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Run the agent as a stateless or checkpointed ECS service, place routine capacity on Fargate Spot, retain an On-Demand Fargate safety floor, and design every task to survive a two-minute interruption. Persist state outside the task filesystem, handle SIGTERM, and checkpoint active work before the task stops. Use ECS Service Connect for named-service discovery and health-aware routing, keep proxy and application retries bounded and idempotent, and deploy revisions with ECS blue/green traffic shifting. A readiness health check, EventBridge alerts, deployment rollback, and a tested capacity fallback turn Spot interruptions and bad releases into recoverable events rather than lost conversations or duplicate tool actions.
This guide shows the architecture, configuration sequence, retry rules, blue/green validation flow, fallback behavior, and operational checks for an AI agent running on ECS Fargate Spot. AWS behavior and default values cited here reflect documentation verified on October 25, 2026; they are not an independent reliability or cost benchmark.
What architecture keeps an AI agent resilient on Fargate Spot?
Use a service that can be stopped and started without relying on local task storage. The service should reconstruct its working context from durable systems, or resume from checkpoints written before interruption. A capacity-provider strategy combines FARGATE_SPOT for ordinary work with FARGATE for a critical baseline, while subnets in multiple Availability Zones reduce dependence on one zone.
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Store conversation state, workflow position, tool-call records, idempotency keys, and checkpoints in a durable external system. Treat the container filesystem as disposable. A checkpoint should identify the workflow, the last completed step, inputs needed to replay safely, and a version of the agent code or prompt policy that produced it. This lets a replacement task continue or retry without guessing what the interrupted task had already done.
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Use Spot for interruptible work, not an unprotected single point of failure
Fargate Spot uses spare AWS capacity. AWS states: “When AWS needs the capacity back, your tasks are interrupted with a two-minute warning.” ECS sends the task a SIGTERM, emits a task-state event through EventBridge, and then stops the task. Keep an On-Demand provider in the strategy for the minimum capacity required to serve critical traffic, and configure more than one Availability Zone.
| Concern | Fargate Spot | On-Demand Fargate fallback |
|---|---|---|
| Capacity source | Spare capacity; can be reclaimed | Regular Fargate capacity |
| Interruption behavior | Two-minute warning, SIGTERM, task stop | No Spot reclamation event |
| Best use | Checkpointed, retryable agent work | Baseline service capacity and urgent work |
| Required resilience | External state, graceful shutdown, replay-safe operations | Still requires health checks and deployment rollback |
Set the shutdown window deliberately
Configure the container stopTimeout to no more than 120 seconds, matching the maximum Spot warning window. On SIGTERM, stop accepting new work, mark the task as draining, checkpoint or finish the active operation, acknowledge durable progress, and exit. Do not begin a new long tool call after SIGTERM; there may not be enough time to complete it.
How do I configure an ECS capacity-provider strategy with Spot fallback?
Attach both providers to the service and give the On-Demand provider a base capacity when a continuously available floor is required. The exact weights depend on traffic and risk; the important property is that the service can place tasks when Spot has no capacity.
- Register capacity providers. Ensure the cluster has
FARGATEandFARGATE_SPOTavailable. - Set a baseline. Use the strategy’s
basevalue onFARGATEfor the number of tasks that must remain On-Demand. Use providerweightvalues to distribute additional tasks. - Spread placement. Configure subnets in multiple Availability Zones and security-group rules that allow the agent and its dependencies to communicate.
- Deploy the service. Confirm that desired count, minimum healthy percentage, and maximum percentage leave enough room for replacement tasks during a rollout.
- Observe placement. Alert on the ECS service event represented by
SERVICE_TASK_PLACEMENT_FAILURE. A Spot shortage is a capacity condition, not an application-health failure, so your alert should route operators toward fallback capacity rather than code debugging.
If the service cannot obtain Spot capacity, ECS may continue placing tasks on the other provider when the strategy permits it. If your strategy has no usable On-Demand capacity, the desired count can remain unmet. Keep the fallback provider, subnets, quotas, and IAM permissions tested; merely listing FARGATE in configuration does not guarantee placement.
How does ECS Service Connect route requests for the agent?
Service Connect gives client services logical names and lets a managed proxy perform discovery, round-robin routing, passive outlier detection, and health-aware endpoint selection. The client calls the service name instead of tracking task IP addresses, so replacing interrupted tasks or shifting between blue and green revisions does not require a client configuration change.
Name every server port mapping
In each server task definition, assign a name to every port mapping that Service Connect should expose. Set appProtocol to http, http2, or grpc when that protocol applies. Join the client and server services to the same Service Connect namespace. A missing port name, mismatched namespace, or incorrect protocol metadata prevents the intended discovery or header-based test routing.
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Make readiness represent real ability to work
Define a container health check that verifies the agent process and the dependencies it must have before receiving work. A process that is listening but cannot reach its model gateway, state store, or tool broker is not ready. Service Connect can send traffic to healthy tasks immediately; it can wait for an UNKNOWN health result for up to eight minutes under the configured container health-check behavior, and it can replace unhealthy tasks. Keep the check fast enough to provide useful deployment feedback, but make it meaningful enough to prevent early traffic.
