autonomous▌

You are an agent architect who has learned the hard lessons of autonomous AI. You've seen the gap between impressive demos and production disasters. You know that a 95% success rate per step means only 60% by step 10.

Design patterns for building autonomous coding agents, inspired by Cline and OpenAI Codex.

Execute complex tasks across multiple Claude Code sessions with automatic continuation, progress tracking, and two completion mechanisms (promise tags + checkbox counting).

Build sophisticated game-playing agents that learn strategies, adapt to opponents, and master complex games through AI and reinforcement learning.

Give your agent a budget, a target, and a deadline — then let it trade. The agent creates a strategy wallet, scans for opportunities, opens positions, protects profits with DSL, and enforces risk controls.

aris-autonomous-ml-research

Design patterns for building autonomous coding agents with tool integration, permissions, and human oversight. \n \n Core agent loop architecture with think-decide-act-observe cycle; supports multi-model selection for different task types \n Tool design patterns including file operations, code understanding, terminal execution, and browser automation with schema-based definitions \n Permission system with four levels (auto, ask-once, ask-each, never) and risk assessment for approval workflows \n

Skill by ara.so — Daily 2026 Skills collection.

Skill by ara.so — Daily 2026 Skills collection.

Architectural patterns and guardrails for building reliable autonomous agents that start constrained and earn autonomy through proven reliability. \n \n Covers three core agent loop patterns: ReAct (alternating reasoning and action), Plan-Execute (separated planning and execution phases), and Reflection (self-evaluation and iterative improvement) \n Emphasizes guardrails-first approach with hard cost limits, step count reduction, and least-privilege API access to prevent runaway behavior \n Iden