Scale agents with shared skills and tools

To scale agent services, create multiple agent instances that share the same skill and tool definitions. This pattern supports horizontal scaling, keeps behavior consistent, and reduces management overhead.

Key benefits

  • Parallel tool invocation: Spawned agents can invoke the same tools concurrently, so the system can handle traffic spikes without overloading a single agent instance.
  • Stateless tool catalogs: Shared skill and tool definitions eliminate duplication and reduce management overhead. Each spawned agent loads the same schema catalog.
  • Efficient resource utilization: Tool backends (databases, APIs, services) can handle parallel requests from multiple agent instances, scaling horizontally with demand.

Scaling architecture

When you deploy multiple agent instances:

  1. Define shared skills once: All agent instances reference the same skill definitions, so you ensure consistent behavior and reduce configuration drift.
  2. Register tools centrally: Maintain tool catalogs (schemas, endpoints, authentication) in a central registry that all agents access at runtime.
  3. Spawn agents dynamically: As load increases, create new agent instances that immediately inherit the shared tool catalog without reconfiguration.
  4. Distribute requests: Route incoming requests to available agent instances. Each agent can invoke tools independently and in parallel.

Example: High-volume customer support

In a customer support scenario with variable load:

  • Baseline: Five agent instances handle standard requests, each invoking the "search_knowledge_base" skill and "create_ticket" tool.
  • Traffic spike: Incoming request volume doubles during peak hours. Automatically spawn five additional agent instances.
  • Parallel execution: All 10 agents invoke the same tools concurrently. The "create_ticket" tool backend receives requests from 10 agent instances simultaneously and queues them for processing.
  • Scale-down: When traffic normalizes, retire the extra agent instances. The five remaining agents continue serving requests with the same skill and tool definitions.

Considerations

  • Tool backend capacity: Ensure your tool backends (databases, APIs, external services) can handle parallel requests from multiple agent instances. Consider rate limiting, connection pooling, and horizontal scaling of the tool backend.
  • Stateless agent design: Keep agent instances stateless so you can spawn or retire any instance without losing context or data. Persist agent state (conversation history, user preferences) in external storage if needed.
  • Tool call ordering: When an agent invokes multiple tools in sequence, ensure tool backends maintain consistency and handle concurrent requests from different agents appropriately.
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