Hosted Agents

Hosted Agents is a community skill for building and deploying autonomous AI agents on managed cloud infrastructure, removing the need for local compute resources, manual scaling operations, and direct infrastructure management.

What Is This?

Overview

Hosted Agents provides patterns for deploying AI agent workflows to cloud platforms with managed infrastructure. It covers provisioning, lifecycle management, scaling policies, monitoring dashboards, and alerting for agents that run persistently or on demand. The skill addresses infrastructure concerns so developers can focus on agent logic rather than server administration and capacity planning.

Who Should Use This

This skill serves teams deploying AI agents to production environments, platform engineers building agent hosting layers, and developers who need reliable uptime and availability without managing servers directly. It is relevant when agent workloads require elastic scaling or persistent availability beyond what local machines can provide.

Why Use It?

Problems It Solves

Running agents locally limits availability to a single machine and session. Manual deployment scripts break when scaling across multiple regions or handling failover between zones. Without health checks and automatic restart policies, crashed agents go unnoticed for extended periods. Resource contention between agents on shared infrastructure causes unpredictable latency and degraded performance.

Core Highlights

Declarative deployment configuration defines agent resources, scaling rules, and health checks in version-controlled files. Automatic restart and failover policies keep agents running through transient failures without manual intervention. Horizontal scaling adjusts agent replica count based on queue depth or incoming request volume. Centralized logging aggregates output from all distributed agent instances into a single observable stream.

How to Use It?

Basic Usage

apiVersion: agents/v1
kind: HostedAgent
metadata:
  name: research-agent
spec:
  image: agents/research:latest
  replicas: 2
  resources:
    cpu: 500m
    memory: 1Gi
  healthCheck:
    path: /health
    intervalSeconds: 30
  scaling:
    minReplicas: 1
    maxReplicas: 10
    targetQueueDepth: 5
  env:
    - name: MODEL_ENDPOINT
      valueFrom:
        secretRef: model-api-key

Real-World Examples

import httpx
import asyncio

class AgentManager:
    def __init__(self, api_url: str, token: str):
        self.client = httpx.AsyncClient(
            base_url=api_url,
            headers={"Authorization": f"Bearer {token}"}
        )

    async def deploy(self, config: dict) -> dict:
        resp = await self.client.post("/agents", json=config)
        resp.raise_for_status()
        return resp.json()

    async def scale(self, agent_id: str, replicas: int) -> dict:
        resp = await self.client.patch(
            f"/agents/{agent_id}/scale",
            json={"replicas": replicas}
        )
        return resp.json()

    async def check_health(self, agent_id: str) -> dict:
        resp = await self.client.get(f"/agents/{agent_id}/health")
        return resp.json()

    async def get_logs(self, agent_id: str, lines: int = 100) -> list:
        resp = await self.client.get(
            f"/agents/{agent_id}/logs", params={"lines": lines}
        )
        return resp.json()["entries"]

async def main():
    manager = AgentManager("https://platform.example.com", "token")
    deployment = await manager.deploy({
        "name": "support-agent",
        "image": "agents/support:v2",
        "replicas": 3
    })
    print(f"Deployed: {deployment['id']}")
    health = await manager.check_health(deployment["id"])
    print(f"Status: {health['status']}")

Advanced Tips

Use blue-green deployments to roll out agent updates without downtime. Implement circuit breakers around external API calls so one failing dependency does not cascade across the entire agent fleet. Store agent state in external databases rather than in-process memory to enable seamless replica replacement during scaling events.

When to Use It?

Use Cases

Deploy customer support agents that scale automatically with incoming ticket volume. Run data processing agents on schedules with automatic resource provisioning and teardown. Host multi-agent systems where coordinator and worker agents communicate through managed message queues with guaranteed delivery.

Related Topics

Container orchestration platforms, serverless function architectures, message queue systems, infrastructure as code tooling, and agent framework deployment patterns.

Important Notes

Requirements

A container registry for storing agent images, a hosting platform with autoscaling support such as Kubernetes or a managed container service, API credentials for the target cloud provider, and a CI/CD pipeline for automated image builds and deployments.

Usage Recommendations

Do: set resource limits per agent to prevent runaway costs during load spikes. Use health checks with appropriate intervals to detect failures quickly. Pin agent image versions in production deployments to ensure reproducible behavior.

Don't: deploy agents without monitoring and alerting configured for error rates. Allow unlimited scaling without cost guardrails or budget alerts. Store secrets in plain environment variables without encryption at rest.

Limitations

Cold start latency affects on-demand agents that scale from zero replicas. Network policies between agents running in different namespaces add configuration complexity. Cloud provider rate limits may throttle rapid scaling events during sudden traffic spikes. Debugging distributed agent failures requires correlation IDs and structured logging to trace issues across replicas.