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Multi-Agent Systems

📖 NVIDIA Multi-Agent Blog - NVIDIA resources on multi-agent architectures

Multi-Agent Architecture Fundamentals

Why Multi-Agent?

  • Specialization - Each agent focuses on a specific domain or skill
  • Scalability - Add agents for new capabilities without modifying existing ones
  • Robustness - Failure of one agent does not necessarily halt the system
  • Complexity management - Break complex tasks into manageable agent responsibilities
  • Parallel processing - Independent agents can work simultaneously

Agent Roles

Common Role Patterns: - Orchestrator - Coordinates other agents, manages task flow - Researcher - Gathers and synthesizes information - Coder - Writes, reviews, and debugs code - Critic - Evaluates outputs and provides feedback - Planner - Decomposes tasks and creates execution plans - Executor - Carries out specific actions using tools - Validator - Checks results against requirements and constraints

Orchestration Patterns

Sequential Orchestration

  • Agents execute in a defined order
  • Output of one agent feeds as input to the next
  • Simple to reason about and debug
  • Slowest execution - no parallelism
  • Best for pipeline-style workflows

Parallel Orchestration

  • Independent agents run simultaneously
  • Results are aggregated at a synchronization point
  • Faster execution for independent sub-tasks
  • Requires careful result merging
  • Best when tasks can be cleanly decomposed

Hierarchical Orchestration

  • Top-level orchestrator delegates to sub-orchestrators
  • Each sub-orchestrator manages a team of specialized agents
  • Scales to complex organizational structures
  • Clear chain of responsibility
  • Best for large, multi-domain tasks

Dynamic Orchestration

  • Orchestrator decides which agents to invoke at runtime
  • Agent selection based on task analysis and available capabilities
  • Agents can spawn or invoke other agents as needed
  • Most flexible but hardest to predict and debug
  • Best for open-ended tasks with variable requirements

Communication Patterns

Message Passing

  • Agents communicate through structured messages
  • Messages include role, content, and metadata
  • Supports request-response and publish-subscribe patterns
  • Message queues for asynchronous communication
  • Serializable for persistence and replay

Shared Blackboard

  • Central data store that all agents can read and write
  • Agents monitor the blackboard for relevant updates
  • Reduces direct agent-to-agent coupling
  • Requires access control and conflict resolution
  • Good for collaborative problem-solving

Event-Driven

  • Agents emit events when they complete work or detect conditions
  • Other agents subscribe to relevant event types
  • Loose coupling between agents
  • Supports reactive and proactive agent behaviors
  • Scalable to many agents

Conflict Resolution

Voting

  • Multiple agents vote on the best answer or action
  • Majority vote or weighted voting based on agent expertise
  • Simple to implement, democratic decision-making

Debate

  • Agents present arguments for their position
  • Iterative refinement through structured discussion
  • Converge on consensus or escalate to a judge agent
  • Improves quality through adversarial review

Authority-Based

  • Designated authority agent makes final decisions
  • Other agents provide recommendations and evidence
  • Clear accountability and predictable outcomes
  • Risk of single point of failure

Escalation

  • Start with automated resolution
  • Escalate to senior agent or human when confidence is low
  • Tiered approach balances automation and oversight

Shared Memory and Knowledge

Shared Context

  • Common knowledge base accessible to all agents
  • Updated as agents discover new information
  • Versioned to track knowledge evolution
  • Access-controlled for sensitive information

Task State

  • Shared tracking of task progress and status
  • Each agent updates its portion of the task state
  • Orchestrator monitors overall progress
  • Enables checkpoint and resume for long tasks

Collaboration Protocols

  • Handoff - One agent explicitly transfers control to another
  • Delegation - Agent assigns sub-tasks to other agents
  • Consultation - Agent requests input from another without transferring control
  • Notification - Agent informs others of important events or results

📖 NVIDIA NIM Multi-Model - Deploying multiple models for multi-agent systems

Design Considerations

Agent Granularity

  • Too few agents - each agent too complex, hard to maintain
  • Too many agents - excessive communication overhead
  • Balance specialization depth with coordination costs
  • Start simple, add agents as complexity demands

Failure Handling

  • Individual agent failure should not crash the system
  • Retry failed agent tasks with same or alternative agent
  • Timeout enforcement for each agent interaction
  • Graceful degradation when agents are unavailable
  • Dead letter handling for unprocessable messages

Performance

  • Minimize inter-agent communication for latency-sensitive tasks
  • Cache frequently accessed shared state
  • Batch messages when possible
  • Monitor agent utilization and adjust scaling

Testing Multi-Agent Systems

  • Unit test each agent in isolation
  • Integration test agent pairs and communication
  • End-to-end test complete workflows
  • Chaos testing - randomly disable agents
  • Load testing - verify scaling under concurrent tasks

Key Exam Concepts

  • Know orchestration patterns: sequential, parallel, hierarchical, dynamic
  • Understand communication methods: message passing, blackboard, events
  • Agent role design and specialization principles
  • Conflict resolution strategies: voting, debate, authority
  • Shared memory and knowledge management
  • Failure handling in multi-agent systems