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type, concept_type, tags, created, modified, related, sources, provenance, summary
type concept_type tags created modified related sources provenance summary
types/concept.md workflow
multi-agent
collaboration
sync
coordination
2026-07-26 2026-08-29
exemplifies
LLM Wiki Pattern
see-also
Memory Lifecycle
composition
Mesh Sync
composition
Shared vs Private
composition
Work Coordination
Source - LLM Wiki v2
sourced Wissensmanagement mit mehreren Agenten; erweitert das LLM-Wiki-Muster um Mesh Sync, die Trennung von geteiltem und privatem Wissen und leichtgewichtige Arbeitskoordination.

Multi-Agent Collaboration

Typ: Workflow (Multi-Agent Knowledge Management)

Definition

Multi-Agent Collaboration behandelt die Realität, dass viele praktische Anwendungsfälle mehrere Agenten oder mehrere Menschen beinhalten, die zur gleichen Knowledge Base beitragen. Das ursprüngliche LLM-Wiki-Pattern ist Single-User, Single-Agent; v2 erweitert es auf Kollaborationsszenarien.

Kernpunkte

Das Problem

Single-Agent-Annahmen scheitern, wenn:

  • Mehrere Agenten parallel arbeiten (verschiedene Coding-Sessions, Recherchethreads)
  • Mehrere Menschen zur gleichen Knowledge Base beitragen
  • Wissen über Sessions oder Benutzer hinweg geteilt werden muss
  • Koordination erforderlich ist, um doppelte Arbeit zu verhindern

Die Lösung: Drei Komponenten

1. Mesh Sync Wenn mehrere Agenten parallel arbeiten, müssen ihre Beobachtungen in ein gemeinsames Wiki zusammengeführt werden:

  • Standardstrategie: Last-Write-Wins in den meisten Fällen
  • Konfliktauflösung: Zeitstempel-basiert mit manueller Anpassung
  • Merge-Strategie:
    • Kein Konflikt: Beide Aktualisierungen akzeptieren
    • Konflikt: Neuere bevorzugen oder zur menschlichen Überprüfung kennzeichnen
    • Semantischer Konflikt: Contradiction Resolution auslösen

Implementierung:

Agent A writes: "API rate limit is 100 req/min" (timestamp: 10:00:00)
Agent B writes: "API rate limit is 100 req/min" (timestamp: 10:00:05)
Result: Accept B (last-write-wins, no conflict)

Agent A writes: "API rate limit is 100 req/min" (timestamp: 10:00:00)
Agent B writes: "API rate limit is 200 req/min" (timestamp: 10:00:05)
Result: Flag for human review (conflict)

2. Shared vs. Private Knowledge Nicht alles Wissen sollte gleichermaßen geteilt werden:

Bereich Beschreibung Beispiel
Private Persönliche Beobachtungen, Vorlieben, Workflows "Mein bevorzugter Editor ist VS Code"
Shared Team-/Projektwissen, Entscheidungen, Architektur "Projekt X verwendet Redis zum Caching"

Promotionsmodell:

  • Mit privaten Beobachtungen beginnen
  • Zu Shared promovieren, wenn:
    • Information über mehrere Agenten überprüft ist
    • Information allgemein nützlich ist (nicht persönlich)
    • Mensch explizit als Shared markiert

3. Work Coordination Einfache Koordination, um doppelte Arbeit zu verhindern und Fortschritt zu verfolgen:

Verfolgung:

  • Wer arbeitet an was
  • Was ist blockiert (und warum)
  • Was ist fertig
  • Was braucht Überprüfung

Implementierung:

  • Statusfeld auf Seiten: in-progress, blocked, done, needs-review
  • Zuständigkeitsfeld: Welcher Agent/welche Person ist verantwortlich
  • Blockierungsbeziehungen: Seite A blockiert Seite B

Kein vollständiges Task-Management-System - nur genug, um doppelte Arbeit zu verhindern.

Implementierung

Basierend auf Agent Memory-Erfahrung:

  1. Mesh Sync aktivieren mit Konfliktauflösung
  2. Scoping implementieren (privat vs. geteilt)
  3. Einfache Koordinationsfelder zu Seiten hinzufügen
  4. Mit Event-Driven Automation für Sync-Trigger integrieren
  5. Alle Multi-Agent-Operationen in Audit Trail protokollieren

Vorteile

  • Kollaboration: Mehrere Agenten können zur gleichen Knowledge Base beitragen
  • Effizienz: Verhindert doppelte Arbeit
  • Flexibilität: Unterstützt sowohl persönliches als auch Team-Wissen
  • Skalierbarkeit: Funktioniert mit beliebig vielen Agenten
  • Transparenz: Klare Sicht darauf, wer was tut

Wann zu verwenden

  • Team-Umgebungen mit mehreren Benutzern
  • Multi-Agent-Setups (parallele Recherche, Coding, etc.)
  • Gemeinsame Knowledge Bases
  • Situationen, die Koordination erfordern

Wann NICHT zu verwenden

  • Single-User, Single-Agent-Szenarien
  • Situationen, in denen Einfachheit wichtiger ist als Kollaboration
  • Sehr kleine Knowledge Bases

Verwandte Concepts

Siehe auch

Beziehungen