Chris Romp examines the limitations of AI coding agents like GitHub Copilot with DSLs, outlining why these tools struggle and offering actionable mitigation strategies informed by research and Microsoft’s extensibility tools such as MCP and Azure Bicep.

AI Coding Agents and Domain-Specific Languages: Challenges and Practical Mitigation Strategies

AI coding agents like GitHub Copilot are now embedded in modern software engineering workflows, offering context-aware suggestions and code completions trained on large datasets of public code. However, when it comes to Domain-Specific Languages (DSLs)—which are often underrepresented in public code and have unique syntax and semantics—these agents face major limitations.

Challenges of AI Agents with DSLs

Minimal Training Exposure

  • Little or no DSL content exists in model training datasets.
  • Copilot lacks knowledge of syntax, idioms, or API usage for unfamiliar DSLs, causing it to “guess” and often generate incorrect or fabricated code.

DSL Syntax Divergence

  • DSLs may have non-C-like control structures, custom operators, declarative semantics, or bespoke type systems.
  • These violate mainstream language assumptions, meaning Copilot and similar tools lack the necessary rules for reliable code generation.

Confabulated Semantics

  • LLMs fabricate APIs or conflate DSL syntax with similar mainstream languages.
  • Particularly problematic in abstract or fast-evolving domains like game scripting or infrastructure orchestration.

Missing Schema, Types, or Tooling Signals

  • General-purpose languages benefit from rich tooling (LSP, compiler errors, IntelliSense), which most DSLs lack.
  • The absence of structured domain data prevents Copilot from making grounded predictions or corrections.

Practical Mitigation Strategies

The solution is to supply the AI with domain knowledge and structure. Key strategies include:

1. Explicit Domain Context

  • Onboard Copilot by translating familiar code into DSL equivalents with explicit syntax and usage guidance.
  • Provide syntax rules, conventions, forbidden patterns, and canonical examples through Custom Agents or persistent configuration files.

GitHub Copilot Custom Agents

  • Use custom personas with DSL-aware context, including grammar overviews and correct/incorrect code examples.

Repository-level Instructions (copilot-instructions.md)

  • Include clear rules and patterns for the DSL within your repository’s instruction file.

2. Seed the Workspace with High-Quality DSL Examples

  • Open 3–5 well-commented reference files in the workspace to anchor Copilot’s suggestions.
  • Use Microsoft’s DSL-Copilot project as a reference structure.

3. Compiler or Validator in the Loop

  • Generate code, run it through the DSL compiler/linter, then feed errors back to Copilot for correction.
  • Iterative process continues until valid, useful code is produced.

4. Inject Domain Schema (Azure Bicep Example)

  • Expose DSL schemas or type systems to the AI using tools like Azure’s Model Context Protocol (MCP) server and VS Code extensions.
  • Anchoring AI completions with formal schemas dramatically reduces hallucination and increases accuracy.

Approximations for DSLs Lacking Formal Schema

  • Hand-crafted type sheets, valid function lists, dataflow diagrams, and forbidden operators can supply missing grounding.

Conclusion

AI coding agents can be highly effective for DSLs if supplied with explicit context, canonical examples, structured instructions, and validation mechanisms. Microsoft’s tooling—such as Azure Bicep, MCP, and repository-level customization—demonstrates best practices for raising agent accuracy. The main takeaway: AI code tools do not inherently understand new or obscure DSLs, but with domain rules, patterns, and feedback loops, they can become effective development partners.

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