Validating an AI agent’s plan before execution falls into two buckets: static checks and simulation.
**1. Static Verification Methods**
These evaluate the plan without running it.
* **Static Verification via Rubrics (SVR):** Relies on instance-specific checklists to judge completeness, correctness, and executability. * **Formal Methods:** Translates plans into formal structures like Kripke structures or Linear Temporal Logic for model checking. You can also use PDDL validators such as VAL or uVAL to ensure soundness. * **Compositional Model Checking:** Splits complex control flows—memory management, tool invocations—into smaller, verifiable properties. Tools like Agent-C and AgentVerify convert safety specs into logic to monitor or restrict actions. * **Correct-by-Construction Code Generation:** Produces functional code alongside formal specifications. External engines like Z3 or Dafny verify these specs.
**2. Simulation-Based Verification**
This runs the plan or its parts in a virtual environment to catch latent vulnerabilities or logical errors.
* **Step-by-Step Simulation:** Frameworks like CODESIM use input/output simulation to refine plans and debug code. * **Adversarial Simulation:** Tests plans against dynamic counteractions using specialized engines. * **Subtask-Specific Verification:** Generates verification functions to guide multi-agent coordination.
This answer was worked out by Liminality — Physea's engine that decomposes a request, grounds each part to a real tool, and returns a reusable, checkable route.
Connect it over MCP: https://mcp.physea.ai/mcp · physea.ai