What years of production-grade concurrency teaches us about building AI agents
georgeguimaraes.com
February 18, 2026
14 min read
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53/100
Summary
José Valim asserts that Elixir is the optimal programming language for AI, supported by a Tencent study showing it achieved the highest LLM code completion rate at 80.3%. The actor model from Erlang, introduced in 1986, is being rediscovered in AI development, paralleling patterns emerging in the Python AI ecosystem.
Key Takeaways
- Elixir achieved the highest LLM code completion rate among 20 programming languages, with Claude Opus 4 scoring 80.3% on Elixir problems.
- The BEAM virtual machine, designed for concurrency and long-lived connections, is well-suited for handling the demands of AI agents that require multiple round-trips and extended response times.
- BEAM processes are lightweight (approximately 2KB each), allowing for millions of concurrent processes with isolated memory, preemptive scheduling, and incremental garbage collection.
- Phoenix, the web framework for Elixir, is capable of managing over 100,000 concurrent WebSocket connections effectively.
Community Sentiment
Positives
- The BEAM's supervision tree model effectively manages long-lived, stateful conversations, which aligns well with the needs of AI agents in handling failures.
- The discussion highlights the potential for improved concurrency in AI applications through the use of Erlang processes, which can enhance performance in I/O bound workloads.
Concerns
- Agent frameworks appear bloated and may not provide clear advantages over existing models like Claude, leading to confusion among developers about their utility.
- Concerns were raised about the durability of execution in Elixir, indicating that it may not adequately address critical issues in agent frameworks without additional state management.
