Why Gleam Emerges as the Optimal Language for AI Coding Agents

The fundamental workflow of AI coding agents operates on a simple yet powerful loop: write code, check if it works, fix what's broken, and repeat. The efficiency of this feedback cycle determines the ultimate utility of these agents, and as developers at Curling IO discovered during their experience building Version 3 in Gleam, language choice profoundly impacts this process. Their findings suggest that Gleam's compiler-centric approach creates an environment where AI agents can self-correct with minimal human intervention, potentially establishing a new paradigm for evaluating programming languages in the era of AI-assisted development.

The Compiler as Immediate Feedback Mechanism

In dynamically typed languages, the "check if it works" step typically means running a test suite—a process that can take anywhere from 30 seconds to several minutes. This delay compounds during iterative development, where agents might compile dozens of times in a single session. The cumulative effect of these waiting periods significantly slows the development workflow.

Gleam fundamentally alters this equation by making compilation the primary verification mechanism. The Gleam compiler completes in a few seconds even for substantial projects, providing immediate feedback. When compilation fails, the error messages are remarkably specific: pinpointing the exact file, line number, problematic code, and expected correction. This granular guidance allows AI agents to understand precisely what needs fixing without human interpretation.

The value of this approach extends beyond mere speed. Test suites, by their nature, may not cover every code path, allowing certain errors to remain hidden until runtime or even production. In contrast, the compiler examines every line of code during compilation, catching issues before the code ever runs. This shift from runtime to compile-time error detection represents a fundamental improvement in the development workflow when working with AI agents.

Eliminating Entire Classes of Common Errors

Gleam's design intentionally eliminates several notorious error sources that plague dynamically typed languages, each representing significant advantages when working with AI coding assistants.

The Null Reference Problem

Perhaps most significantly, Gleam eliminates null references entirely. Instead, optional values are represented as Option(T), which must be explicitly handled as either Some(value) or None. This design choice prevents the infamous billion-dollar mistake of null references that has caused countless production failures, including the 2025 Google Cloud outage where a single null value cascaded through Service Control, taking down multiple GCP and Workspace products for hours.

In JavaScript or similar languages, AI agents must remember to check for null at every potential point of failure—a cognitive burden that often leads to oversight. Gleam removes this possibility entirely, with the compiler forcing developers to handle both cases. The security implications are equally important, as unhandled nulls can lead to authentication bypasses or data exposure through error pages that should never be reached.

Structural Change Management

When AI agents modify data structures, dynamically typed languages offer no immediate feedback about code that depends on those structures. For example, adding a new field to a type in JavaScript will not cause any failures until code that references the missing field executes. This delayed discovery means errors might only surface during testing, or worse, in production.

Gleam's compiler addresses this by failing to compile whenever code references a structure that has been changed. If an agent adds a field to a type, every function that constructs or destructures that type will fail to compile until updated. The compiler provides a comprehensive list of locations requiring modification, allowing the agent to methodically work through each one. This pattern extends to all structural changes: renaming fields, modifying types, or adding variants to union types.

Exhaustive Pattern Matching

Gleam's type system requires that pattern matches cover every possible case. When matching on a Result type, both Ok and Error must be handled. When matching on a custom union type with four variants, all four must be explicitly addressed. This requirement prevents the common scenario where AI agents write conditional logic that handles common cases but neglects edge cases, creating runtime errors waiting to happen.

Consider a payment status type with variants for Pending, Completed, Refunded, and Failed. If an agent later adds a Disputed variant, every case expression matching on PaymentStatus across the entire codebase will fail to compile until the new variant is handled. This comprehensive approach ensures structural consistency throughout the codebase.

Language Design for AI Generation

Several aspects of Gleam's design specifically address the challenges and capabilities of AI coding agents:

Simplified Syntax and Reduced Decision Space

Gleam is intentionally a small language with minimal syntax. There's essentially one way to define a function, one way to handle errors (Result types), and one way to handle optional values (Option types). This design matters significantly for AI agents because a smaller decision space reduces the likelihood of incorrect choices.

When multiple approaches exist for accomplishing the same task, AI agents must select one, potentially choosing non-idiomatic patterns or violating established conventions. Gleam's more constrained approach usually offers one clear way to accomplish each task, eliminating this ambiguity. The language itself has already made many design decisions, reducing the cognitive load on both human developers and AI assistants.

Canonical Formatting

Gleam's formatting tool, gleam format, produces canonical output with no configuration required. This consistency means that AI-generated code, after formatting, appears identical to hand-written code. The inability to distinguish between AI-generated and human-written code represents a significant advantage, as it eliminates the need for extensive style cleanup or reformatting.

In contrast, dynamically typed languages often involve extensive configuration for linters and formatters, with different projects potentially adopting different style guidelines. This variability creates additional complexity for AI agents, which must infer the appropriate style for each context.

