Single AI agents (you can learn about Bedrock AgentCore here) are hitting their limits. You've probably seen examples of ai agent trying to research, analyze, write, and edit simultaneously, doing none of it particularly well. The solution isn't a bigger model or more prompt engineering. It's multiple specialized agents coordinating like a development team.
Swarm: Multi-agent orchestration pattern where specialized agents coordinate through decentralized handoffs rather than central control.
Handoff: Explicit transfer of work between agents when one completes their expertise area or encounters a task better suited for another specialist.
Entry Point: The agent that receives initial user input and begins the coordination process.
Traditional AI workflows use either one generalist agent or rigid pipelines where Task A always leads to Task B. Swarms flip this entirely. Specialized agents coordinate dynamically, handing off work based on actual need rather than predetermined flows.
A researcher finds sources and passes findings to an analyst. The analyst identifies gaps and loops back to research with specific questions. A writer drafts content, and a reviewer catches issues that might require circling back to either research or writing. Everyone sees the full context. Nobody's locked into a sequence.
This isn't agents randomly chatting. It's structured coordination with shared working memory, clear specialization, and guardrails preventing endless loops.
No central orchestrator. Agents decide who handles the next step based on task requirements, not a fixed workflow.
Shared context across all agents. Every agent sees the conversation history and intermediate results. No information silos where one agent doesn't know what another discovered.
Dynamic handoffs. When an agent hits the edge of their expertise or completes their part, they explicitly pass to the most appropriate specialist.
Emergent solutions. The best path often reveals itself through agent coordination rather than upfront planning.
Here's what a basic content creation swarm looks like:
from strands import Agent
from strands.multiagent import Swarm
researcher = Agent(
name="researcher",
system_prompt="You specialize in technical research and source verification."
)
writer = Agent(
name="writer",
system_prompt="You transform research into clear technical narratives."
)
reviewer = Agent(
name="reviewer",
system_prompt="You refine for clarity and technical accuracy."
)
swarm = Swarm(
[researcher, writer, reviewer],
entry_point=researcher,
max_handoffs=10,
execution_timeout=600.0,
node_timeout=180.0
)
result = swarm("Create a post about distributed graph training")
The researcher gathers information, the writer crafts narrative, the reviewer polishes—but the actual coordination is fluid based on what each agent discovers.
For more background on the Strands framework and how it integrates with AWS infrastructure, check out our post on getting started with Strands on Amazon Bedrock AgentCore.
Descriptive agent names matter. "Researcher" beats "Assistant_1" when you're debugging logs at 2am trying to understand why agents are looping.
Focused system prompts prevent scope creep. A researcher who knows they're only finding and verifying information performs better than one with vague "help the user" instructions.
Strict timeouts are non-negotiable. Official defaults are 5 minutes per agent and 15 minutes total, but for faster iteration we typically tighten these to 3 and 10 minutes respectively. Set both per-agent (node_timeout) and total (execution_timeout) limits based on your workflow complexity.
Handoff detection saves you. Without max_handoffs monitoring, you get ping-pong matches where the same two agents pass work back and forth 50+ times. Monitor for repetitive cycles in your logs.
Generic prompts create indecisive agents. They don't know when to push through versus when to hand off, resulting in circular conversations.
Without guardrails, swarms loop infinitely. I've watched a researcher and writer trade the same document 73 times before realizing my handoff window was too permissive.
Shared context grows with every handoff. For complex workflows, agents might need to summarize contributions to keep token counts manageable.
Content workflows requiring research, writing, fact-checking, and editing with unpredictable iteration needs. We've used this pattern extensively in our own technical content creation, including our GraphStorm pipeline guide.
Complex analysis where you need financial, technical, and market perspectives coordinating findings.
Code review systems where architecture, security, and performance experts each examine different aspects then synthesize recommendations.
Customer service where qualification, troubleshooting, and resolution each need specialized knowledge, and the path varies by issue.
NOTE: Swarms are overkill for simple Q&A or deterministic processes. Use them when tasks require diverse expertise you can't pack into one agent, when the optimal path isn't predetermined, or when you need iterative refinement based on intermediate results.
Building swarms locally is straightforward. Running them in production with proper security, observability, and cost control is another story. AWS Bedrock AgentCore provides the infrastructure layer:
You write swarm logic with Strands, configure your deployment with agentcore configure, launch with agentcore launch, and get enterprise scaling without custom infrastructure work.
Swarms aren't universal. They're overkill for simple Q&A or deterministic processes. Use them when:
Begin with three agents and one workflow. Perfect the handoff criteria before scaling up. Monitor execution logs to see how agents actually coordinate versus your expectations.
The breakthrough moment comes when agents make handoffs you didn't anticipate but that make perfect sense. That's when you know it's working.
NOTE: If you're working with graph-based data or need to process complex relationships between entities, combining swarm orchestration with specialized tools like GraphStorm can unlock powerful capabilities. The swarm handles coordination while specialized agents leverage domain-specific frameworks.
With Amazon Nova models offering frontier intelligence at 75% lower cost and AgentCore providing production infrastructure, swarm patterns are economically viable for real workloads. The combination of cost-effective models, solid orchestration frameworks, and enterprise-grade infrastructure means sophisticated multi-agent systems are no longer experimental curiosities.
We're early in understanding what coordinated agent swarms can accomplish, but the foundations are solid. The frameworks work, the models are capable, and the infrastructure is production-ready.
Interested in exploring swarm-based solutions for your workflows? Metal Toad's team can help you design and deploy multi-agent systems on AWS. Get in touch to discuss your use case.