Autonomy, a self-directed AI agent framework, launches with built-in safeguards—a system that can plan and execute tasks independently while logging every decision for audit.

What happened: Autonomy is a new open-source AI agent system that runs a task loop (AgentLoop) without human intervention. It selects relevant skills from a library of 13 built-in procedures, generates action candidates ranked by a 5-dimensional score (with beam width=3), executes them through a controlled gateway that checks risk levels, and learns from outcomes—all events are recorded in full for replay.

Why it matters: For organizations wanting to automate complex workflows, the framework's approval gates and risk-level checks mean autonomous execution doesn't mean unmonitored execution. Every action carries an expected effect, verification plan, and risk assessment (LOW / MEDIUM / HIGH), so teams can audit exactly what the agent did and why.

What to watch: The system terminates clearly on six end-states (ACHIEVED, BLOCKED, NO_CANDIDATES, APPROVAL_DENIED, MAX_STEPS_REACHED, FAILED) and supports interactive mode via terminal UI or batch runs. After each run, if goals succeed with ≥2 working steps, it auto-drafts new procedure skills at 0.85 confidence, and a background curator automatically merges duplicate or subset skills to prevent library bloat.