You can move a multi-model agent from Amazon ECS on Fargate to Amazon Bedrock AgentCore Runtime without necessarily replacing its model-routing logic—but AgentCore does not automatically port an ECS service or take over the application’s model integrations. Treat the move as a change to the agent’s hosting, runtime contract, permissions, networking, and session model, then test each model and retrieval path before cutover.
What changes—and what can stay the same
Amazon Bedrock AgentCore Runtime is a managed hosting layer for agents, not a replacement for an agent’s orchestration code. Your application can continue choosing among Amazon Bedrock, Amazon SageMaker AI, and other model backends, provided the application is configured and authorized to reach them. You remain responsible for routing, credentials, retrieval, tools, and application behavior. AWS describes the platform as framework- and model-flexible in its AgentCore Runtime overview.
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A relevant AWS example is Sanhita Sarkar’s 18 September 2026 post, “Migrating multi-model AI agents to Amazon Bedrock AgentCore runtime.” It describes a healthcare agent moved from ECS/Fargate that continued to use Bedrock, SageMaker AI, a containerized model server, and OpenSearch Service for vector retrieval. The backends shared a Hugging Face Messages API-compatible interface. The post says, “The migration required no changes to the core agent logic.” That is a reported result for that workload and architecture—not a guarantee that another ECS application will need no code or configuration changes.
Keep the boundary clear during planning: AgentCore hosts the runtime, while your agent still connects to its chosen model and data services. Draw the current dependency map—including every model, retrieval service, tool, secret, and outbound endpoint—and verify each connection from the target runtime’s network and execution role.
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Choose the Runtime compute type before adapting the workload
AgentCore offers microVMs and Instances. They differ in session duration, state, infrastructure ownership, networking, and GPU support; compute type cannot be changed after a runtime is created. Use the requirements of the workload—not the familiarity of the ECS configuration—to make this decision. AWS’s Runtime documentation and Instances documentation describe the following distinctions.
| Decision point | microVMs | Instances |
|---|---|---|
| Operating model | Fully managed, serverless sessions | AWS-managed EC2 infrastructure in your account through a capacity provider |
| Maximum session duration | Up to 8 hours | Up to 14 days |
| State across session stops | Session-based microVM context | Persistent compute and volumes can support stop-and-resume workflows |
| GPU | Not supported in the documented comparison | Supported GPU and accelerator instance families can be selected through the capacity provider |
| Agent collaboration | One runtime hosts one agent per session | Multiple agents can share an instance and filesystem under the documented session model |
| Networking and account control | PUBLIC or VPC options in AWS’s comparison | VPC; EC2 resources and their billing are in your account |
| Typical fit | Lightweight, API-driven work that starts and completes within hours | Long-running, stateful, GPU-dependent, or collaborative workloads |
Provisioning is part of the Instances trade-off: AWS notes that the first invocation for a new session takes longer because instance provisioning is included. Account for startup behavior along with session length, persistent state, GPU needs, VPC design, and the billing model when choosing.
Migration checklist for an ECS/Fargate agent
1. Inventory the application and its dependencies
Document the agent process and startup command, listening port, health checks, HTTP or streaming behavior, container architecture, environment variables, secrets, filesystem use, and every outbound dependency. Include all model endpoints, retrieval stores, and tools; a model being supported by the platform does not prove that your particular endpoint is reachable from your eventual network configuration.
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Do not assume an ECS task definition can be copied unchanged. Container images are a supported artifact path, but the service’s lifecycle and contract still have to fit the selected AgentCore compute type.
2. Select an artifact path and meet its requirements
For AgentCore Instances, AWS specifies a Runtime HTTP contract: the container must serve GET /ping with a healthy-status JSON response and accept POST /invocations, returning the response payload on port 8080. Confirm that your application or its Runtime SDK adapter implements that contract, and validate image architecture and startup behavior against the chosen compute type. See AWS’s Instances getting-started guide.
You can deploy either a container image or packaged code directly. AWS’s direct-code guide, accessed 5 October 2026, lists these deployment-mode figures:
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| Deployment mode | Package limit | Documented new-session creation rate |
|---|---|---|
| Direct code deployment | 250 MB | 25 sessions per second |
| Container-based deployment | Up to 2 GB | 1.6 sessions per second |
These are AWS-published service limits and deployment figures, not independent performance benchmarks. Check the live direct code deployment documentation when planning production capacity. AWS suggests container deployment when the package is larger than 250 MB, an existing container CI/CD pipeline matters, or specialized packaging is required.
3. Remap permissions and network paths
Map the responsibilities of the ECS task role and execution role to the AgentCore Runtime execution role and invocation permissions. For Instances, infrastructure is provisioned in your AWS account through a capacity provider, and the Runtime must be configured for VPC networking. For either compute type, test access to every model endpoint, retrieval system, secret store, and tool using the actual target role and network configuration before cutover.
4. Decide what happens to session state
Classify state held in process memory, local files, external databases, or ECS volumes as conversation-scoped, durable, shared, or disposable. AgentCore sessions use a runtimeSessionId; AWS documents isolated microVM session contexts and persistent storage for Instances across stop/resume cycles. Neither means an ECS task’s local filesystem or lifecycle maps one-to-one to a Runtime session. Exercise retries, resume behavior, and concurrent sessions with the state design you intend to ship. AWS documents the session models in its microVM guide and Instances guide.
5. Stage rollout and preserve rollback
AWS describes Runtime versions as configuration snapshots and endpoints as references to versions. Use that versioning model to test representative multi-model paths before shifting production traffic. Verify that runtime logs and traces, error handling, and application-level telemetry are useful; instrument model selection, provider failures, retrieval latency, and token or inference spend as well as runtime behavior. Retain a known-good version and a tested route back to the ECS service during rollout. See the Runtime documentation for the version and endpoint model.
6. Keep image and dependency maintenance in the operating plan
For container-image deployment, AWS says AgentCore patches the underlying compute OS kernel, while you remain responsible for agent code and dependencies and for regularly rebuilding and redeploying from a current secure base image. Direct code deployment has different runtime patch responsibilities, but application code and dependencies still need updates. Managed hosting reduces infrastructure work; it does not make an application artifact maintenance-free. AWS outlines these responsibilities in its deployment guide.
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AWS describes microVM pricing as consumption-based and Instances as EC2 compute billed in the customer’s account, where existing EC2 pricing mechanisms may apply. Those descriptions do not establish that AgentCore costs less than ECS/Fargate for a particular agent. The cited AWS material does not provide a matched comparison for a specific request mix, model spend, or network topology.
Best Value
Build a like-for-like estimate using the same traffic profile and include:
- Active and idle compute, plus session duration and startup behavior
- Inference across every model backend, not just the runtime host
- Persistent storage and network transfer or connectivity costs
- Logs, traces, and observability services
- Image builds, deployment pipeline work, and operational effort
Use those inputs to compare the full current ECS/Fargate system with the proposed Runtime design; managed orchestration is an architecture choice, not a savings figure.
When the migration is a good fit
AgentCore Runtime is worth evaluating when managed agent hosting and its session model address a real operational need, and your team can validate the new contract, permissions, networking, and state behavior. Preserve the existing multi-model routing where it remains appropriate, but treat each integration as an explicit migration test. Choose microVMs for session-oriented work within their documented duration and capabilities; choose Instances when longer sessions, persistent compute, GPUs, or shared-instance collaboration are requirements. Make the compute choice before creating the runtime, then prove functionality and cost with your workload rather than extrapolating from AWS’s example.
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