Swarm Systems
Multi-agent coordination, swarm intelligence, and collective behavior. How groups of AI agents work together and when they don't.
Key Guides
Latest Signals
- Fourteen Papers, Three Ways to Break: ICLR 2026's Multi-Agent Failure Playbook
- The Coordination Tax: Why More Agents Don't Mean Better Results
- The First Model Trained to Swarm: What the Benchmarks Actually Show
- Agents That Reshape, Audit, and Trade With Each Other
- When Agents Meet Reality: The Friction Nobody Planned For
AI Agent Orchestration Patterns: From Single Agent to Production Swarms
37% of multi-agent failures trace to inter-agent coordination, not individual agent limitations. Six production orchestration patterns with specific framework implementations, known failure modes, and quantitative guidance.
Swarm Intelligence Explained: From Ant Colonies to AI Agent Fleets
In 1987, Craig Reynolds published three lines of code that made pixels fly like birds. Swarm intelligence borrows nature's playbook for solving problems that defeat traditional algorithms.
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.
Multi-Agent Systems: The 90% Performance Jump Nobody's Talking About
If 2025 was the year of AI agents, 2026 is shaping up as the year of multi-agent systems. Internal evaluations from early 2025 surfaced something striking:
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.
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.
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.