# AGENTCOST01-BP02 Optimize multi-agent collaboration cost through efficient handoff patterns
In multi-agent systems, the largest hidden cost is redundant context that travels with every handoff. Structured handoff messages and shared memory keep coordination cost proportional to task complexity rather than conversation length.
**Desired outcome:**
+ You have handoff messages carrying only the task specification, relevant facts, and constraints, not full conversation transcripts.
+ You have collaborating agents sharing common context through a managed memory layer instead of re-transmitting it on every handoff.
+ You track per-handoff and per-workflow coordination costs as distinct metrics.
**Common anti-patterns:**
+ Passing full conversation history in every handoff, causing input token cost to scale with conversation length regardless of relevance to the receiving agent.
+ Building deep supervisor hierarchies where multi-level nesting adds orchestration model invocations at each layer, so coordination cost exceeds execution value.
+ Skipping shared memory for collaborating agents, re-transmitting common facts in every agent's context window and causing linear cost growth with agent count.
+ Running multi-agent workflows without handoff cost tracking, reducing the risk of identification of workflows where coordination overhead has grown disproportionate to the execution work.
**Benefits of establishing this best practice:**
+ Coordination overhead stays proportional to task complexity rather than conversation length or agent count.
+ Shared memory removes redundant context transmission, reducing per-handoff token cost.
+ Per-handoff cost visibility enables data-driven tuning of multi-agent interaction patterns.
**Level of risk exposed if this best practice is not established:** High
## Implementation guidance
The most expensive thing an agent can send is context the receiver already has (or doesn't need). Every handoff that copies the full conversation across the boundary pays again for information that never changed. Treat handoff messages as structured summaries containing the task specification, relevant facts, and constraints the worker must respect. [Amazon Bedrock AgentCore Memory](https://docs.aws.amazon.com/bedrock-agentcore/latest/devguide/memory.html) enables write-once, read-many patterns where one agent stores a fact under a session ID and actor ID, and every collaborator reads it without re-embedding it in their own prompt. [Amazon Bedrock AgentCore Gateway](https://docs.aws.amazon.com/bedrock-agentcore/latest/devguide/gateway.html) adds the corresponding discipline on the tool side, using MCP-compatible Semantic Tool Selection to present only tools relevant to the current intent rather than the full catalog.
Context distillation means that a small model call or a Lambda function can compress incoming context into the minimum sufficient information for the next agent's task before the handoff crosses the boundary. The cost of the distillation call is typically less than the cost of repeatedly transmitting untrimmed context through deeper workflows.
Every supervisor-worker layer adds at least one inference for delegation and one for synthesis. Hierarchies deeper than three levels compound that overhead quickly, and most deep hierarchies can be flattened by replacing intermediate supervisors with direct worker-to-worker communication through the AgentCore Runtime Agent-to-Agent protocol. The diagnostic metric is the orchestration-to-execution token ratio. Supervisors should consume no more than 20% of total workflow tokens, leaving 80% for workers doing execution. A ratio that drifts above 20% means coordination has grown disproportionate to work.
Visibility is a prerequisite for these patterns. [Amazon Bedrock AgentCore Observability](https://docs.aws.amazon.com/bedrock-agentcore/latest/devguide/observability.html) provides distributed tracing so agent-to-agent communication costs appear as their own category rather than hidden inside aggregate workflow cost. [Amazon Bedrock AgentCore Evaluations](https://docs.aws.amazon.com/bedrock-agentcore/latest/devguide/evaluations.html) runs in real time against live tool-call traces and as offline test suites in CI/CD pipelines, so redundant or unnecessary invocations are caught early.
### Implementation steps
1. **Design structured handoff messages:** Replace full conversation history with a summary object containing the task specification, relevant facts, and the constraints the receiving agent must respect. Version the message schema so receivers can reject malformed handoffs.
1. **Insert context distillation at boundaries:** Add a small-model call or Lambda function that extracts minimum sufficient context before each handoff, so input tokens at transitions reflect current task needs rather than accumulated history.
