AI agents are incredible. They can write code, summarize docs, answer support tickets, process customer data—you name it.

But getting them into production? That’s where things get messy.

Too often, you're stuck duct-taping scripts to cron jobs, managing cloud functions, handling flaky retries, and praying it all works in production.

WaveAssist gives you a better way.

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🧠 Build Your Agent, We’ll Run It

With WaveAssist, you can build lightweight AI agents in Python and let us handle:

• Scheduling
• Webhook triggers
• Secure secret management
• Logging & error tracking
• Retry logic and scalability

Whether your agent connects to OpenAI, Claude, HuggingFace, or a custom model—WaveAssist runs it cleanly and reliably, on your terms.

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🚀 Example: A Daily Support Bot

Let’s say you built an LLM agent that summarizes the day’s customer support tickets from Intercom and drafts follow-up responses.

Here’s what that looks like in WaveAssist:

# waveassist node
def summarize_tickets():
    tickets = fetch_from_intercom()
    summary = llm_agent(tickets)
    send_to_slack(summary)

Schedule it for 5pm daily. We’ll run it in a secure, scalable container, manage your secrets, and surface logs if anything fails.

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⚡ From One-Off to Always-On

LLM tools are evolving fast — but they’re only useful if they run when and how you need them.
WaveAssist bridges the gap between prototypes and production by making automation infrastructure effortless.

No devops. No cloud setup. Just your code, running where and when it should.

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🔗 Chain, Trigger, and Orchestrate

You can also chain agents together as multi-step workflows. Example:

1. 🧲 Scrape product reviews
2. 🧠 Summarize with LLM
3. 📤 Send to Airtable + Slack

Each step becomes a node.
WaveAssist handles the flow, dependencies, and retries — so you don’t have to.

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🧪 Try It in Minutes

You can build and run your first AI agent on WaveAssist in under 10 minutes.

👉 Get started now
📘 Read the docs

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The future is full of smart agents.
WaveAssist helps you run them — autonomously, reliably, and at scale.