Action Boundaries and Transparency

Overview

Action Boundaries describe what an agent can and cannot do on behalf of a user.
Transparency defines how clearly the agent communicates its reasoning, intent, and limitations back to that user.

Fastino provides a structured way to model these parameters as part of each user’s world model, so your agents stay contextually aligned without becoming opaque or unpredictable.

These settings can be derived from:

• Explicit rules set by the user or workspace.

• Learned behavioral boundaries based on past interactions.

• Dynamic reinforcement through Fastino’s ingestion and query APIs.

Core Principles

1. Bounded Autonomy — Agents act only within clearly defined limits (e.g., “schedule meetings but never send messages without review”).

2. Contextual Awareness — Boundaries adapt based on context, tools, and user behavior.

3. Explainability — Agents articulate why they acted or deferred an action.

4. User Override & Transparency — Users can view, audit, and adjust decision rules.

5. Continuous Learning — Fastino refines boundaries from user feedback and outcomes.

Defining Action Boundaries

You can define an agent’s operational limits through Fastino traits, events, or policy documents.

Example 1 — Declaring Explicit Rules

PUT /register
{
  "user_id": "usr_42af7c",
  "traits": {
    "name": "Ash Lewis",
    "timezone": "America/Los_Angeles",
    "preferences": {
      "boundaries": {
        "can_schedule": true,
        "can_send_emails": false,
        "can_reschedule_others": "requires_confirmation"
      }
    }
  }
}

This defines a permission schema directly inside the user profile.
Your agent can retrieve and apply these settings dynamically.

Example 2 — Fetching Operational Policy

Agents can retrieve a user’s action boundaries at runtime:

POST /query
{
  "user_id": "usr_42af7c",
  "question": "What actions am I allowed to perform automatically?"
}

Response

{
  "answer": "Ash allows automated scheduling and calendar updates but prefers manual confirmation before messages or file sends."
}

This allows your AI assistant to self-check before executing any external operation.

Adding Transparency to Agent Behavior

Transparency ensures that every autonomous or semi-autonomous action is traceable and explainable.
You can store reasoning traces, decision rationales, or action logs as documents in Fastino.

Example 3 — Logging Agent Reasoning

POST /ingest
{
  "user_id": "usr_42af7c",
  "source": "scheduler_agent",
  "documents": [
    {
      "doc_id": "log_20251027_01",
      "kind": "decision_record",
      "title": "Meeting Reschedule Attempt",
      "content": "The assistant moved a meeting from 11 AM to 1 PM due to user preference for afternoon scheduling."
    }
  ]
}

This enables traceability and auditing — key requirements for trust and compliance.

Combining Boundaries with Learning

Fastino’s personalization engine can infer and refine boundaries automatically from observed behavior.

For example:

• If users consistently override certain actions (e.g., “Don’t auto-send messages”), Fastino’s memory model will flag this as a learned preference.

• Subsequent /query responses will reflect this adaptation:

This allows for human-in-the-loop alignment, without rigid rules or retraining.

Applying in Agent Architectures

Example: Assistant Self-Auditing

1. The user says: “Schedule tomorrow’s sync.”

2. Agent checks boundaries with Fastino:

3. Fastino responds:

4. Agent proceeds to schedule and logs:

5. A transparency note is ingested:

This keeps both autonomy and auditability intact.

Integrating into Multi-Agent Systems

In multi-agent setups (e.g., calendar, finance, and email agents), Fastino ensures:

• Shared boundary awareness across all tools.

• Consistent permissioning for collaborative actions.

• Unified transparency logs for all actions.

Example integration in MCP:

Use Cases

Best Practices

• Model permissions as traits in /register for clarity.

• Query boundaries before executing high-impact actions.

• Store reasoning traces via /ingest for every external action.

• Inform users when boundaries are reached or actions are deferred.

• Use summaries (/summary?purpose=permissions) to give agents lightweight access to limits.

• Log overrides to refine behavioral adaptation over time.

Example: Query for Action Transparency Summary

GET /summary?user_id=usr_42af7c&purpose=permissions

Response

{
  "summary": "Ash’s assistant can schedule and update meetings but must confirm before messaging others or modifying shared documents."
}

This endpoint gives your agent a single deterministic snapshot of what’s allowed.

Summary

The Action Boundaries and Transparency framework ensures your AI agents are predictable, compliant, and trusted.
By using Fastino’s personalization endpoints to define, query, and log behavior limits, you give your agents clear operational rules — and your users full visibility into how and why actions are taken.

Next, continue to Personalization Use Cases → Routine Prediction to learn how Fastino predicts user behavior and daily patterns.