1. **Configure shared memory with ownership rules:** Provision [Amazon Bedrock AgentCore Memory](https://docs.aws.amazon.com/bedrock-agentcore/latest/devguide/memory.html) accessible to all collaborating agents, and document which agent owns writes to each namespace so shared state has a clear provenance.
1. **Flatten deep hierarchies:** Audit multi-agent workflows for supervisor-worker depth greater than three levels, and replace intermediate supervisors with direct worker-to-worker communication through the AgentCore Runtime Agent-to-Agent protocol where the routing can be made explicit.
1. **Expose specialized agents through Gateway:** Publish agents as tools through [Amazon Bedrock AgentCore Gateway](https://docs.aws.amazon.com/bedrock-agentcore/latest/devguide/gateway.html) MCP server capabilities, and turn on Semantic Tool Selection so collaborating agents see only tools relevant to the current request.
1. **Evaluate tool-call efficiency in CI and in production:** Run [Amazon Bedrock AgentCore Evaluations](https://docs.aws.amazon.com/bedrock-agentcore/latest/devguide/evaluations.html) against live tool-call traces to flag inefficient usage at runtime, and against offline test suites in the CI/CD pipeline to catch regressions before deployment.
1. **Track the orchestration-to-execution ratio:** Tag every invocation with workflow-id and agent-role, build CloudWatch dashboards that display the supervisor-to-worker token ratio per workflow, and configure AWS Budgets alerts when orchestration overhead exceeds 20% of total workflow token cost.
## Resources
**Related best practices:**
+ [AGENTCOST01-BP01 Use the reflection pattern to design efficient agent reasoning loops](agentcost01-bp01.html)
+ [AGENTCOST01-BP03 Implement cost-effective patterns like hybrid supervisor for multi-agent coordination](agentcost01-bp03.html)
+ [AGENTCOST01-BP04 Design agent hierarchies and delegation patterns that reduce coordination overhead](agentcost01-bp04.html)
+ [AGENTCOST05-BP02 Implement distributed cost tracing for multi-agent workflows](agentcost05-bp02.html)
**Related documents:**
+ [Amazon Bedrock AgentCore Memory](https://docs.aws.amazon.com/bedrock-agentcore/latest/devguide/memory.html)
+ [Amazon Bedrock AgentCore Runtime](https://docs.aws.amazon.com/bedrock-agentcore/latest/devguide/agents-tools-runtime.html)
+ [Amazon Bedrock AgentCore Gateway](https://docs.aws.amazon.com/bedrock-agentcore/latest/devguide/gateway.html)
+ [Amazon Bedrock AgentCore Observability](https://docs.aws.amazon.com/bedrock-agentcore/latest/devguide/observability.html)
+ [Amazon Bedrock AgentCore Evaluations](https://docs.aws.amazon.com/bedrock-agentcore/latest/devguide/evaluations.html)
+ [Agentic AI patterns and workflows on AWS](https://docs.aws.amazon.com/prescriptive-guidance/latest/agentic-ai-patterns/introduction.html)
**Related videos:**
+ [AWS 2025 - AgentCore Deep Dive: Memory](https://www.youtube.com/watch?v=-N4v6-kJgwA)
+ [AWS re:Invent 2024 - Balance cost, performance & reliability for AI at enterprise scale (AIM3304)](https://www.youtube.com/watch?v=Lwvv8Q33eeE)
**Related examples:**
+ [GitHub: awslabs/amazon-bedrock-agentcore-samples - Multi-agent tutorials](https://github.com/awslabs/amazon-bedrock-agentcore-samples/tree/main/01-tutorials/01-AgentCore-runtime/03-advanced-concepts)
+ [GitHub: awslabs/amazon-bedrock-agentcore-samples - Evaluations tutorials](https://github.com/awslabs/amazon-bedrock-agentcore-samples/tree/main/01-tutorials/07-AgentCore-evaluations)
**Related services:**
+ [Amazon Bedrock AgentCore](https://aws.amazon.com/bedrock/agentcore/)
+ [Amazon CloudWatch](https://aws.amazon.com/cloudwatch/)