6/6 This is the core takeaway: With structured plans and decision-theoretic optimization techniques, you can get a lot more with the same models, tools and compute. And as you scale, the gap widens.
@ai21labs
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ReAct Agents and High-Variance Execution in Long-Horizon Tasks
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4/6 ReAct-style agents encode the entire plan in text, and only get evaluated at the very end. In long-horizon tasks, that turns execution into a high-variance gamble.
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Structured Execution: Selective Scaling for Efficient Agent Workflows
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5/6 With structured execution, execution is controlled and stays on track. You can scale only individual steps that will benefit from it, instead of doing it for the entire agentic trajectory.
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Maestro: Optimizing Compute Through Agentic Framework Structure
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2/6 SWE-bench is not the story. Maestro is not a dedicated SWE agent, it’s an agentic framework that optimizes based on structure and learned priors. ex: knowing that 4 runs of GPT-5 mini are cheaper and better than one GPT-5 run, leads to better use of the same compute –
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Test-Time Compute: Optimizing Where and When to Spend Resources
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3/6 Once you treat execution as a test-time compute problem, the question changes. It’s no longer “which model should I use?” It’s “where should I spend compute, and when should I stop?”. That’s why multiple cheaper runs can win:
you’re buying optional paths, not just more -
Orchestrated Test-Time Compute Scaling for Long-Horizon Agentic Tasks
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1/6 Long-horizon agentic tasks are breaking our mental models. More tokens, bigger models, and best-of-N only go so far. Orchestrated approach to test-time-compute scaling is what long-horizon tasks need. Here’s what we learned using SWE-bench as a test case. Read the blog for
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Building High-Quality Deep Research Agents While Reducing Token Waste
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🎙️ How do you make a high quality deep research agent without burning thousands of tokens?@tavilyai's Dean Sacoransky joins our host, @yuvalinthedeep on log-driven iteration, cutting wasted tokens, and improving quality section by section.
— AI21 Labs (@AI21Labs) 6 janvier 2026
Watch it here:… pic.twitter.com/sQDWlvV0xRHow do you make a high quality deep research agent without burning thousands of tokens? @tavilyai
's Dean Sacoransky joins our host, @yuvalinthedeep on log-driven iteration, cutting wasted tokens, and improving quality section by section. Watch it here: -
Scaling AI Workloads with Ray: From Data to Agents
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🎙️ In this episode of Yet Another AI Podcast, we sat down with @lindavivah from @anyscalecompute on scaling AI workloads with Ray from data processing to training, inference, and agents.
— AI21 Labs (@AI21Labs) 30 décembre 2025
We look at Ray’s roots in RL, why tools like vLLM build on it, and when Ray vs. SaaS makes… pic.twitter.com/9W8QPxztiOIn this episode of Yet Another AI Podcast, we sat down with @lindavivah from @anyscalecompute on scaling AI workloads with Ray from data processing to training, inference, and agents. We look at Ray’s roots in RL, why tools like vLLM build on it, and when Ray vs. SaaS makes
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Building Competitive Moats and Fast Shipping with Small Teams
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🎙️New episode of Yet Another AI Podcast @AiImagen shares how to build real moats when everyone uses the same models and how a two-person “Commando Squad” vibe-codes in production to ship fast without risking the core product.
— AI21 Labs (@AI21Labs) 17 décembre 2025
Tune in: https://t.co/cSOUz0H7FW pic.twitter.com/bm2YxIu6CENew episode of Yet Another AI Podcast @AiImagen shares how to build real moats when everyone uses the same models and how a two-person “Commando Squad” vibe-codes in production to ship fast without risking the core product. Tune in: https://
ai21.com/yaap/everyones
-got-the-same-model/?utm_source=org-twitter
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Vibe Agent: Create AI Agents in Plain English with Maestro
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Vibe Agent in AI21 Maestro helps you create AI agents from a single plain-English description. It suggests purpose, validation checks, tools, and model/compute settings while explaining each step in real time.
— AI21 Labs (@AI21Labs) 16 décembre 2025
🎥Watch the video and start building with AI21 Maestro:… pic.twitter.com/UOSnkquX6XVibe Agent in AI21 Maestro helps you create AI agents from a single plain-English description. It suggests purpose, validation checks, tools, and model/compute settings while explaining each step in real time. Watch the video and start building with AI21 Maestro: