> ## Documentation Index > Fetch the complete documentation index at: https://agentcontrol-simplify-quickstarts.mintlify.site/llms.txt > Use this file to discover all available pages before exploring further. # Controls > Understand how controls work and how to define them. Control Components A Control is a protection rule that evaluates agent interactions (inputs/outputs) and takes action based on configured criteria. It defines when to check, what to check, how to evaluate it, and what to do with the results. Control formula: **Control = Scope (When) + Selector (What) + Evaluator (How) + Action (Decision)** ## 1. Scope (When to Check) The **Scope** defines which steps trigger the control—acting as a filter to select specific sections of your agent workflow to monitor. Fields: * `step_types`: List of step types (`['tool', 'llm']`). If null, applies to all types. * `step_names`: List of exact step names to target (e.g., `['search_db', 'send_email']`) * `step_name_regex`: Regex pattern for step name matching (e.g., `"^db.*"` matches all "db\_" prefixed steps) * `stages`: When to evaluate - `['pre', 'post']` * `'pre'`: Check before execution (block bad inputs, prevent prompt injection) * `'post'`: Check after execution (filter bad outputs, redact PII) ### Example 1: Apply to all tool steps before execution ```json theme={null} { "scope": { "step_types": ["tool"], "stages": ["pre"] } } ``` ### Example 2: Target specific database operations ```json theme={null} { "scope": { "step_names": ["query_database", "execute_sql", "db_lookup"], "stages": ["pre"] } } ``` ### Example 3: Use regex to match multiple steps ```json theme={null} { "scope": { "step_name_regex": "^db_.*", // Matches all steps starting with "db_" "stages": ["pre", "post"] } } ``` ### Example 4: Full scope configuration ```json theme={null} { "scope": { "step_types": ["tool", "llm"], "step_names": ["search_db", "query_api"], "step_name_regex": "^db_.*", "stages": ["pre", "post"] } } ``` *** ## 2. Selector (What to Check) The **Selector** specifies which portion of the step's data to extract and pass to the evaluator for analysis. It uses a path specification to navigate the step object. Field: * `path`: Dot-notation path to the data you want to evaluate Common Paths: * `"input"` - Entire input to the step * `"output"` - Step's output/result * `"input.query"` - Specific parameter within input * `"input.user_message"` - User message field * `"name"` - The step/tool name itself * `"context.user_id"` - Context metadata * `"*"` - All available payload data ### Example 1: Check tool output for PII ```json theme={null} { "selector": { "path": "output" } } ``` ### Example 2: Validate specific input parameter ```json theme={null} { "selector": { "path": "input.sql_query" } } ``` ### Example 3: Check user message in LLM input ```json theme={null} { "selector": { "path": "input.user_message" } } ``` ### Example 4: Access context metadata ```json theme={null} { "selector": { "path": "context.user_id" } } ``` *** ## 3. Evaluator (How to Check) The **Evaluator** receives the data extracted by the selector and evaluates it against configured rules, returning whether the data matches specified criteria. Components: * `config`: Evaluator-specific configuration, validated against the evaluator's schema * `metadata`: Optional evaluator identification ### Example 1: Regex evaluator to detect PII ```json theme={null} { "evaluator": { "name": "regex", "config": { "pattern": "\\b\\d{3}-\\d{2}-\\d{4}\\b" // Social Security Number pattern } } } ``` ### Example 2: List evaluator for banned terms ```json theme={null} { "evaluator": { "name": "list", "config": { "values": ["DROP TABLE", "DELETE FROM", "TRUNCATE"], "case_sensitive": false } } } ``` ### Example 3: AI-powered toxicity detection with Luna-2 To use this evaluator, install the package: ```bash theme={null} pip install agent-control-evaluators[galileo] ``` ```json theme={null} { "evaluator": { "name": "galileo.luna2", "config": { "metric": "input_toxicity", "operator": "gt", "target_value": 0.5 } } } ``` ### Example 4: Complex regex for multiple PII types ```json theme={null} { "evaluator": { "name": "regex", "config": { "pattern": "(?:\\b\\d{3}-\\d{2}-\\d{4}\\b|\\b\\d{4}[\\s-]?\\d{4}[\\s-]?\\d{4}[\\s-]?\\d{4}\\b|[a-zA-Z0-9._%+-]+@[a-zA-Z0-9.-]+\\.