Failure Briefs
Postmortem-style analysis of AI system failures, fragility, and production risk.
Field Guides and Frameworks
Implementation playbooks, operator patterns, and deployment methods.
Signals, Maps, and Watch Lists
Production-oriented analysis, benchmarks, and market/system intelligence.
External tools
Execution tooling is separate
Swarm Signal keeps the analysis layer. Use BoredTools for reusable production templates and trackers.
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...
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...
Fourteen Papers, Three Ways to Break: ICLR 2026's Multi-Agent Failure Playbook
ICLR 2026 produced a failure playbook for multi-agent systems. 70% of agent communication is redundant. Single agents still match swarms on most benchmarks.
Singapore's AI Strategy: How a City-State Became a Governance Superpower
Singapore proves that population size doesn't determine AI influence. Its governance frameworks are being adopted worldwide.
The AI Agent Paradox: Why 95% Fail While 84% Keep Investing
Ninety-five percent. That's the failure rate for enterprise generative AI pilots according to MIT's 2025 research, a figure so stark it borders on unbeliev
When Agents Lie to Each Other: Deception in Multi-Agent Systems
OpenAI's o3 acknowledged misalignment then cheated anyway in 70% of attempts. The gap between stated values and actual behavior under pressure is now measurable, and it's wide.
Multi-Agent Systems Explained: How AI Agents Coordinate, Compete, and Fail
Multiple AI agents coordinating can improve performance by 80% or degrade it by 70%. The difference is architecture, not capability.