A hands-on repository documenting the progressive evolution of AI agent architectures, from simple reasoning loops to increasingly autonomous and adaptive systems.
This project focuses on understanding how modern agent capabilities emerge by incrementally adding new architectural components rather than relying on high-level frameworks.
The goal is to study:
- How agents reason
- How agents plan
- How agents execute tasks
- How agents evaluate their own work
- How agents recover from failures
- How agent architectures evolve over time
Modern agent systems are often presented as complete frameworks, making it difficult to understand the individual building blocks that make them work.
This repository takes the opposite approach.
Each stage introduces a single new capability while preserving everything learned in previous stages.
ReAct
↓
Planning
↓
Execution
↓
Reflection
↓
Replanning
↓
Memory
↓
Human-in-the-Loop
↓
Multi-Agent Systems
↓
Autonomous Agents
Rather than treating these concepts as separate topics, this project treats them as evolutionary steps in increasingly capable agent systems.
The foundation.
Capabilities:
- Reasoning
- Tool usage
- Observation handling
- Iterative thinking loops
Goal:
Understand how an agent can think and act.
Adds structured task decomposition.
Capabilities:
- Goal analysis
- Task generation
- Planning before execution
Goal:
Understand how agents can break complex objectives into smaller tasks.
Separates planning from execution.
Capabilities:
- Dedicated planner
- Dedicated executor
- Clear responsibility boundaries
Goal:
Understand modular agent design.
Adds self-evaluation.
Capabilities:
- Result evaluation
- Feedback generation
- Retry mechanisms
Goal:
Understand how agents assess the quality of their work.
Adds failure recovery.
Capabilities:
- Failure tracking
- Alternative strategy generation
- Adaptive execution loops
- Replanning
Goal:
Understand how agents recover from unsuccessful attempts.
Planned explorations:
- Memory Systems
- State Persistence
- Human-in-the-Loop Agents
- Multi-Agent Collaboration
- Long-Term Planning
- Autonomous Workflows
src/
│
├── agents/
│ ├── react/
│ ├── planning/
│ ├── reflection/
│ ├── replanning/
│ └── ...
│
├── state/
│
├── prompts/
│
├── tools/
│
└── llm/
This repository is not intended to be a production-ready framework.
It is an educational exploration of agent architecture design.
By the end of the journey, the objective is to understand not only how modern AI agents work, but why each architectural component exists and what problems it solves.
The emphasis is on learning through implementation and observing how new capabilities emerge as the architecture evolves.