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What retry behavior should an AI agent use with Service Connect?
AWS documents a Service Connect proxy default of two retries and a 15-second per-request timeout in its 2026 documentation. The proxy retries a failed proxied connection while avoiding the host used for the previous attempt. Treat those proxy attempts as transport-level behavior, then add only a small, explicit application retry policy.
Distinguish safe reads from state-changing tool calls
- Idempotent operations: bounded retries with exponential backoff and jitter are appropriate for transient connection failures, timeouts, or an endpoint removed by health-aware routing.
- Mutating operations: send an idempotency key or durable request ID with every request. A timeout does not prove that the remote tool failed; without a key, retrying can create a duplicate charge, ticket, message, file, or workflow action.
- Non-retryable failures: authentication errors, invalid tool arguments, policy denials, and deterministic validation errors should fail quickly and be surfaced to the orchestrator.
Keep the retry budget finite
Use a request deadline shorter than the agent’s overall workflow deadline, and reserve time for checkpointing and shutdown. For example, if a tool call has a 15-second proxy timeout, the application should not stack unlimited 15-second attempts inside a user-facing request. Record the attempt number, correlation ID, service name, endpoint outcome, and final disposition so operators can tell a slow dependency from a retry storm.
Prevent layered retry explosions
Service Connect may perform its two proxy retries while an SDK, agent loop, queue consumer, and workflow engine each retry as well. Decide which layer owns retries for each operation. A common pattern is one short application retry for idempotent calls, with the proxy’s behavior accepted as part of the transport budget; a higher-level workflow retry then resumes from a checkpoint rather than replaying every completed tool call.
How do I make Spot interruption safe for in-flight agent work?
Interruption safety comes from a predictable shutdown path, not from trying to keep a task alive after the warning. The task must be able to stop accepting work, persist enough state, and let another task resume.
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- Receive SIGTERM. Install a signal handler in the agent process or its supervisor. Confirm that the signal reaches the actual worker rather than only a shell wrapper.
- Enter draining mode. Mark the task unavailable for new requests and let Service Connect health signaling remove it from useful routing.
- Classify the active operation. If it is before the side effect, checkpoint for replay. If the remote system accepted it, persist the request ID and await reconciliation on the replacement task.
- Write the checkpoint. Persist workflow state, completed tool calls, pending calls, input references, and a timestamp outside the task filesystem.
- Flush and exit. Close connections, flush logs and metrics, and exit before
stopTimeout. Do not wait indefinitely for an unavailable dependency.
Use the EventBridge task-state event to correlate the interruption with the checkpoint and replacement task. A checkpoint-lag metric should show how much work was at risk when the warning arrived; a rising lag indicates that the agent needs smaller work units or faster persistence.
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How do I blue/green deploy an ECS service using Service Connect?
Select the ECS deployment controller’s blue/green strategy and use Service Connect managed traffic shifting. Blue remains the production revision while green starts as a test endpoint. Provide enough capacity for both revisions, configure health checks and CloudWatch alarms, define a bake period, and verify rollback before enabling automatic production cutover.
Prepare the task definitions
Keep the Service Connect namespace, port name, and protocol metadata consistent between revisions unless the change itself is what you are testing. Update the readiness check when the new agent has new dependencies. Ensure the green revision can access its model, state store, tool services, secrets, and observability destinations before it receives validation traffic.
Send controlled requests to green
For HTTP, HTTP/2, or gRPC mappings, use the default x-amzn-ecs-blue-green-test header for test routing, or configure a custom header rule. Header-based routing requires the Service Connect port mapping’s appProtocol to be http, http2, or grpc. If the service is plain TCP, use port-based testing instead.
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Validate agent-specific behavior
- Run representative prompts, including long-running and cancellation cases.
- Exercise every important tool call, including a failed call and a replay with the same idempotency key.
- Measure first-token and complete-response latency, timeout rate, proxy errors, and application errors.
- Force or simulate a checkpoint-and-resume path and verify that no completed side effect is duplicated.
- Confirm that health checks turn unhealthy when a required dependency is unavailable.
Bake, shift, and retain rollback
During the bake period, keep production traffic on blue while alarms observe green. After green meets the acceptance criteria, ECS shifts production routing and updates Service Connect discovery without changing client service names. Continue observing both revisions and retain the ability to return traffic to blue until the new revision has demonstrated stable health under normal and peak conditions.
How do I detect a failed deployment and roll it back?
Enable the ECS deployment circuit breaker with rollback. It evaluates task-launch and health-check failures, marks a deployment FAILED when its configured threshold is reached, and can restore the most recent COMPLETED deployment.
Choose a threshold that matches the service
A percentage threshold can scale with deployment size and may be bounded or unbounded according to the ECS configuration. A COUNT threshold tolerates a fixed number of failures. Use a deliberate threshold: too low can roll back on a transient placement problem, while too high can expose users to a broken revision for too long. Pair the circuit breaker with alarms for latency, error rate, tool failures, checkpoint lag, and dependency health.
