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The short answer: run each agent execution as a managed workload. A control plane decides when and where it runs and what happens when it fails. A runtime executes it and reports status back. This borrows a well-tested model from distributed systems: work has a lifecycle, placement is a decision rather than a side effect, and retries are a policy you write down. The analogy has limits. An LLM agent is not an operating-system process, and Kubernetes is one well-documented implementation of these ideas, not the only one.
Decide the agent’s lifetime before choosing infrastructure
The first design question is how long an agent run lives and what starts it. Google Cloud’s guidance on hosting AI agents on Cloud Run separates runtime shapes by lifecycle: request-driven stateless services, dedicated always-on stateful instances, queue-consuming worker pools for background fleets, and jobs for run-to-completion workflows. The taxonomy is useful even outside that platform, so the table below uses it as a starting point, with the caveat that it is one vendor’s classification. Google Cloud: Host AI agents on Cloud Run resources
| Shape | Trigger and lifetime | Public request endpoint | State | Typical fit |
|---|---|---|---|---|
| Request-driven service | Starts for each incoming request | Yes | Stateless | An agent answering user requests interactively |
| Always-on stateful instance | Runs continuously on a dedicated instance | Not stated | Stateful | An agent that holds a live session or a standing loop |
| Queue-consuming worker pool | Pulls tasks from a message queue | Not stated | Not stated | Background fleets processing a backlog |
| Job | Runs to completion, then ends | Not stated | Not stated | Bounded, run-to-completion workflows |
“Not stated” means the cited Google Cloud page does not define that attribute for the shape. It does not mean the attribute is impossible.
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#1 Best Overall
The control loop: how a scheduler places and retries work
Infrastructure schedulers and agent fleets share the same underlying loop. Written out for agents, it looks like this:
- Discover eligible work by pulling a task from a queue or accepting a trigger, and confirm its preconditions such as inputs, budget and permissions.
- Filter out execution targets that cannot run the task because of resources, policy or constraints.
- Rank the remaining targets and choose one.
- Commit the assignment, recording it durably before work starts.
- Observe progress through status reports or heartbeats from the runtime.
- Record each state change in durable storage.
- Retry or fail according to policy, and mark terminal failures with a reason.
This loop is an architectural synthesis, not a description of any one product. Kubernetes’ scheduler covers the filter, rank and commit steps for Pods. It does not hold durable agent workflow state, so discovery, observation, recording and the retry policy belong to your application or workflow layer.
Filter, then score, then bind
The Kubernetes scheduler documentation describes placement in these terms: “The scheduler finds feasible Nodes for a Pod and then runs a set of functions to score the feasible Nodes and picks the Node with the highest score among the feasible ones to run the Pod.” Kubernetes: Kubernetes Scheduler
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The documentation names resource requirements, policy, affinity, locality and interference as factors that can matter. For agents, the same filter applies. An agent that needs a node with a particular accelerator, or that must read data held in one region, is excluded from every other target before ranking begins. Ranking then only chooses among genuinely feasible options. If you build your own dispatcher, make the filter explicit in code so that “no eligible target” is visible rather than silently queued forever.
Rank #2
Separate the decision from the commit
The Kubernetes Scheduling Framework separates a scheduling cycle from a binding cycle and exposes plugin extension points for customising behaviour. Attempts that abort, or find no feasible placement, return to a queue for retry. Kubernetes: Scheduling Framework
The practical lesson is that “unschedulable” should be a named state with its own reason and retry timing, not an exception that drops the task. The same separation helps agent fleets: deciding where a run should go is a different step from confirming that it started there, and a failure in the second step should not erase the first.
Completion and availability are different workload shapes
A service is expected to stay up. A Job is expected to finish. Kubernetes Jobs model the second case, and the documentation states the core behaviour directly: “The Job object will start a new Pod if the first Pod fails or is deleted (for example due to a node hardware failure or a node reboot).” Jobs also support parallel execution, and CronJobs create Jobs on a schedule. Kubernetes: Jobs
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The following illustrative manifest uses the batch/v1 Job API to reconcile ten invoice shards with at most five running at once. Check field names against the documentation for your cluster version before deploying.
Rank #3
apiVersion: batch/v1
kind: Job
metadata:
name: invoice-reconcile
spec:
completions: 10
parallelism: 5
backoffLimit: 4
template:
spec:
restartPolicy: Never
containers:
- name: agent
image: example/invoice-agent:1.4.0
env:
- name: SHARD_QUEUE
value: invoices-shardsHere completions defines how many successful runs count as finished, parallelism caps concurrency, and backoffLimit caps retries. Adding a CronJob with a schedule field would run this reconciliation nightly. The shape is right for this work because the task has a defined end. A conversational assistant that must answer at any hour would not fit this shape.
