Why Elixir is Perfect for Building AI Agents at Scale

AI agents - tools that can plan, decide, and act on behalf of users - are gaining popularity fast. They’re being used to automate tasks like coding, customer support, data analysis, and more. But while most AI agent tools are built in Python, we believe Elixir offers unique advantages -especially when it comes to running things reliably and at scale.

In this article, we’ll explore what AI agents are, how they’re usually built, and why Elixir (yes, Elixir!) is worth considering for orchestrating intelligent systems.

What Are AI Agents?

An AI agent is more than just a chatbot. It’s a system powered by large language models (LLMs), like GPT or Claude, that can:

• Understand a goal
• Break it into smaller steps
• Use tools or APIs to gather information
• Decide what to do next
• Repeat until the task is complete

For example, if you ask an agent, “Find top Elixir developers in Berlin and send them a LinkedIn message,” it might:

1. Search the web or use an API to find names
2. Write a draft message
3. Decide if it needs approval
4. Send the message via LinkedIn (if integrated)

The agent doesn’t just respond to a prompt - it acts. That’s a big shift from traditional AI interactions.

A helpful visual comes from this IBM article on AI agents, which describes them as LLMs + memory + tool access, running in loops until the job is done.

How Are AI Agents Usually Built?

Most AI agents today are built using Python frameworks like:

• LangChain: for chaining prompts, tools, and memory into complex workflows
• AutoGen (by Microsoft): for building multi-agent teams that collaborate
• CrewAI: for assigning roles to agents and coordinating them

These tools make it easy to build agent behavior without reinventing everything from scratch. They support:

• Connecting to LLMs (OpenAI, Anthropic, etc.)
• Creating and managing agent memory
• Using external tools (like search, math, APIs)
• Logging and debugging agent steps

But they also have some downsides: lots of abstraction, slower performance at scale, and less transparency when things go wrong.

Elixir’s Strengths for Agent-Oriented Systems

Elixir isn’t the first language people think of for AI - but it should be on your radar if you’re building agent orchestration, especially at scale.

Here’s why:

1. Massive Concurrency

Elixir runs on the Erlang VM (BEAM), which was built for telecom systems that handle thousands of calls at once. In Elixir, you can spawn hundreds of thousands of lightweight processes - perfect for managing many agents, tools, or subtasks in parallel.

2. Fault Tolerance

If something crashes, it won’t take your whole system down. Elixir uses supervisors to restart failed processes automatically. This is critical when you’re coordinating a fleet of AI agents that depend on external APIs.

3. Real-Time Features

Elixir’s Phoenix LiveView makes it easy to build apps that show agent progress live. Want to stream each step of what your AI agent is doing? No problem.

4. Great Observability

Elixir has built-in logging and telemetry. That means you can easily track what each agent is doing, how long each step takes, and why something failed.

Elixir experts, i.e. Saša Jurić notes in his talk “The Soul of Erlang and Elixir”, this model is designed for building reliable systems that stay up - something every AI product needs.

What About AI Libraries in Elixir?

While Elixir doesn’t have as many off-the-shelf agent libraries as Python, things are moving fast:

• LangChain Elixir: a lightweight LLM integration layer by Fly.io’s Mark Ericksen
• GenAI: a general-purpose LLM client with support for OpenAI, Mistral, Claude, and Gemini
• InstructorLite (Elixir): structured response parsing with streaming output - great for agents that need to extract data
• Jido and SwarmEx: experiments in agent state and orchestration

These tools show that the Elixir AI community is starting to explore serious AI use cases - not just calling models, but orchestrating real workflows.

For example, developers are using Elixir development processes to coordinate long-running agent tasks, store intermediate results, and retry steps if they fail - all while maintaining a live user interface with LiveView.

Connecting Elixir to Python-Based Agents

Many teams use Python frameworks for the “agent brain” and Elixir for the system around it.

You can connect them easily using:

• Ports (Elixir runs Python scripts and talks via stdin/stdout)
• gRPC (this guide explains how to use gRPC in Elixir)
• REST APIs (Elixir talks to FastAPI or Flask backends)

That way, you get the best of both worlds: Python’s AI tooling and Elixir’s robustness and real-time features.

This approach is already used in production systems where Python handles model calls, but Elixir manages workflows, queues, and UI.

Real-World Use Cases

Some examples where hire Elixir developers makes sense for agent-based apps:

• Customer support tools: Elixir can manage user sessions and stream agent responses
• Coding assistants: LiveView lets you show each step as the agent writes or explains code
• Task runners: Supervisor trees help retry failed steps automatically
• Multi-agent simulations: Run hundreds of agents in parallel without blowing up your server

As the field evolves, we expect more libraries, patterns, and hosted platforms to support agent orchestration in Elixir.

Conclusion

Python may lead the way in building LLM tools - but Elixir is quietly becoming a powerful backend for running and managing AI agents at scale. If you’re a CTO or product leader thinking about building AI-driven services, don’t overlook Elixir’s unique strengths: concurrency, fault-tolerance, observability, real-time UX. As more teams move from “cool AI demos” to real products, Elixir software house could be a 100% fit that keeps it all running smoothly behind the scenes.