Step 1: Wrap Your Agent in an API

Your agent code needs to be exposed as an HTTP endpoint so it can receive requests from anywhere. Use FastAPI (Python) or Express (Node.js).

from fastapi import FastAPI
from pydantic import BaseModel
from your_agent import run_agent

app = FastAPI()

class AgentRequest(BaseModel):
    query: str
    session_id: str = None

@app.post("/agent")
async def agent_endpoint(req: AgentRequest):
    result = await run_agent(req.query, session_id=req.session_id)
    return {"result": result}

Key decisions at this stage:

• Sync vs async: Agent runs can take 30–120 seconds. Consider async with polling or WebSockets for long-running tasks.
• Session handling: If your agent needs conversation history, you need a session store (Redis, PostgreSQL).
• Auth: Add API key or JWT authentication before going live.

Step 2: Containerize with Docker

Docker ensures your agent runs identically in dev, staging, and production. A minimal Dockerfile:

FROM python:3.11-slim
WORKDIR /app
COPY requirements.txt .
RUN pip install --no-cache-dir -r requirements.txt
COPY . .
EXPOSE 8000
CMD ["uvicorn", "main:app", "--host", "0.0.0.0", "--port", "8000"]

Pro tips:

• Pin dependency versions in requirements.txt — LLM client libraries change frequently.
• Use .dockerignore to exclude .env, __pycache__, and large data files.
• Keep secrets out of the image — pass via environment variables at runtime.

Step 3: Choose Your Hosting Platform

Your choice depends on traffic, budget, and technical complexity:

• Railway — Best for getting started fast. Push from GitHub, Railway handles everything. Free tier available. Our top pick for indie developers.
• Fly.io — Great for global edge deployment. CLI-first, Docker-native. Generous free allowance.
• Render — Simple PaaS, good for APIs. Auto-deploys from GitHub.
• AWS / GCP / Azure — Maximum control and scale. ECS, Cloud Run, or AKS for containerized agents. More DevOps overhead.
• Modal — Serverless Python with GPU support. Ideal if your agent needs GPU inference.

Step 4: Manage Secrets Properly

Your agent has API keys (OpenAI, Anthropic, etc.). Never hardcode these. Options:

• Railway / Render / Fly: use their built-in environment variable UI
• AWS: use Secrets Manager or Parameter Store
• Self-hosted: HashiCorp Vault or doppler

Rotate keys regularly. Set spending limits on your LLM API accounts. A runaway agent can burn through budget in minutes.

Step 5: Add Observability

You cannot debug a production agent without tracing. Set up LLM observability before your first real user hits the endpoint.

• LangSmith — If you're using LangChain/LangGraph, this is the default. Full trace visibility.
• Langfuse — Open-source alternative, self-hostable, framework-agnostic.
• Helicone — Drop-in OpenAI proxy with logging. Zero code change needed.

At minimum, log: request ID, input, output, model used, token count, latency, tool calls made, and any errors.

Step 6: Handle Failures Gracefully

LLM APIs fail. Rate limits hit. Tools time out. Your agent needs to handle this:

• Retry with backoff: Wrap LLM calls in exponential backoff (tenacity library in Python).
• Fallback models: If GPT-4o is unavailable, fall back to Claude or GPT-3.5.
• Timeout limits: Set a max execution time (e.g., 120 seconds). Kill and return an error if exceeded.
• Graceful degradation: If a tool fails, let the agent continue with a note that the tool was unavailable.

Step 7: Control Costs

Multi-step agents use tokens at every step. Without guardrails, costs spiral. Strategies:

• Use GPT-4o-mini or Claude Haiku for sub-tasks, GPT-4o for final synthesis only.
• Set a max_iterations cap on your agent loop (e.g., 10 steps max).
• Cache repeated LLM calls — if the same prompt is called twice, return cached result.
• Set hard spend limits in your OpenAI/Anthropic account dashboard.
• Monitor cost per request with LangSmith or Langfuse dashboards.

Quick Reference: Recommended Stack

• Framework: LangChain + LangGraph
• API server: FastAPI + uvicorn
• Container: Docker
• Hosting: Railway (easy) or Fly.io (global)
• Observability: LangSmith or Langfuse
• Vector DB: Pinecone (managed) or Qdrant (self-hosted)
• Secrets: Platform env vars + .env locally

All tools mentioned above are indexed in the AgDex directory.