Retries and side effects
Retry behaviour means a run can execute more than once. The Job documentation does not guarantee exactly-once side effects, so the guarantee has to come from your application. The usual engineering approach is to give each unit of work a stable identifier, write results with an upsert keyed on that identifier rather than an append, and check for an existing receipt before any external action such as sending a message or submitting a payment. If the receipt exists, the retry records success and skips the action.
Orchestration patterns decide what each agent does next
Scheduling answers where a run lands. Orchestration answers which agent runs next and with what inputs. Microsoft’s guide to AI agent orchestration patterns describes sequential and concurrent patterns along with operational pitfalls, and Google Cloud’s guide to choosing a design pattern for agentic AI systems covers selection factors and multi-agent trade-offs. Microsoft Learn: AI Agent Orchestration Patterns and Google Cloud: Choose a design pattern for your agentic AI system
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|---|---|---|---|
| Sequential chain | Each stage depends on the previous output | Clear order and simple tracing | One slow stage delays everything; a mid-chain failure needs resumable state |
| Concurrent fan-out and fan-in | Subtasks are independent of one another | Parallel throughput | Merging results and concurrent writes to shared state |
| Dynamic routing | The next agent depends on content judged at runtime | Adapts to input | Harder to predict cost, paths and test coverage |
| Human-gated step | A judgment or approval must be made by a person | Keeps accountability with a person | The waiting state must be persisted, and pending approvals can stall a queue |
The “use when” and “main risk” columns are this article’s synthesis of the trade-offs those guides describe.
Rank #4
Sequential chains
A predetermined chain of specialist agents suits linear dependencies, such as extract, validate, then draft. Its weakness is that the slowest stage sets the pace, and a failure in the middle should resume from the last recorded stage rather than restart from the beginning.
Concurrent fan-out and fan-in
When subtasks are independent, dispatch them in parallel and merge the results afterwards. The merge step is where most of the complexity sits. If two concurrent agents update the same record, the design needs a single writer per record or versioned writes. Do not assume that mutable state changed by one agent is immediately visible to another.
Dynamic routing and human gates
When the next step depends on judgment, a model-directed route may be necessary. The cost is less predictability in paths, cost and tests. Where a person must approve an action, persist the pending state with enough context to resume later, so that a restart or a slow approver does not lose the run.
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Combining patterns
Real fleets usually mix these. A common arrangement is a sequential intake stage, a concurrent processing stage, and a human-gated publication stage. Choose the pattern per stage, not once for the whole system.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What grows as the fleet grows
Adding agents adds operational cost and coordination risk faster than most teams expect. The sources highlight several areas to watch:
- Monitoring per agent and per handoff. A failure often appears at a handoff rather than inside one agent, so trace the chain as a unit.
- Latency. Each sequential stage and each handoff adds delay, and the delays accumulate.
- Resource use and inference expense. Retries and extra agents multiply model calls, so cost tracks the number of attempts, not only the number of tasks.
- Consistency of shared state. Concurrent agents that touch the same mutable data need explicit consistency rules.
- Security. Each agent identity should hold only the permissions its stage needs.
- Evaluation. Output quality can drift as prompts, models and inputs change, so measure completion quality over time rather than only success or failure.
Design checklist before you scale out
- How queue priority behaves when one tenant or agent type floods the queue.
- What happens to in-flight runs when a deadline passes or a user cancels, and whether partial side effects need compensation.
- Whether intake slows, sheds load or keeps queuing when downstream model capacity is saturated.
- Which signal drives worker count: queue depth, queue age, or in-flight runs.
- Which steps wait for a human, and where the pending state is stored.
- Which dashboards show queue age, placement decisions, retry counts, latency, cost and completion quality for each agent type.
Where the analogy breaks, and version caveats
- A Pod is not an agent. An agent may be a request handler, an actor, a queue worker or a workflow state machine. One scheduling strategy will not fit every agent architecture.
- Kubernetes behaviour depends on version. Feature availability can vary by release and feature gate, so confirm the documentation for your cluster before copying manifests.
- Cloud runtime categories are vendor-specific. The Google Cloud taxonomy describes one platform’s shapes, not a universal comparison of products.
- Cloud product capabilities change. Confirm current limits and features on the vendor’s pages before designing around them.
For foundations behind the Job and work-queue patterns used here, the Microsoft-published book Designing Distributed Systems discusses Kubernetes Jobs and work queues. It is general distributed-systems material rather than guidance on AI agents. Microsoft / Azure: Designing Distributed Systems
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