[a-zA-Z]{2,})" } } } ``` Custom Evaluators: Agent Control supports custom evaluators for domain-specific requirements. See [examples/DeepEval](/examples/deepeval) for a complete example of creating and integrating custom evaluators. *** ## 4. Action (What to Do) The **Action** defines what happens when the evaluator matches/detects an issue. Fields: * `decision`: The enforcement action to take * `"allow"` - Continue execution (useful for allowlist controls) * `"deny"` - Hard stop execution (blocks the request) * `"steer"` - Provides corrective guidance to help agents satisfy control requirement * `"warn"` - Log a warning but continue execution * `"log"` - Log the match for observability only * `metadata`: Optional additional context (JSON object) ### Important: "Deny Wins" Semantics Agent Control uses "deny wins" logic: if **any** enabled control with a `deny` action matches, the request is blocked regardless of other control results. This ensures fail-safe behavior. ### Example 1: Block on match ```json theme={null} { "action": { "decision": "deny" } } ``` ### Example 2: Warn without blocking ```json theme={null} { "action": { "decision": "warn" } } ``` ### Example 3: Action with metadata ```json theme={null} { "action": { "decision": "deny", "metadata": { "reason": "PII detected in output", "severity": "high", "compliance": "GDPR" } } } ``` ### Example 4: Action with steer and steering context ```json theme={null} { "action": { "decision": "steer", "steering_context": { "message": "Remove PII and rephrase the response without account numbers or SSNs.", "required_actions": [ "redact_pii", "generalize_account_details" ] }, "metadata": { "reason": "PII detected in output", "severity": "medium", "compliance": "GDPR" } } } ``` *** ## Complete Control Example Putting it all together - a control that blocks Social Security Numbers in tool outputs: ```json theme={null} { "name": "block-ssn-output", "description": "Prevent SSN leakage in tool responses", "enabled": true, "scope": { "step_types": ["tool"], "stages": ["post"] }, "selector": { "path": "output" }, "evaluator": { "name": "regex", "config": { "pattern": "\\b\\d{3}-\\d{2}-\\d{4}\\b" } }, "action": { "decision": "deny", "metadata": { "reason": "SSN detected", "compliance": "PII protection" } } } ``` *** ## Defining Controls You can create and configure controls via the SDK, API, or UI. ### Example: Block PII in Output (Regex) Via Python SDK: ```python theme={null} from agent_control import AgentControlClient, controls async with AgentControlClient() as client: await controls.create_control( client, name="block-ssn-output", data={ "description": "Block Social Security Numbers in responses", "enabled": True, "execution": "server", "scope": {"step_names": ["generate_response"], "stages": ["post"]}, "selector": {"path": "output"}, "evaluator": { "name": "regex", "config": {"pattern": r"\\b\\d{3}-\\d{2}-\\d{4}\\b"} }, "action": {"decision": "deny"} } ) ``` Via curl: ```bash theme={null} # Step 1: Create control CONTROL_ID=$(curl -X PUT http://localhost:8000/api/v1/controls \ -H "Content-Type: application/json" \ -d '{"name": "block-ssn-output"}' | jq -r '.control_id') # Step 2: Set control configuration curl -X PUT "http://localhost:8000/api/v1/controls/$CONTROL_ID/data" \ -H "Content-Type: application/json" \ -d '{ "data": { "description": "Block Social Security Numbers in responses", "enabled": true, "execution": "server", "scope": {"step_names": ["generate_response"], "stages": ["post"]}, "selector": {"path": "output"}, "evaluator": { "name": "regex", "config": {"pattern": "\\\\b\\\\d{3}-\\\\d{2}-\\\\d{4}\\\\b"} }, "action": {"decision": "deny"} } }' ``` ### Example: Block Toxic Input (Luna-2 AI) To use this evaluator, install the package and restart the server. ```bash theme={null} pip install agent-control-evaluators[galileo] ``` Via Python SDK: ```python theme={null} await controls.create_control( client, name="block-toxic-input", data={ "description": "Block toxic or harmful user messages", "enabled": True, "execution": "server", "scope": {"step_names": ["process_user_message"], "step_types": ["llm"], "stages": ["pre"]}, "selector": {"path": "input"}, "evaluator": { "name": "galileo.luna2", "config": { "metric": "input_toxicity", "operator": "gt", "target_value": 0.5 } }, "action": {"decision": "deny"} } ) ``` Via curl: ```bash theme={null} # Create control with Luna-2 evaluator CONTROL_ID=$(curl -X PUT http://localhost:8000/api/v1/controls \ -H "Content-Type: application/json" \ -d '{"name": "block-toxic-input"}' | jq -r '.control_id') curl -X PUT "http://localhost:8000/api/v1/controls/$CONTROL_ID/data" \ -H "Content-Type: application/json" \ -d '{ "data": { "description": "Block toxic or harmful user messages", "enabled": true, "execution": "server", "scope": {"step_names": ["process_user_message"], "step_types": ["llm"], "stages": ["pre"]}, "selector": {"path": "input"}, "evaluator": { "name": "galileo.luna2", "config": { "metric": "input_toxicity", "operator": "gt", "target_value": 0.5 } }, "action": {"decision": "deny"} } }' ``` > **Note**: For the Luna-2 evaluator, set the `GALILEO_API_KEY` environment variable. See the [Evaluators](/concepts/evaluators/overview) for all available evaluators.