Pel, A Programming Language for Orchestrating AI Agents

TL;DR

Pel is a new programming language designed to enable safe, expressive, and efficient orchestration of AI agents and LLMs, addressing limitations of existing control methods.

Contribution

Pel introduces a novel, Lisp-inspired language with syntax-level capability control, advanced features like piping, closures, natural language conditions, and automatic parallelization for LLM orchestration.

Findings

Pel provides a secure, expressive platform for complex AI agent control.

The language supports efficient parallel execution through static dependency analysis.

Pel enhances robustness and safety in LLM-based systems.

Abstract

The proliferation of Large Language Models (LLMs) has opened new frontiers in computing, yet controlling and orchestrating their capabilities beyond simple text generation remains a challenge. Current methods, such as function/tool calling and direct code generation, suffer from limitations in expressiveness, scalability, cost, security, and the ability to enforce fine-grained control. This paper introduces Pel, a novel programming language specifically designed to bridge this gap. Inspired by the strengths of Lisp, Elixir, Gleam, and Haskell, Pel provides a syntactically simple, homoiconic, and semantically rich platform for LLMs to express complex actions, control flow, and inter-agent communication safely and efficiently. Pel's design emphasizes a minimal, easily modifiable grammar suitable for constrained LLM generation, eliminating the need for complex sandboxing by enabling capability control at the syntax level. Key features include a powerful piping mechanism for linear composition, first-class closures enabling easy partial application and functional patterns, built-in support for natural language conditions evaluated by LLMs, and an advanced Read-Eval-Print-Loop (REPeL) with Common Lisp-style restarts and LLM-powered helper agents for automated error correction. Furthermore, Pel incorporates automatic parallelization of independent operations via static dependency analysis, crucial for performant agentic systems. We argue that Pel offers a more robust, secure, and expressive paradigm for LLM orchestration, paving the way for more sophisticated and reliable AI agentic frameworks.