To build multi-agent systems on AWS in 2026, most teams write agents with an open framework such as Strands Agents, LangGraph or CrewAI and run them on Amazon Bedrock AgentCore, which supplies the runtime, memory, tool gateway, identity, observability, evaluation and policy services. The hard choices are not which SDK to use but how many agents you really need, how they coordinate and how you control cost and permissions.
Updated October 2026
Who this is for: cloud architects, platform engineering heads and CTOs at AWS-first companies in financial services, retail, telecom, media and manufacturing.
The business problem: agent sprawl on your AWS bill
Teams that start with one helpful agent quickly add a researcher, a planner, a checker and a writer. Each extra agent adds model calls, latency, IAM permissions and failure points. Without a shared runtime and controls, you end up with dozens of agents nobody can trace or cost. That is how projects join the 40%+ of agentic AI projects Gartner expects to be canceled by end of 2027.
What changed in 2026
- Amazon Bedrock AgentCore now lists Harness, Runtime, Memory, Gateway, Identity, Code Interpreter, Browser, Observability, Evaluations, Optimization, Policy, Registry and Payments services.
- AgentCore Runtime works with "CrewAI, LangGraph, LlamaIndex, Google ADK, OpenAI Agents SDK, and Strands Agents", supports MCP and A2A, and provides "support for multi-modal and multi-agent agentic workloads".
- The AgentCore product page says security controls are "enforced at the platform layer with access policies verified by automated reasoning".
- AWS added prescriptive guidance for multi-tenant agentic AI, covering siloed and pooled agent deployments and tenant isolation.
The AWS building blocks for multi-agent systems
| Need | AWS option | Notes |
|---|---|---|
| Agent code | Strands Agents (open source) or any framework | Strands is model-driven and supports workflow, graph and swarm multi-agent patterns |
| Hosting agents | AgentCore Runtime | Serverless, framework-agnostic, MCP and A2A |
| Shared memory | AgentCore Memory | Memory stores can be shared across agents |
| Tools | AgentCore Gateway | Turns APIs and Lambda functions into MCP-compatible tools |
| Permissions | AgentCore Identity and Policy | Per-agent identity; deterministic boundaries and business rules |
| Quality and tracing | AgentCore Observability and Evaluations | Trace steps and tool calls; automated agent assessment |
| Discovery | AgentCore Registry | Catalog of agents, MCP servers, tools and skills |
Reference architecture
API Gateway, app, EventBridge event
Plans and delegates (AgentCore Runtime)
Research · policy check · action (A2A)
Lambda, internal APIs, knowledge bases (MCP)
Figure: A supervisor-style multi-agent system on AWS. Requests enter through an API or event, a supervisor agent on AgentCore Runtime delegates to specialist agents over A2A, agents call tools exposed as MCP through AgentCore Gateway, and shared services handle memory, identity, policy, tracing, evaluation and models.
Choosing a coordination pattern
AWS describes multi-agent setups as horizontal (peer specialists) or vertical (hierarchical) in its agentic AI overview. In practice:
- Workflow / sequential: fixed order, easiest to test. Good for document and claims pipelines.
- Supervisor (vertical): one agent plans and delegates. Good for customer service and IT ops.
- Graph: explicit branches and loops. Good when steps depend on earlier results.
- Swarm (horizontal): peers hand work to each other. Powerful but hardest to predict and cost.
Microsoft's vendor-neutral guidance applies on AWS too: multi-agent is justified when a single agent fails because of prompt complexity, tool overload or security boundaries. Otherwise, a single agent with tools is cheaper. See agentic workflows for pattern trade-offs.
Designing a multi-agent system on AWS? Get a free architecture and cost sizing review: we check whether you need multiple agents, which pattern fits, and the expected cost per task. Book the review.
Where it's heading: 2023 → 2030
| Period | Multi-agent on AWS |
|---|---|
| 2023–2024 (history) | Agents for Amazon Bedrock and custom Lambda orchestration; multi-agent mostly DIY. |
| 2025 | Strands Agents open-sourced (May 2025); AgentCore launched for production agents. |
| 2026 (now) | AgentCore adds policy, evaluations, registry and more; MCP and A2A are standard; framework-agnostic runtime. |
| 2028 (expected) | Agents from different vendors and tenants call each other through registries and A2A as routine. |
| 2030 (our view) | Multi-agent systems span cloud and edge, with one policy and audit layer across providers. |
How to start in 30 days
- Week 1: Build the use case as one agent with tools. Record where it fails.
- Week 2: Split only the failing responsibilities into specialist agents; give each its own identity and minimal tool set.
- Week 3: Deploy on a managed runtime with tracing and evaluations; set iteration and cost limits per task.
- Week 4: Load-test with real cases; compare single- vs multi-agent on accuracy, latency and cost per task before go-live.
How NexaStack helps
Many enterprises run agents across AWS, other clouds and their own data centres or plants. NexaStack is a unified control plane to deploy, operate and govern agents across edge devices, machines, robots and private cloud, with a Composable Agent Framework, Unified Inference Engine, Observability and Evaluation Layer, Agentic Identity Management and Policy, Safety and Governance. Use it alongside AWS services when data must stay on-premises or at the edge. Related reading: what is agentic AI, AI agent infrastructure stack, agent observability tools and AI agents with Amazon Kendra.
FAQ
What is a multi-agent system?
A set of AI agents, each with its own role and tools, that coordinate to complete a task one agent cannot handle reliably on its own.
What is Amazon Bedrock AgentCore?
AWS's platform for running production AI agents built with any framework and model, with services for runtime, memory, tool gateway, identity, observability, evaluation and policy.
Strands Agents vs LangGraph vs CrewAI on AWS?
All run on AgentCore Runtime. Strands is AWS's model-driven open source SDK; LangGraph suits explicit graphs; CrewAI suits role-based crews. Pick by team skills and how much control you need over the flow.
How do we stop multi-agent costs and permissions getting out of hand?
Give every agent its own identity and minimal tools, enforce policies at the platform layer, cap iterations and spend per task, and track cost per completed task. See AgentOps.
What are the latest multi-agent trends on AWS in 2026?
Framework-agnostic managed runtimes, A2A agent-to-agent calls, MCP tool gateways, agent registries, policy enforcement and built-in evaluations.
Get your multi-agent design right before you scale it. Book a NexaStack demo and free AWS agent architecture review. Book a demo.
