Catalog
concept#Architecture#Software Engineering#Integration#Reliability

Multi-Agent Systems

An architectural paradigm of distributed autonomous agents that cooperate or compete to solve tasks. Focuses on coordination, communication and emergent behavior across software and robotic agents.

Multi-agent systems describe distributed collections of autonomous, interacting agents that cooperate or compete to solve complex tasks.
Established
High

Classification

  • High
  • Technical
  • Architectural
  • Intermediate

Technical context

Message brokers (MQTT, RabbitMQ) for asynchronous communicationContainer orchestration (Kubernetes) for scaling agent instancesROS (Robot Operating System) for robotic agents

Principles & goals

Decentralization promotes robustness and scalability.Clear communication protocols and ontologies are necessary.Agents should balance autonomous decisions with global objectives.
Build
Domain, Team

Use cases & scenarios

Compromises

  • Unpredictable interactions lead to side effects in the system.
  • Poor protocols may cause deadlocks or resource contention.
  • Security flaws in agent communication allow manipulation.
  • Clear interfaces and robust error handling for agent communication.
  • Use simulations early to evaluate emergent effects.
  • Integrate monitoring and distributed tracing tools for interaction analysis.

I/O & resources

  • Agent definitions and behavior rules
  • Communication protocols and ontologies
  • Environment information and sensor data
  • Coordinated actions and decisions
  • Logs and traces of interacting agents
  • Performance statistics and simulation results

Description

Multi-agent systems describe distributed collections of autonomous, interacting agents that cooperate or compete to solve complex tasks. They provide architectural principles for coordination, negotiation, and emergent behavior across software or robotic agents. MAS apply in simulation, automation, distributed control and socio-technical modeling.

✔

  • Scalable, modular systems via distributed agent architecture.
  • Improved fault tolerance through local decision-making.
  • Flexibility in heterogeneous and dynamic environments.

✖

  • Coordination can be costly and complex in large networks.
  • Predictability of emergent behavior is limited.
  • Effort for consistency and security grows with agent count.

  • Throughput per agent

    Tasks processed per time unit and per agent; indicator of efficiency.

  • Coordination latency

    Time between coordination request and confirmed action; affects responsiveness.

  • Error rate due to interactions

    Share of failed interactions or deadlocks; measure of stability.

JADE (Java Agent Development Framework)

A framework for implementing distributed agents and agent communication in Java.

Multi-agent Simulation for Traffic

Traffic simulations use agent-based models to analyze congestion and routing.

Cooperative Robotic Inspection

Swarms of robots perform collaborative inspections of critical infrastructure.

1

Define goals and agent roles; establish domain goals and KPIs.

2

Select or standardize communication protocols and ontology.

3

Build a prototype with a few agents and a simulation environment.

4

Run scaling tests, add observability and gradually move to production.

⚠️ Technical debt & bottlenecks

  • Incomplete documentation of agent APIs and protocols.
  • Ad-hoc message formats that prevent later interoperability.
  • Lack of observability integration hinders troubleshooting.
Communication bandwidthGlobal consistencyObservability of distributed interactions
  • Using MAS for simple deterministic workflows that require central control.
  • Not implementing monitoring and thus missing interaction failures.
  • Equipping agents with full world knowledge and thereby sacrificing scalability.
  • Defining shared communication semantics too late.
  • Ignoring security aspects of decentralized communication.
  • Skipping simulation phase and going directly to large-scale tests.
Distributed systems and network protocolsAgent modeling and multi-agent designTesting, observability and simulation of interactions
Fault tolerance through decentralizationLatency requirements and local decision needsScalability in dynamic environments
  • • Limited network bandwidth and latency
  • • Resource constraints of individual agents
  • • Regulatory requirements for security and privacy