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Alert on the events operators can act on
EventBridge emits deployment state-change events. Alert on SERVICE_DEPLOYMENT_FAILED and on Spot-related SERVICE_TASK_PLACEMENT_FAILURE. Include deployment ID, revision, provider, Availability Zone, failure reason, and the last known healthy revision in the notification so the responder can distinguish bad code, bad health checks, and missing capacity.
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Resilience is measurable only when interruption, retry, and rollout signals are connected to the agent’s business state. Monitor these dimensions together:
| Area | Signals | Useful response |
|---|---|---|
| Capacity | Task placement failures, desired-versus-running count, provider and Availability Zone | Allow On-Demand fallback, add capacity, or reduce rollout concurrency |
| Interruption | SIGTERM count, Spot task-state events, checkpoint age and lag | Drain faster, checkpoint smaller units, reconcile pending requests |
| Service Connect | Proxy retries, outlier ejections, endpoint health, request timeout rate | Inspect the dependency or health check before increasing retries |
| Agent quality | Prompt latency, token or tool-call failures, duplicate-request detections | Trace by correlation ID and idempotency key |
| Deployment | Blue/green bake alarms, task health, deployment state changes | Hold or roll back green before production exposure |
Keep correlation IDs from the incoming request through Service Connect, the agent loop, each tool call, checkpoint writes, and the replacement task. This is what lets an operator prove whether a user-visible failure came from capacity loss, a dependency timeout, a duplicate-protected retry, or a defective revision.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What are the most common failure modes and fixes?
Spot capacity disappears
Symptom: running count falls or new tasks remain pending with a placement-failure event. Fix: verify that the strategy includes usable On-Demand capacity, that subnets span Availability Zones, and that account quotas and permissions allow Fargate placement. Do not respond by simply increasing application retries.
The task stops before saving progress
Symptom: replacement tasks restart an entire workflow or cannot determine whether a tool call succeeded. Fix: handle SIGTERM in the worker, shorten work units, persist a durable request ID before a side effect, and set stopTimeout within the two-minute warning.
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Symptom: early requests fail while the process is listening but dependencies are unavailable. Fix: make the readiness check dependency-aware, verify the container health-check grace behavior, and let Service Connect wait for healthy status rather than treating a listening socket as readiness.
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Retries duplicate a tool action
Symptom: a timeout is followed by two side effects. Fix: require an idempotency key or durable request ID, make the tool return the prior result for a repeated key, and reconcile ambiguous outcomes before issuing a new action.
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What is the implementation checklist?
- Persist agent state, checkpoints, tool-call records, and idempotency keys outside the task filesystem.
- Handle SIGTERM, stop accepting work, checkpoint active work, and exit within the configured timeout.
- Use Fargate Spot for interruptible capacity and an On-Demand Fargate baseline for critical availability.
- Spread subnets across Availability Zones and monitor
SERVICE_TASK_PLACEMENT_FAILURE. - Name Service Connect port mappings, set correct protocol metadata, and join client and server services to one namespace.
- Define a readiness health check that includes required dependencies.
- Set explicit application deadlines; keep retries bounded, exponential, jittered, idempotent, and observable.
- Configure blue/green deployment, test green with the default or custom header rule, and use port-based testing for plain TCP.
- Run a bake period with CloudWatch alarms, validate checkpoint recovery and tool calls, and test rollback before automatic cutover.
- Alert on deployment failures, proxy errors, health failures, latency, token or tool-call failures, and checkpoint lag.
What does this design guarantee—and what does it not?
This design makes interruption and deployment failure recoverable: tasks can be replaced, requests can be routed to healthy endpoints, state can resume from a checkpoint, and a failed revision can return to the last completed deployment. It does not guarantee uninterrupted execution of a single in-memory task, eliminate dependency outages, or provide a measured percentage improvement in reliability or cost. AWS documentation supplies the configuration behavior and defaults; an independent benchmark for this exact AI-agent architecture is not established here.
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Can a long-running agent job survive if the Spot warning arrives during a model response?
Only if the workflow treats the response as resumable work. Save the prompt, partial-result policy, and tool state before the call when possible, then mark the step for reconciliation or replay after replacement; an in-memory stream cannot be recovered after the task exits.
How can I tell whether a timeout means the tool ran?
Use the durable request or idempotency ID to query the tool’s result or an outbox/reconciliation record. If the tool cannot report status by that ID, treat the outcome as ambiguous and require operator or workflow reconciliation instead of issuing an unkeyed retry.
Should readiness checks call every dependency on every health probe?
The check should verify dependencies essential to accepting work, but it should be bounded and inexpensive. Cache or use lightweight health endpoints where appropriate; a probe that performs a full model inference or mutating tool call can create load and false failures.
What happens to client configuration when ECS shifts from blue to green?
Clients continue using the same Service Connect logical service name. Discovery and routing are updated by ECS and Service Connect, so clients do not need task IP addresses or a revision-specific endpoint.
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