Agent Behavior Sandbox

A tiny sandbox to explore how an agent interprets tasks, applies rules, and changes behavior as signals drift.

This project is part of my Applied Intelligence Systems Series, exploring how intelligent systems behave beneath the UI layer — from signal ingestion to rule evaluation to action selection and feedback.

The goal of this sandbox is to provide a simple, interactive way to see how different variables affect an agent’s behavior:

• Task description
• Rules and constraints
• Context and input signals
• Noise / drift in the environment
• Execution trace and outcomes

The simulation is intentionally small and easy to extend.

---

Features (MVP)

The first version will include:

• Input fields for task, rules, and context
• A simple agent “reasoning” trace (steps the agent takes)
• Visual comparison of behavior under two different rule sets or contexts
• Basic drift controls (e.g., add noise, change a constraint, toggle a rule)
• Simple action flow: Task → Interpret → Decide → Act → Log

Demo Screenshot

---

Behavior Flow Diagram

    [Task + Context + Rules]
                |
                v
         Input Normalization
        (clean, validate, shape)
                |
                v
         Interpretation Layer
     (what does this task mean?)
                |
                v
        Policy & Rule Engine
     (check constraints, priorities)
                |
                v
          Decision Selection
      (choose next action or plan)
                |
                v
            Execution Step
      (apply action to environment)
                |
                v
           Behavior Log / Trace
    (what the agent did and why)

---

Purpose

Agent behavior often looks “mysterious,” but real systems depend on:

• Clear task definitions
• Transparent rules and constraints
• Stable, well-structured inputs
• Explicit traces of what the agent decided and why
• Awareness of how drift in signals changes outcomes

This sandbox provides a small, understandable way to visualize these concepts without building a full agent framework.

---

How This Maps to Real Intelligence Systems

Even though it's minimal, each part corresponds to real architecture:

Task & Context

In production systems, tasks arrive with partial context (user state, environment, permissions). Mis-specified tasks or missing context cause surprising behavior.

Rules & Constraints

Policies, guardrails, and business rules limit what an agent is allowed to do. Real engines implement this as policy checks, allow/deny lists, and safety filters.

Interpretation Layer

Before acting, intelligent systems interpret inputs: “what does this mean?”
This is where prompt parsing, schema mapping, or semantic understanding lives.

Decision Selection

Agents often have multiple possible actions. Selection might depend on priority, cost, risk, or external constraints.

Execution Trace

A good system exposes why and how decisions were made. Traces are critical for debugging, audits, and governance.

Drift & Noise

When inputs, rules, or context drift slightly, behavior can shift dramatically.
This sandbox makes that shift visible at a small scale.

This tool is a legible micro-version of how agent-like systems work under the hood.

---

Part of the Applied Intelligence Systems Series

Main repo:
https://github.com/rtfenter/Applied-Intelligence-Systems-Series

---

Status

MVP planned.
This sandbox will focus on core mechanics required to demonstrate agent behavior under different rules and contexts, not on building a full production agent framework.

---

Local Use

Everything will run client-side.

To run locally (once files are added):

1. Clone the repo
2. Open index.html in your browser

That’s it — static HTML + JS, no backend required.