ARC Agent Definition Language
Agent Definition Language (ADL) – Simplifying AI Agent Design
Why it matters
Traditional prompting for AI agents is complex, inconsistent, and brittle.
Each new LLM release or behavior change requires re-tuning prompts—a process that is time-consuming and hard to scale.
ADL (Agent Definition Language) solves this by introducing a structured, standardized, and model-agnostic way to define agent behavior.
Key Benefits
1. Business-Friendly
- Anyone not just technical experts—can define and manage agent behavior.
- Reduces reliance on prompt-engineering specialists and engineers.
- Business requirements are defined directly by business in the Agent ( highlight this ) especially useful in enterprises where complex reqs and overlaps impact time to market and need predivatlbit
2. Standardized & Scalable
- Encapsulates behavior into use cases with clear steps, solutions, and fallbacks.
- Provides consistency across models and across customer scenarios.
3. Resilient to Model Changes
- Decouples agent logic from raw prompts.
- When models evolve, ADL ensures minimal rework.
4. Extensible & Flexible
- Supports conditionals for personalization.
- Enables tool calls (@function()) for dynamic execution.
- Allows use case references to link complex workflows.
5. Operational Reliability
- Built-in fallbacks prevent failure loops.
- Structured validation of tools and references ensures robustness.
Business Impact
- Faster time-to-market for new agent behaviors.
- Lower cost of maintenance as agents evolve.
- Greater adoption since business teams can contribute directly.
- Future-proofing against LLM drift and vendor lock-in.
👉 Think of ADL as the “agent behavior standard”—the same way SQL standardized database queries, ADL standardizes how we define intelligent agents.