2_OPERATE

Imported from ActiveInferenceInstitute/Active_InferAnts (2_OPERATE/AGENTS.md). Install upstream with npx skills add ActiveInferenceInstitute/Active_InferAnts --skill 2_OPERATE. Copyright stays with the author.

AGENTS.md — Runtime Execution

Agent guidance for the 2_OPERATE directory within the Active InferAnts framework.

Purpose

Runtime execution, simulation management, and cognitive utilities. This phase orchestrates the execution of Active Inference simulations and agent behaviors.

Directory Contents

Pipeline Modules

• 📄 plan_Simulation.py — SimulationPlanner class (alias SimulationSetup): simulation planning — environment setup, agent initialization, and parameter configuration (reads 1_PREPARE/configs)
• 📄 execute_Simulation.py — SimulationExecutor class: time-stepped simulation loop with visualization frequency and sleep duration controls
• 📄 render_Simulation.py — SimulationRenderer class: matplotlib-based animation, Entity rendering, and real-time visualization
• 📄 environment.py — Environment class: 2D grid world with pheromone field, food sources, and nests; build_environment_from_config helper
• 📄 MetaInformAnt_Simulation.py — MetaInformAntSimulation class: drives active_infer_ants agents through the grid environment (progress, aggregate results, visualization parameters)

Observability & Support

• 📄 data_logging.py — DataLogger class: per-step records and JSON result persistence
• 📄 performance_monitor.py — PerformanceMonitor class: runtime timing and parameter-adjustment checkpoints
• 📄 performance_metrics.py — PerformanceTracker class: scalar metrics and cross-run aggregates
• 📄 error_handling.py — SimulationError exception + handle_simulation_error re-raise helper
• 📄 exception_handling.py — SimulationExceptionHandler class: fail-closed error payloads
• 📄 report_generator.py — ReportGenerator class: plain-text simulation reports
• 📄 computational_resources.py — estimate_computational_resources advisory cost estimate
• 📄 visualization.py — SimulationVisualizer class: summary and comparative plots

Analysis

• 📄 cognitive_utilities.py — CognitiveUtilities class: agent behavioral analysis, cognitive load assessment, decision support, and multi-format export (JSON/CSV/Excel via pandas)
• 📄 situational_Antwareness.py — AgentVisualizer abstract base class + ConcreteAgentVisualizer: A/B/C/D/G/F matrix visualization, eigenvalue analysis, seaborn heatmaps, and agent state evolution tracking

Agent Instructions

• Maintain simulation execution pipeline: plan_Simulation.py → execute_Simulation.py → render_Simulation.py.
• The pipeline is wired to the active_infer_ants package for the agent-side API; keep the wiring and the integration suite (tests/test_simulation_pipeline.py) in sync.
• Ensure CognitiveUtilities export methods (JSON/CSV/Excel) produce valid output.
• Preserve the AgentVisualizer ABC hierarchy and ConcreteAgentVisualizer integration with the simulation context.
• The optional per-agent visualizer requires matrix attributes (A_matrix, B_matrix, …) that active_infer_ants agents do not carry; keep that call path guarded and non-fatal.
• Track pipeline inputs from 1_PREPARE agent definitions and outputs consumed by 3_MEASURE.
• Follow the repository's CC BY-NC-ND 4.0 license.
• Keep all documentation synchronized with actual contents.

Quality Checklist

• All files documented and up-to-date
• Pipeline flow (plan → execute → render) verified
• Integration suite covers the pipeline end-to-end
• README.md and SPEC.md synchronized with this AGENTS.md