AGENTS

→ View original post on X — @dair_ai

New research challenges the assumption that multi-agent systems improve LLM reasoning

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@dair_ai

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13 May 2026 15h00

Do multi-agent systems make LLM reasoning better? Most AI devs assume that it should. But this new paper shows that this is often not the case. It ran 22,500 deterministic trajectories across GAIA, SWE-bench, and Multi-Challenge with three frontier models. Agents frequently

→ View original post on X — @ingliguori

Enterprise AI Governance and the Challenge of Agentic Workflows

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@ingliguori

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13 May 2026 14h17

Most companies do not have an AI problem. They have an AI governance problem. As agents move across systems, data, and workflows, the real challenge becomes: Visibility Control Accountability Microsoft’s Agent 365 updates point to the next enterprise bottleneck:

→ View original post on X — @alphasignalai

The Future of AI Agents: The War Room

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@alphasignalai

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13 May 2026 14h00

The future of AI agents isn't text. It's a war room.

Hermes already lets you run a fleet of specialized agents that hand work to each other.

The problem was visibility.

You had to SSH into your machine and grep logs to see what each one was doing.

Orchestration War Room is… pic.twitter.com/lYFiHWyrVW
— AlphaSignal AI (@AlphaSignalAI) 13 mai 2026

The future of AI agents isn't text. It's a war room. Hermes already allows you to run a fleet of specialized agents that hand off work to each other. The issue was visibility. You had to SSH into your machine and grep logs to see what each one was doing. Orchestration War Room is…

Exécuter plusieurs agents en parallèle pour optimiser les résultats

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13 May 2026 12h58

5/5 Ensemble: Run multiple distinct agent configs in parallel → select the best trajectory. Leverages success of the portfolio vs single variants (see also: @/karpathy’s LLM Council). Can outperform best-of-N on accuracy but at lower cost & latency. Here’s how each method did

Best-of-N : optimisation parallèle d’agents IA

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13 May 2026 12h58

4/5 Best-of-N: Run one agent config N times in parallel → select the best trajectory. Leverages LLMs’ non-determinism – but hinges on a good eval mechanism (we use an LLM-as-a-Judge). Can increase accuracy without linearly increasing latency – but parallel runs can add up $$.

Agent Scaffolding: Building the Infrastructure

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13 May 2026 12h58

3/5 Agent scaffolding: Adjust prompts, integrate tools/skills, set execution policies, retries, memory, etc. → create the operational framework. Useful for long-horizon or multi-step tasks.

4 agent optimization methods tested on BrowseComp-Plus

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13 May 2026 12h58

1/5 We’re seeing 4 common agent optimization methods for hitting the right accuracy-cost or accuracy-latency tradeoff. We tried them all out on BrowseComp-Plus (results at end of the thread).

→ View original post on X — @ronald_vanloon

AI voice use cases for podcasts, training, and storytelling

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@ronald_vanloon

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13 May 2026 10h35

Think about the use cases: → AI podcasts with distinct hosts
→ Training simulations with realistic dialogue
→ Storytelling with emotional range
→ Assistants that sound more natural
→ Multi-speaker conversations where every voice has a personality This is not just