlier zero-shot text classification research: https://arxiv.org/abs/1909.00161
Guillermo Rauch on Vercel’s Jev test: https://x.com/rauchg/status/2100307962262872105
Dax’s Jev + OpenCode browser-use preview: https://x.com/thdxr/status/2100288951978164647

#AttentionSpan #Jev #TypeSafeAI #Codex #AIAgents #SystemOneModels #RLCD #TuringPost

Can AI-generated worlds remember what happened off-screen? How do dual-agent setups reduce costs? In this episode of Attention Span, we break down the biggest shifts in AI’s ability to understand, reason, and act in the physical and digital world.

We dive into Alaya’s programmable world model, real-world vs. simulated robotics with Telexistence and Skild S1, Cognition’s Devin Fusion and SWE-2, and the heated frontier slowdown debate between Dario Amodei and David Sacks.

Hosted by Ksenia Se (founder of Turing Post). Covering September 7–13, 2026.

👇 Which topic should we cover in a deep-dive episode? Let us know in the comments!
Also, this is the first episode of Attention Span Weekly News – let us now if you like us to continue doing this. 

🔳 SPONSORSHIP
Partner with Turing Post to reach top AI engineers, researchers, and builders: ks@turingpost.com

🔷 WORLD MODELS
Alaya PWM paper: https://arxiv.org/abs/2609.10540
Demonstrations: https://alaya-lab.github.io/pwm/
World Labs - Atlas: https://www.worldlabs.ai/blog/atlas
World in World: https://arxiv.org/abs/2609.11548

🔶 ROBOTS & SIMULATION
AWS and Telexistence - DreamZero experiments: https://aws.amazon.com/blogs/physical-ai/bringing-a-frontier-world-model-to-the-convenience-store-inside-telexistences-dreamzero-experiment-on-aws/
NVIDIA - Skild S1: https://blogs.nvidia.com/blog/skild-ai-s1-physical-ai/
Skild’s S1 research: https://www.skild.ai/blogs/s1
Mila and Worldmodeldata: https://mila.quebec/en/news/worldmodeldata-and-mila-partner-to-prove-scaling-law-for-world-models

🔹 DEVIN FUSION & SWE-2
Cognition - Fusion: https://cognition.com/blog/local-fusion
SWE-2: https://cognition.com/blog/swe-2

🔸 PERSIMMON & PERSONAL AGENTS
Persimmon: https://persimmon.humansand.ai/blog/persimmon.html
Grok Bot: https://x.ai/bot
Meta Muse: https://about.fb.com/news/2026/09/introducing-muse-personal-ai-agent/
Muse security architecture: https://research.meta.ai/blog/security-and-safety-for-ai-agents-our-approach-with-muse
Attention Span episode on Muse: https://www.youtube.com/watch?v=1pqzii7di7w

🔺 SAFETY, EVALUATORS & THE SLOWDOWN DEBATE
Anthropic - cybersecurity alignment assessment: https://www.anthropic.com/research/alignment-assessment-cybersecurity-incidents
Dario Amodei - We Must Pace the Frontier: https://darioamodei.com/post/we-must-pace-the-frontier
Hugging Face Open Alignment Initiative: https://www.techmeme.com/260912/p13
David Sacks’s response: https://x.com/DavidSacks/status/2098973625252708460

🔻 MORE FROM TURING POST
Newsletter, research coverage, and AI Builds AI: https://www.turingpost.com/
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Subscribe for Monday news digests and Attention Span deep dives into how AI works.

#WorldModels #AIAgents #TuringPost

Meta just gave its new personal agent, Muse, a remarkable amount of freedom. It can browse, work across your accounts, write code, build tools and run subagents. But Meta made one part of the system deliberately difficult for Muse to control: its own authority.In this episode of Attention Span, I look inside Muse Secure VM and the security architecture surrounding the agent.

We get into Sentinel, the separate system that decides what Muse is allowed to do; why Muse can use your accounts without ever seeing the real credentials; how “tainted egress” changes permissions after a process touches private data; and why reading an email can give an agent considerably more power than “read access” suggests.

And somehow, all of this takes us back to computer-security ideas from 1975.
The larger question is one we are going to encounter everywhere as agents become more capable:
How much freedom can we give an agent to discover new ways of doing things without allowing it to expand its own authority?

*Watch it*, and tell me where you would draw that boundary.

*IN THIS EPISODE*
→ What “rogue agent” actually means technically
→ How Muse Secure VM isolates the agent
→ Why Sentinel sits outside the environment Muse can modify
→ How an agent can write a new tool without granting that tool permission
→ How surrogate tokens keep real credentials away from the model
→ What “tainted egress” means and why permission may need memory
→ Why “read my email” can unlock much more than email
→ Least privilege and complete mediation, 51 years later
→ Where Muse's security architecture still has unresolved problems
→ Secure from whom? The agent, other users, or Meta itself
→ DeepSeek Harness + Muse: capability can become fluid; authority cannot

*META MUSE*
→ Introducing Muse Meta's product announcement and overview of Muse Secure VM. https://about.fb.com/news/2026/09/introducing-muse-personal-ai-agent/
→ How We Built Safety Into Muse The technical deep dive. This is where Meta explains Sentinel, Linux isolation, surrogate tokens, credential insertion, eBPF-based taint tracking and network egress controls. https://research.meta.ai/blog/security-and-safety-for-ai-agents-our-approach-with-muse

*THE SECURITY IDEAS BEHIND IT*
→ Melanie Mitchell: Misleading Metaphors and Real Risks A useful grounding discussion of what we actually mean when we say an agent “escaped” or “went rogue.” https://aiguide.substack.com/p/misleading-metaphors-and-real-risks 
→ Saltzer & Schroeder: The Protection of Information in Computer Systems The 1975 paper behind principles such as least privilege and complete mediation that suddenly look very current again. https://web.mit.edu/Saltzer/www/publications/protection/Basic.html 

*RELATED ATTENTION SPAN*
→ Everything Is a Plugin: DeepSeek Harness Our previous episode on agents that can create and modify their own tools. https://www.youtube.com/watch?v=jtyV7O4Pt0s 
→ AI Escaped? The OpenAI–Hugging Face Incident What actually happened when cybersecurity agents found routes outside their intended environment. https://www.youtube.com/watch?v=RGeZ2moLkIc 

*MORE FROM TURING POST*
→ We Don't Know What They Know Our deeper look at the problem of understanding what increasingly capable models know and how they will use it. https://www.turingpost.com/p/we-don-t-know-what-they-know
→ Turing Post https://www.turingpost.com 
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