Orchestration · Software component

Swarm Agent

Software componentOrchestrationOrchestration & Toolsarc:SwarmAgent

A simple agent that applies local behavioural rules to neighbour state and local signals, producing emergent group behaviour without global knowledge.

Responsibility. Acts on local rules using only neighbour observations.

Also known as: Swarm member, Particle

Direct neighbourhood (hover for relationship types)

Relationships

is overridden by control

Swarm Anomaly Detector Ch1.3

is monitored by assurance

Swarm Anomaly Detector Ch1.3

Design guidance

SHOULD be chosen when problems need distributed coordination, continuous adaptation, or search over very large solution spaces.
SHOULD NOT be used where precise timing, exact global optimisation or predictable behaviour is safety-critical.
MUST have rule parameters (e.g., inertia, cohesion weights, evaporation rates) tuned and tested at realistic scale.
SHOULD bound neighbour communication to avoid superlinear bandwidth growth.

Quantitative guidance

As stated by the sources; verify before use.

Drone swarms redistributed coverage within 7 seconds after individual failures (Ch1.3).
Experimental swarms achieved 96% spatial coverage, cut collisions from 30 to zero in 10-second windows, and reached 80%+ goal convergence (Ch1.3).

Classification

Patterns: Swarm intelligenceSeparation-alignment-cohesion rulesPositive/negative feedback loopsParticle Swarm OptimizationAnt Colony Optimization
Quality attributes: Performance efficiency (ISO/IEC 25010)Reliability (ISO/IEC 25010 | NIST AI RMF: valid and reliable)Functional suitability: correctness and validity (ISO/IEC 25010 | NIST AI RMF: valid)
Risks mitigated: Single point of failureCentral bottleneck

Sources

Ch1.3: T. Nguyen, "Multi-Agent Systems," in Mastering Agentic AI Systems: Guide for the NVIDIA NCP-AAI Exam, 1st ed. 2026, ch. 1.3. ISBN: 9798244538229.