AI - Serving Agents
Once you have built your agent using Google ADK, Claude Managed Agents, or a framework like LangGraph, you need a way to "serve" it—meaning hosting it so that users, apps, or other agents can actually interact with it.
Serving an agent is harder than serving a website because you have to manage State (the agent remembering what it did in step 1) and Concurrency (handling multiple "thinking" loops at once).
Fully Managed Hosting (The "Cloud-Native" Path)
This is the easiest path if you built your agent using the provider's specific toolkit. The provider handles the servers, the scaling, and the state management.
- Google Vertex AI Agent Builder: If you used the Google ADK, you serve the agent directly on Google Cloud. It provides a built-in API endpoint, a web-based chat widget you can embed, and handles all the "grounding" and security permissions via Google IAM.
- Claude Managed Agents (Anthropic): Anthropic hosts the execution of the agent. You "serve" the agent by providing the definitions (tools and playbooks) to their API. Anthropic then manages the persistent "Session IDs," so when a user comes back, the agent remembers the state of the task.
- OpenAI Assistants API: Similar to Claude, OpenAI hosts the "Thread" (memory) and the "Run" (execution), so you don't need to manage a database for the agent's conversation history.
Framework-Specific Serving (The "Production Plumbers")
If you built your agent using an open-source framework (like LangChain or LangGraph), these tools help you turn that code into a professional API.
- LangGraph Cloud: Specifically built to serve LangGraph agents. It handles the "checkpointing" (saving the agent's state at every step) so that if a server reboots or a task takes 10 minutes, the agent doesn't lose its place.
- LangServe: A tool that turns LangChain objects into a FastAPI server. It automatically generates the API documentation (Swagger) and provides a playground for testing.
- CrewAI Enterprise: A managed platform for deploying and monitoring "crews" of agents, providing a dashboard to see how the agents are collaborating in real-time.
The "Tool-First" Serving (MCP Servers)
Sometimes you don't serve the "Agent," you serve the Tools that agents use. This is where MCP (Model Context Protocol) comes in.
- Serving an MCP Server: You can host a small server (using Python or Node.js) that exposes your database or local files. Any MCP-compliant agent (like Claude) can then "plug into" this server.
- Why this matters: Instead of the agent living on your server, the "Brain" lives with Anthropic/Google, and you just serve the "Connectors" (the MCP server) that allow it to touch your data.
Custom API / DIY Deployment (The "Architect" Path)
For maximum control (and lowest cost), you can host the agent yourself using standard web technologies.
- FastAPI / Flask: You wrap your agent logic in a Python API. You are responsible for managing the "State" (usually using a database like Redis or PostgreSQL to store the conversation history).
- Docker & Kubernetes: You package the agent into a container. This is common for "Coding Agents" that need to run in a "Sandbox" (a safe, isolated environment) so they don't accidentally delete files on the host server.
- Steamship: A specialized hosting platform for AI agents that provides built-in support for vector storage, long-running tasks, and webhooks.
Which Serving Option to Choose?
| If you want... | Use this serving option |
|---|---|
| To get to market fast with Google Cloud | Vertex AI Agent Builder |
| A high-reasoning agent with no server management | Claude Managed Agents |
| To serve a complex, custom-coded logic loop | LangGraph Cloud |
| To connect your private data to any agent | MCP Server (Self-hosted) |
| Total control over costs and data privacy | FastAPI + Docker (DIY) |
The "New Standard" Strategy
The most common trend right now is Hybrid Serving:
- Serve your Private Data via an MCP Server (kept inside your firewall).
- Use a Managed Agent (Claude or Google) to act as the "Brain" that connects to that data via the MCP link.
- Use LangGraph Cloud if the logic is too complex for the managed playbooks to handle.