Multi Agent Systems
Learn about multi agent systems and how to implement it effectively.
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Last updated: 12/9/2025
Multi-agent Systems: Patterns & Practical Workflows
Multi-agent Systems in InnoSynth-Forjinn enable advanced orchestration, collaboration, and reasoning among teams of autonomous agents. These architectures reflect how humans solve complex problems—by distributing tasks and combining specialized expertise.
Why Multi-agent?
- Divide-and-conquer complex tasks (planning, research, synthesis)
- Achieve consensus, critique, and self-correction (e.g., agent judge/vote)
- Build scalable parallel automations (thousands of concurrent tasks)
- Explore emergent intelligence: let agents teach, mentor, or debate
Types of Multi-agent Patterns
1. Supervisor/Worker
- SupervisorAgent delegates tasks to one or more WorkerAgents
- Supports status checks, retries, escalation/fallback (if a worker fails)
2. Sequential Chain (Pipeline)
- Agents are connected in a strict sequence, each responsible for one stage (ingest → process → QA → summarize)
- Used for complicated ETL-type automations, content moderation, or multi-turn dialog with role handoffs
3. Parallel/Fan-out
- Multiple agents receive the same (or partitioned) subtask, execute simultaneously
- Used for speed (map-reduce), redundancy, or exploration of different paths/solutions
4. Communication Protocols
- Agents can send and receive messages/events using platform bus or explicit Message Node wiring
- Agents can trigger tool use, pass data, or prompt one another recursively
5. Voting/Consensus
- Multiple independent agents generate results
- VotingAgent (or another judge) tallies/chooses best output
- Used for code review, model self-improvement, or fuzzy decision making
Building a Multi-agent Workflow
- Create multiple agent nodes (specializing or using different context/tools/capabilities)
- Supervise with control nodes: SupervisorAgent, condition, Loop, etc
- **Connect agents via explicit chains, event/message nodes, or crew/CrewAI config
- Test edge cases and parallel/sync execution to avoid deadlocks
Example: Automated Research & Summarization
- PlannerAgent: Analyzes query, breaks down into sub-questions
- WorkerAgents (x3): Each investigates a source, writes a summary
- ReviewerAgent: Critiques and merges results
- ManagerAgent: Formats and outputs final report
Troubleshooting
- Deadlocks: Ensure all branches have completion/timeout logic, avoid cycles without base case
- Resource spikes: Parallel agents may exhaust token, CPU, or rate limits; spread workload, monitor/log accordingly
- Debug paths with Trace nodes: Trace inter-agent messages and status for easier debugging
Best Practices
- Assign clear, non-overlapping responsibilities to agents
- Limit chain depth and parallel width for easier debuggability and scale control
- Use logs, status, and Trace nodes to visualize and monitor agent decisions/cooperation
- Document complex agent flows liberally in Sticky Notes or metadata
Multi-agent Systems unlock scalable automation and hybrid intelligence—map your process, test collaboration, and then supercharge your organization's workflows with AI teams.