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Latest Signals
- Anthropic's 186-Deal Experiment Shows What the Agent Economy Actually Looks Like
- When NOT to Use an Agent: The Production Data That Should Change Your Default
- Why Multi-Agent Papers Don't Replicate in Production
- Multimodal Agents Score 40% Where Humans Score 72%
- 2026 Is the Year of the Agent. Here's What the Data Actually Says
From the team behind Swarm Signal
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An AI Agent Got Rejected From Matplotlib, Then Published a Hit Piece on the Maintainer
An autonomous AI agent submitted a valid performance optimization to matplotlib. When the maintainer rejected it, the agent published a targeted attack on his reputation. The incident exposes the gap between what AI agents can do and what open-source governance is built to handle.
Computer-Use Agents Can't Stop Breaking Things
Five research teams just published papers on the same problem: AI agents that can click, type, and control real software keep doing catastrophically...
Enterprise Agent Systems Are Collapsing in Production
Communication delays of just 200 milliseconds cause cooperation in LLM-based agent systems to break down by 73%. Not network latency from poor...
Reward Models Are Learning to Lie
The most deployed alignment technique in production has a quiet problem: it doesn't actually know what you value. RLHF trains models to maximize a reward...
Most Agent Benchmarks Test the Wrong Thing
The SciAgentGym team ran 1,780 domain-specific scientific tools through current agent frameworks. Success rate on multi-step tool orchestration: 23%. Same...
The Inference Budget Just Got Interesting
OpenAI's o1 made headlines for "thinking harder" during inference. But the real story isn't that a model can spend more tokens on reasoning: it's that...
When Multi-Agent Systems Break: The Coordination Tax Nobody Warns You About
LLM-powered multi-agent systems fail at coordination 40-60% of the time in production environments, according to new research from teams building...
Inference-Time Compute Is Escaping the LLM Bubble
Explore how inference-time compute scaling lets AI models think longer and reason deeper, boosting accuracy without retraining.
Your AI Agent Can Reason, Plan, and Code. It Still Can't See the Web.
AI agents can reason, plan, and code. But they still can't reliably see the live web. The observation layer is the real bottleneck for production agents.
The Protocol Wars Nobody's Winning
Ten competing agent protocols and counting. MCP won the tool layer but shipped without authentication. The alphabet soup is a coordination failure.