Try: help | create-agent demo | agent-interview demo
Experimental autonomous AI agent environment
1. Core Concept โ PEAS ๐ง
- Performance Measure: Criteria to evaluate success.
- Environment: Where the agent operates.
- Actuators: How the agent acts.
- Sensors: How the agent perceives.
2. Agent Architecture ๐๏ธ
- Perception: Collect and process input.
- Decision: Choose optimal action.
- Action: Execute via actuators.
3. Simple Pseudo-Code ๐ป
loop:
sense()
decide()
act()
log()
4. Purpose of AiAgent ๐ฏ
AiAgent is an experimental terminal environment designed to help users
understand, design, and simulate AI Agents as structured autonomous systems.
Instead of treating AI as a black box,
AiAgent emphasizes transparency, modularity,
and engineering-driven thinking.
5. Vision & Mission ๐
Vision
To become a foundational experimentation platform
for autonomous AI Agents that are transparent,
modular, and trustworthy.
Mission
- Make AI agent architecture understandable
- Bridge theory with system design
- Empower builders and researchers
- Prepare AI for autonomous execution
6. Litepaper / Whitepaper ๐
AiAgent addresses the lack of transparency in modern AI systems
by offering a terminal-based environment focused on agent architecture.
It enables users to create, inspect, and simulate AI Agents
using explicit models such as PEAS and decision loops.
7. Roadmap AiAgent ๐ฃ๏ธ
- Phase 1: Foundation & simulation
- Phase 2: Interactive agent design
- Phase 3: Applied intelligence (non-LLM)
- Phase 4: Autonomous & Web3 integration
- Phase 5: Multi-agent systems
- Phase 6: Safety & governance