agents
Deep Dives and Frameworks
Implementation playbooks, operator patterns, and durable analysis.
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.
The Coordination Tax: Why More Agents Don't Mean Better Results
Once a single agent solves a task correctly 45% of the time, adding more agents makes the system worse. Independent multi-agent systems amplify errors 17.2 times.
The First Model Trained to Swarm: What the Benchmarks Actually Show
Every multi-agent system before K2.5 was a framework bolted on top of a model that never learned to coordinate. PARL changes the equation, but the benchmarks tell a nuanced story.
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.
Context Is The New Prompt
Prompt engineering hit its ceiling. The teams pulling ahead now are engineering context: retrieval, memory, tool access, not tweaking instructions. Context is the new prompt.
2026 Is the Year of the Agent. Here's What the Data Actually Says
Every major cloud vendor and analyst firm agrees: 2026 is the year AI agents go from pilot to production. The data backs them up, but it also reveals the gap between adoption and outcomes is wider than anyone's admitting.
Agents That Reshape, Audit, and Trade With Each Other
As agents gain autonomy over communication, inspection, and resource negotiation, three converging patterns are redefining multi-agent infrastructure: dynamic topology, embedded auditing, and adversarial trade.
The Budget Problem: Why AI Agents Are Learning to Be Cheap
The next generation of agents will not be defined by peak capability but by their ability to match effort to difficulty. Across every subsystem, the field is converging on the same fix: budget-aware routing.
When Agents Meet Reality: The Friction Nobody Planned For
Lab benchmarks show multi-agent systems coordinating well. Deploy them in messy reality and three kinds of friction emerge that no architecture diagram accounted for.
The Red Team That Never Sleeps: When Small Models Attack Large Ones
Automated adversarial tools are emerging where small, cheap models systematically find vulnerabilities in frontier models. The safety landscape is shifting from pre-deployment testing to continuous monitoring.
Agents That Rewrite Themselves: The Self-Modifying Stack Is Here
Three independent papers demonstrate agents rewriting their own training code, generating their own knowledge structures, and refining their reasoning at test time. Self-improvement has moved from theory to working engineering.