cas-agent-setup

Persistent memory for an autonomous agent is set up by initializing a structured STATE.json file to track goals, commitments, and beliefs. This standardized file-based persistence ensures coherent world-state tracking across multiple operational sessions.

An agent consciousness loop is a standardized operational cycle of prediction, observation, and learning driven by error correction. Telemetry logging works by appending predictions and observations to an archival log, automatically consolidating to prevent unbounded memory growth.

Bootstrap a new agentic system by running the initialization script to generate required memory structures and schemas in the agent directory. This establishes a self-correcting world model with append-only telemetry logging and acceptance tests for prediction calibration.

You can refactor an existing agent by applying the CAS specification schemas for prediction, observation, and learning records. This integration enforces structured world-state persistence and enables error-driven learning through standardized telemetry archival.

Validate online coupling and prediction calibration using an included suite of acceptance tests. This test harness verifies active sensing and operational reliability to ensure the agent maintains an auditable, self-correcting world model.

Agent memory grows unbounded without archival constraints during continuous operational loops. Consolidate telemetry logs using automated consolidation processes defined by the CAS specification, preventing excessive growth while maintaining append-only learning records.