Whitespace Insensitivity

While some developers prefer whitespace-significant languages like Elm or Haskell for their aesthetic qualities, AI agents frequently struggle with indentation. Common issues include mixing tabs and spaces or misaligning nesting levels, creating silent semantic errors that change code behavior without obvious indicators.

Gleam's use of curly braces makes whitespace irrelevant to the compiler. While gleam format normalizes whitespace for consistency, the language itself doesn't depend on indentation for structure. This design choice eliminates an entire class of formatting errors that frequently occur with LLM-generated code.

The Trade-offs and Practical Considerations

No approach is without trade-offs, and the Curling IO team acknowledges several challenges in adopting Gleam for AI-assisted development:

Training Data Limitations

Gleam, being a relatively young language, has less training data available compared to JavaScript, TypeScript, or Python. This limitation means that AI agents initially produce lower-quality Gleam code, reaching for patterns that don't exist or inventing functions not present in the standard library.

The team found that while agents write slower initial code in Gleam, the total time from "start writing" to "correct and deployed code" is actually shorter. The faster feedback loop compensates for the lower-quality initial output, with the compiler catching errors that might only surface during testing or production in dynamically typed languages.

Ecosystem Maturity

Gleam's ecosystem remains less mature than more established languages. Fewer libraries are available, and community resources are more limited. This immaturity means that AI-generated code may require more correction on the first pass, particularly for complex functionality that depends on specialized libraries.

However, this gap is gradually closing as Gleam adoption grows and training data becomes more abundant. The unique advantages for AI-assisted development may accelerate this trend as more teams recognize the benefits.

The Shifting Landscape of AI-Assisted Development

The experience building with Gleam and AI agents reveals broader implications for the software development industry:

Redefined Language Evaluation Criteria

As AI coding agents become more prevalent, the criteria for evaluating programming languages are shifting. The question "How fast can a human write this?" is being supplemented with "How much of the developer's review time does this require?" Since developers increasingly become the bottleneck in the development workflow, languages that reduce their review time gain significant advantages.

In Gleam, the compiler has already verified structural correctness before the code reaches a human reviewer. This verification allows developers to focus on higher-level concerns like logic, intent, and design patterns rather than chasing down missing null checks or type mismatches.

The Evolving Role of Developers

AI-assisted coding is already transforming the developer's role from primary implementer to supervisor and reviewer. This shift may lead to either more software being written with fewer developers, or a greater emphasis on the expertise required to effectively review AI-generated output.

The bar for code review has risen significantly. Every diff now requires scrutiny from domain experts, security specialists, and programming experts. The speed of code generation has increased the expertise required from the operator rather than decreasing it.

Security Implications

As AI-generated code increasingly reaches production systems, security vulnerabilities represent a growing concern. Agents are generally effective at catching obvious security issues like SQL injection, but they may miss more subtle problems that arise from the cumulative effect of multiple decisions made across different prompts.

For example, an agent might not realize that a decision made three prompts earlier means all customers' PII is now accessible on a public URL as a side effect. This type of vulnerability requires understanding the full system context, a capability that current AI agents lack.

Alternative Approaches and Industry Evolution

The industry is actively exploring different approaches to AI-assisted development, with notable experiments including:

• Mog: A language designed specifically to be written and read by AIs rather than humans. The Curling IO team expresses skepticism about this approach, arguing that if the developer reviewing diffs is the bottleneck, optimizing for machine readability at the expense of human readability makes the situation worse.

• Codespeak: Takes a different approach by treating specs and code as interchangeable representations that can be seamlessly transformed between each other. The team suggests that both specs and code serve important purposes at different levels of granularity, and collapsing them into a single representation loses the unique strengths of each.

Statically typed languages like Gleam have an inherent advantage in this evolution: types serve as self-documenting specifications that communicate the shape of data and function boundaries without requiring separate documentation. This built-in documentation bridges the gap between high-level intent and low-level implementation.

Conclusion: Language Design in the Age of AI

While Gleam wasn't specifically designed for AI coding agents, its design principles align remarkably well with the needs of AI-assisted development. The language demonstrates that good language design matters more than tools specifically built for AI agents, as the fundamental characteristics of the language shape the development workflow at its core.

After a year of building production software with AI coding agents, the evidence suggests that these tools can be effective when paired with appropriate verification processes. Languages like Gleam, which provide fast, specific feedback and eliminate entire classes of common errors, may offer significant advantages in this new paradigm of software development.

As AI coding capabilities continue to evolve, programming languages that prioritize tight feedback loops, clear error messages, and elimination of common error sources will likely become increasingly valuable. The developer's role may shift toward supervision and review, but human expertise will remain essential—particularly for ensuring that the systems we build are not just functional, but secure, maintainable, and aligned with human intent.

The experience with Gleam suggests that we're entering a new era where language design will be evaluated not just by human productivity, but by how well it enables effective collaboration between human developers and AI coding agents.