Context sharing ensures agents access a shared organizational knowledge graph rather than operating in isolated context windows. When one agent discovers relevant information, such as a service degradation, a deployment change, or a customer escalation, other agents working on related tasks have immediate access. This eliminates the "distributed ignorance" problem where the organization has all the information but no single agent can see the full picture.

Coordination and routing means tasks get directed to the right agent (or combination of agents) based on capability, access permissions, and workload. The orchestration layer determines optimal assignment without manual configuration, handles task decomposition for complex requests, and manages the flow of work across multiple agents.

Conflict resolution prevents chaos when multiple agents attempt to modify the same resource. Whether it's updating the same ticket, changing the same configuration, or notifying the same stakeholder, orchestration provides deterministic resolution through priority rules, locking mechanisms, or merge strategies. Without this, multi-agent systems exhibit race conditions that create duplicate or contradictory actions.

Human-in-the-loop routing ensures high-impact actions go through human review before execution. Agents prepare the analysis, draft the recommendation, and queue the action. Humans approve, modify, or reject. The orchestration layer manages the approval queue, confidence thresholds, and escalation paths. This is the "agent inbox" model: agents do the heavy lifting, humans steer.

Governance enforcement means every orchestrated workflow respects governance constraints. Access controls determine what data each agent can access. Cost budgets span the entire workflow. Complete audit trails run from trigger to completion. Policy enforcement applies to inter-agent interactions, not just individual agent actions.

Observability provides structured traces of every agent decision, context access, and inter-agent communication. When a multi-agent workflow produces unexpected results, teams can replay the entire workflow step by step. This is distributed tracing for AI agents, essential for debugging, optimization, and compliance.