Tyler-R-Kendrick ADL — Agent Definition Language
Overview
ADL is a vendor-neutral, declarative specification for defining AI agents — their identity, capabilities, tools, permissions, and governance metadata. It acts as a portable blueprint (like OpenAPI for APIs) that is independent of any runtime, framework, or vendor. Open-sourced by Next Moca under Apache 2.0, also adopted by Eclipse LMOS.
Purpose
ADL defines what an agent is and what it can do, not how it runs. It complements:
- MCP — how agents call tools at runtime
- A2A — how agents communicate
- Agent Skills — how capabilities are packaged
Example
adl: "1.0"
agent:
name: research-assistant
version: "1.0.0"
description: "Researches topics and produces structured summaries"
author: "team-name"
license: MIT
llm:
provider: anthropic
model: claude-sonnet-4-5-20250929
temperature: 0.3
max_tokens: 4096
system_prompt: |
You are a research assistant. Produce well-structured,
factual summaries with cited sources.
tools:
- name: web-search
type: mcp
server: "search-server"
description: "Search the web for information"
- name: read-document
type: mcp
server: "doc-server"
description: "Read and parse documents"
rag:
- name: knowledge-base
source: "vector-store://company-docs"
description: "Internal documentation and policies"
permissions:
allowed_tools:
- web-search
- read-document
denied_actions:
- file_write
- code_execution
boundaries:
max_tokens_per_request: 8192
max_requests_per_minute: 30
dependencies:
- name: fact-checker
type: agent
description: "Validates factual claims before including them"
governance:
owner: "research-team@company.com"
review_status: approved
last_reviewed: "2026-01-15"
tags:
- research
- internal
Schema Sections
Agent Identity
| Field | Description |
|---|---|
name |
Unique agent identifier |
version |
Semantic version |
description |
What the agent does |
author |
Creator or team |
license |
SPDX identifier |
LLM Configuration
| Field | Description |
|---|---|
provider |
Model provider (anthropic, openai, etc.) |
model |
Specific model ID |
temperature |
Sampling temperature |
max_tokens |
Maximum output tokens |
system_prompt |
System-level instructions |
Tools
Declares which tools the agent can use, with type and connection info.
RAG
Declares knowledge sources the agent can access for retrieval-augmented generation.
Permissions & Boundaries
Defines what the agent is allowed and forbidden to do, plus rate limits and resource caps.
Dependencies
Other agents or services this agent relies on.
Governance
Ownership, review status, and audit metadata for enterprise compliance.
ADL vs Other Formats
| Aspect | ADL | AGENTS.md | Agent Skills |
|---|---|---|---|
| Focus | Agent definition (identity + config) | Project-level coding guidance | Reusable capability packaging |
| Format | Structured YAML schema | Freeform Markdown | YAML frontmatter + Markdown |
| Scope | Per-agent blueprint | Per-project instructions | Per-skill instructions |
| Runtime | Framework-agnostic | N/A | Platform-agnostic |
Best Practices
- Define permissions explicitly — principle of least privilege for tools and actions.
- Use semantic versioning so dependent systems can track agent changes.
- Include governance metadata (owner, review status) for enterprise audit trails.
- Keep system prompts in ADL rather than hardcoded in application code for portability.
- Declare dependencies on other agents explicitly so the orchestration layer knows the graph.
- Use ADL alongside MCP (tool runtime) and A2A (communication) — ADL defines the agent, the other protocols run it.