AgentOS Constraint DSL

Programmable Safety Layer for Autonomous AI Systems

Overview

The AgentOS Constraint DSL is a domain-specific language designed to define, enforce, and execute runtime safety policies for AI agents.

It acts as a guardrail layer between:

AI decision-making → real-world execution

Instead of hardcoding rules in Python, the DSL allows:

• Declarative safety rules
• Runtime validation
• Dynamic policy updates
• Compile-time optimization

Why Constraint DSL?

Traditional AI systems embed rules like this:

if risk_score > 0.7:
    raise Exception("Unsafe")

This approach is:

• Hardcoded
• Not scalable
• Not auditable
• Difficult to modify

DSL Approach

DENY action IF risk_score > 0.7
ALLOW action IF compliance_passed == true

Now rules are:

• Human-readable
• Version-controlled
• Runtime configurable
• Easily testable

Core Concepts

1. Constraint

A constraint defines whether an action is allowed.

ALLOW operation IF latency < 200
DENY operation IF risk_score > 0.8

2. Predicate

A condition evaluated at runtime.

risk_score > 0.7
user_role == "admin"

3. Action

What the system should do.

Action	Meaning
ALLOW	Proceed
DENY	Block execution
WARN	Log but allow

4. Context

Runtime data passed into evaluation.

{
  "risk_score": 0.65,
  "user_role": "user",
  "latency": 120
}

Architecture

DSL Input
   ↓
Lexer → Tokens
   ↓
Parser → AST
   ↓
Compiler → Executable Rules
   ↓
Runtime Engine → Evaluation
   ↓
Decision (ALLOW / DENY / WARN)

Implementation Guide

Project Structure

constraint_dsl/
├── lexer/
│   └── lexer.py
├── parser/
│   └── parser.py
├── ast/
│   └── nodes.py
├── compiler/
│   └── compiler.py
├── runtime/
│   └── engine.py
├── examples/
│   └── rules.dsl
└── tests/

Step-by-Step Implementation

Step 1: Lexer (Tokenization)

Convert DSL text → tokens

Example

DENY action IF risk_score > 0.7

Tokens

[
    ("DENY", "KEYWORD"),
    ("action", "IDENTIFIER"),
    ("IF", "KEYWORD"),
    ("risk_score", "IDENTIFIER"),
    (">", "OPERATOR"),
    ("0.7", "NUMBER")
]

Implementation

class Lexer:
    def tokenize(self, text):
        tokens = []
        words = text.split()

        for word in words:
            if word in ["ALLOW", "DENY", "IF"]:
                tokens.append((word, "KEYWORD"))
            elif word.replace('.', '', 1).isdigit():
                tokens.append((word, "NUMBER"))
            elif word in [">", "<", "=="]:
                tokens.append((word, "OPERATOR"))
            else:
                tokens.append((word, "IDENTIFIER"))

        return tokens

Step 2: Parser (Build AST)

Convert tokens → Abstract Syntax Tree

AST Example

ConstraintNode(
    action="DENY",
    condition=BinaryOp(
        left="risk_score",
        op=">",
        right=0.7
    )
)

Implementation

class Parser:
    def parse(self, tokens):
        return {
            "action": tokens[0][0],
            "field": tokens[3][0],
            "operator": tokens[4][0],
            "value": float(tokens[5][0])
        }

Step 3: Compiler

Convert AST → executable function

class Compiler:
    def compile(self, ast):
        def rule(context):
            if ast["operator"] == ">":
                return context[ast["field"]] > ast["value"]
        return rule

Step 4: Runtime Engine

Evaluate rules

class ConstraintEngine:
    def __init__(self, rules):
        self.rules = rules

    def evaluate(self, context):
        for rule in self.rules:
            if rule(context):
                return "DENY"
        return "ALLOW"

Full Example

DSL

DENY action IF risk_score > 0.7
ALLOW action IF user_role == "admin"

Python Execution

lexer = Lexer()
tokens = lexer.tokenize(dsl_text)

parser = Parser()
ast = parser.parse(tokens)

compiler = Compiler()
rule = compiler.compile(ast)

engine = ConstraintEngine([rule])

decision = engine.evaluate({
    "risk_score": 0.8,
    "user_role": "user"
})

print(decision)  # DENY

Integration with AgentOS

Where It Fits

Agent Runtime
   ↓
Constraint Engine (DSL Runtime)
   ↓
Strategy Execution

Integration Example

class AgentRuntime:
    def __init__(self, constraint_engine):
        self.constraint_engine = constraint_engine

    def run(self, input_data):
        decision = self.constraint_engine.evaluate(input_data)

        if decision == "DENY":
            raise Exception("Constraint violation")

        return "Execution continues"

Advanced Features (Production-Ready)

1. Multiple Rules Support

DENY IF risk_score > 0.7
DENY IF fraud_detected == true

2. Logical Operators

DENY IF risk_score > 0.7 AND user_role != "admin"

3. Rule Priority

PRIORITY 1 DENY IF risk_score > 0.9
PRIORITY 2 WARN IF risk_score > 0.7

4. Rule Versioning

Store rules like:

{
  "version": "v1.2",
  "rules": [...]
}

5. Hot Reloading

• Update rules without restarting system
• Fetch from DB / config service

Real-World Scenarios

Financial System

DENY IF transaction_amount > 100000 AND risk_score > 0.6

AI Chat Safety

DENY IF content_flagged == true
WARN IF toxicity_score > 0.5

Autonomous Systems

DENY IF obstacle_distance < 2

Testing Strategy

Unit Tests

def test_high_risk():
    assert engine.evaluate({"risk_score": 0.9}) == "DENY"

Integration Tests

• Test with real agent runtime
• Validate end-to-end behavior

Performance Considerations

Optimization Techniques

• Precompile rules
• Cache AST
• Use vectorized evaluation (future)

Future Roadmap

• Graph-based constraints
• ML-based adaptive constraints
• UI rule builder
• Policy-as-a-service (SaaS)

Final Insight

The Constraint DSL is not just a feature.

It is:

The safety kernel of AgentOS

It transforms AI systems from:

• Reactive → Proactive
• Unpredictable → Controlled
• Black-box → Auditable