What years of production-grade concurrency teaches us about building AI agents
georgeguimaraes.com
February 18, 2026
14 min read
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
MixedPositives
- 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.
Source
georgeguimaraes.com
Published
February 18, 2026
Reading Time
14 minutes
Relevance Score
53/100
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