Why Elixir is the Best Runtime for Building Agentic Workflows

A Problem Shared is a Problem Halved

As the article title suggests, the solution to the outlined problem is orchestrating standalone AI agents into a system, in other words:

Alright, but what are those? As Anthropic notes in their research on building effective agents:

"We categorize all these variations as agentic systems, but draw an important architectural distinction between workflows and agents:

• Workflows are systems where LLMs and tools are orchestrated through predefined code paths.
• Agents, on the other hand, are systems where LLMs dynamically direct their own processes and tool usage, maintaining control over how they accomplish tasks."

Anthropic

These agentic systems enable us to break down complex problems into specialized components, where each agent focuses on its area of expertise while collaborating within a larger system. Rather than forcing a single AI to be a jack-of-all-trades (and master of none), we can create specialized experts that work together, much like how human development teams operate. 

For these multi-agent systems to be truly effective in production environments, they must fulfill several critical requirements:

These requirements represent significant engineering challenges when implemented from scratch in most programming environments, which brings us to a critical question: 

If agentic workflows are the solution, what's the best runtime environment to build and deploy them? 

As we'll demonstrate throughout this article, Elixir's design philosophy and technical architecture make it uniquely suited for implementing robust, scalable agentic workflows.

The Match Made in Heaven: Actor Model and Agents

At the heart of Elixir lies the Actor Model, a conceptual framework for concurrent computation that seems almost prescient in its design. In this model, "actors" are isolated, independently functioning entities that:

Process messages from their mailbox

Make local decisions based on received messages

Send messages to other actors

Create new actors or modify their behavior for future messages

Sound familiar? This description could just as easily apply to AI agents in a workflow system. The Actor Model provides a battle-tested theoretical foundation for precisely the kind of work we're trying to do with agentic AI systems.

Requirement 1: Parallel Processing with Lightweight Processes

Where Elixir truly shines is in its implementation of the Actor Model through processes. Unlike operating system processes, which are resource-intensive, Elixir processes are incredibly lightweight, measured in kilobytes rather than megabytes. A single server can comfortably run thousands or even millions of them simultaneously.

This means each AI agent in your workflow can run in its dedicated process, working in parallel with perfect isolation. The BEAM virtual machine (Erlang's runtime, which powers Elixir) automatically distributes these processes across all available CPU cores, providing true parallelism without complex thread management code.

Requirement 2: Communication via Message Passing

Effective communication between agents is essential to any workflow system. Elixir's built-in message-passing system provides a natural mechanism for agents to share information and coordinate their activities.

This message-passing paradigm maps perfectly to Anthropic's workflow patterns:

Prompt chaining: Each step in the chain can be its own process, passing messages forward

Routing: Classifier processes can receive inputs and route to specialized handler processes

Parallelization: Multiple agent processes run concurrently by default

Orchestrator-workers: An orchestrator process can spawn and manage worker processes

Evaluator-optimizer: Evaluator and optimizer processes can exchange messages in a loop

Requirement 3: Fault Tolerance with Supervisor Trees

Elixir's approach to fault tolerance is encapsulated in its famous philosophy: "Let it crash." Rather than writing complex error-handling code everywhere, Elixir applications are structured in supervision trees that automatically restart processes when they fail.

This means that when an AI agent encounters an unexpected issue, whether from bad inputs, API failures, or other edge cases, it can simply crash and be automatically restarted by its supervisor without affecting the rest of the system.

For agentic workflows, this translates to extraordinary resilience. If your code generation agent crashes, the review agent and testing agent can continue working on their tasks, and the system will automatically respawn the failed agent to handle new requests.

Requirement 4: Distribution with Erlang Clusters

The final requirement—distributed deployment—is where the Erlang ecosystem, upon which Elixir is built, truly shines. Erlang was designed for telephone switches that required "nine nines" reliability (99.9999999% uptime) across distributed systems.

Elixir inherits this distribution capability through Erlang's cluster features. The remarkable thing about Erlang distribution is that the same message-passing paradigm works identically whether processes are on the same machine or different machines. For agentic workflows, this means you can start with a single machine during development and then seamlessly scale to a cluster when needed, without changing your core logic. Agents can be distributed across machines based on resource requirements, availability needs, or geographical considerations.

And the Cherry on Top

One often overlooked requirement for effective AI agents is comprehensive context. Agents need clear, structured information about the functions they can call, the data structures they're working with, and the expected behavior of system components.

Elixir treats documentation as a first-class citizen, making it an ideal environment for providing AI agents with the context they need. This documentation approach offers several benefits for agentic workflows:

When an AI agent needs to interact with a module, it can first read the documentation to understand the module's purpose and capabilities before attempting to use it. This significantly reduces trial-and-error approaches that plague many AI systems.

Optional Static Type Checking with Dialyzer

While Elixir is dynamically typed by default, it supports optional static type checking through Dialyzer. This provides an additional layer of safety when AI agents generate or modify code. Dialyzer can catch type mismatches and logic errors before runtime, which is particularly valuable when dealing with AI-generated code that might contain subtle errors. AI agents can also utilize these type specifications more effectively to understand the expected data structures and return values.

The combination of rich documentation and optional type checking creates an environment where AI agents have clear guidelines on how to interact with the system, thereby reducing errors and enhancing the quality of their contributions to the workflow.

Final Words

The road from concept to production-ready agentic systems involves multiple considerations:

Designing specialized agents with clear, focused responsibilities

Establishing communication protocols between agents

Setting up appropriate supervision trees for fault tolerance

Planning for distribution as systems scale

Creating comprehensive documentation for the AI context

As mentioned in our initial notes, Elixir's Actor Model provides a "cozy slot" where we can insert LLM-powered agents. The system was designed for deterministic actors following programmed logic, but it works just as well, perhaps even better, for non-deterministic AI agents.

While this article has outlined the fundamental advantages of Elixir for agentic workflows, implementing these systems requires both Elixir expertise and AI engineering knowledge. Our team of specialists, with deep experience in both domains, has helped numerous organizations design, build, and deploy production-grade agentic systems.

Ready to move beyond the 70% ceiling? Contact our experts to discuss how Elixir-based agentic workflows can transform your AI implementation strategy.