Abstract

This document serves as a formal public disclosure of a system and method for autonomous software verification. It introduces the Agentic Simulation Pyramid, replacing static testing tiers (Unit, Integration, E2E) with a hierarchy of autonomous AI agents. The core innovation is the Coverage-to-Intent Feedback Loop, where real-time code coverage metrics are used by a Master Orchestrator to dynamically reconfigure agent behavior to explore unexecuted code paths.

The Core Concept

From Static Tests to Dynamic Simulations Traditional software testing relies on predefined scripts that check "known" scenarios. The Agentic Simulation Pyramid shifts the paradigm toward Exploratory Autonomous Verification. Instead of writing test cases, developers deploy specialized AI agents that "inhabit" the system's ecosystem at different levels of abstraction.

The Agentic Simulation Pyramid (Architecture)

L3: Strategic Layer (Stakeholder Agents) Role: Simulates human roles defined in the specification (e.g., "The Malicious Actor," "The Non-Technical Customer," "The Power User").

Mechanism: These agents ingest User Stories and Requirements in natural language. They do not follow scripts; they pursue goals (e.g., "Attempt to bypass the payment gateway" or "Complete a purchase with an expired coupon").

Outcome: Verification of high-level business logic and emergent UX flaws.

L2: Tactical Layer (Environmental Agents) Role: Simulates the system's external dependencies (API Mocks, Database Engines, Third-Party Microservices).

Mechanism: Unlike static mocks, these agents are "reactive." They can simulate network jitter, race conditions, or malformed downstream responses based on the state of the System Under Test (SUT).

Outcome: Dynamic resilience and integration testing in non-deterministic environments.

L1: Operational Layer (Atomic Agents) Role: Operates at the function and component level.

Mechanism: Agents analyze code signatures and semantic intent to perform "Intelligent Fuzzing." They generate edge-case inputs to trigger specific error handlers and boundary conditions.

Outcome: Maximum robustness of individual code units without manual unit-test authoring.

Conceptual & Model Simulation

While traditional testing starts after the code is written, this framework allows for verification at the architectural level.

Abstract Meta-Simulation and Logic-Level Validation: > This discovery encompasses Model-Level Simulation (Meta-Simulation), wherein AI agents represent abstract system components, architectural nodes, and business roles prior to the implementation of source code. In this modality, instrumentation and coverage metrics are applied to the Logical State-Space and Requirement Matrices defined in the system specification. This allows for the autonomous identification of business logic contradictions, architectural deadlocks, and conceptual gaps during the design phase. By simulating the system’s ‘Digital Twin’ at the conceptual level, the framework enables Semantic Fuzzing of Product Logic, ensuring that the foundational concept is verified through thousands of agentic interactions before a single line of executable code is written.

The Master Orchestrator: Coverage-Driven Exploration

The defining technical claim of this disclosure is the Closed-Loop Feedback Mechanism between code instrumentation and agent behavior:

Instrumentation: The SUT is run within an environment that tracks real-time Code Coverage (Line, Branch, and Path coverage).

Gap Identification: A "Master Orchestrator" agent monitors which code paths remain "cold" (unexecuted).

Dynamic Intent Injection: The Orchestrator identifies the logical conditions required to reach those cold paths and re-prompts/re-configures the lower-level agents to generate inputs that satisfy those conditions.

Autonomous Evolution: The simulation evolves its complexity until the target coverage threshold is met or a regression/crash is identified.

Technical Claims for Prior Art Purposes This disclosure claims the following as public domain and/or obvious evolutionary steps in the field of Software Engineering:

Claim A: The use of Multi-Agent Systems (MAS) to replace static Mocking/Stubbing in integration testing.

Claim B: The method of using LLM-based agents to simulate diverse user personas for automated Acceptance Testing.

Claim C: A feedback loop where Code Coverage metrics act as a fitness function or heuristic for an AI agent to generate new test vectors.

Claim D: The hierarchical orchestration of AI agents where higher-level "Business" agents delegate tasks to lower-level "Component" agents to maximize system-wide verification.

Conclusion

By publishing this framework, the industry moves from "Software Tested by Humans" to "Software Verified by Autonomous Simulations." This method ensures that the depth of verification scales linearly with the complexity of the AI models used, rather than the manual labor available in a QA department.