ARCHITECTURE.md

<h1 align="center">AISA Reference Architecture</h1>

AISA defines agentic AI systems as **composed, governed systems** whose behavior emerges from the interaction between reasoning, execution, infrastructure, evaluation, and policy enforcement.

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<h2 align="center">Layer Responsibilities</h2>

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### LLM Foundation Layer
Core language modeling, inference, and reasoning substrate.

- Tokenization and inference  
- Prompt engineering and instruction tuning  
- LLM APIs, adapters, and context window management  
- Alignment, safety grounding, and fine-tuning  

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### Tool & Environment Layer
Controlled interaction with external systems and execution environments.

- Structured tool definitions and schemas  
- Code execution and sandboxing  
- Safe function calling and Multi-Call Protocol (MCP) support  
- Error handling, retries, and permission control  

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### Cognitive Agent Layer
Goal-directed reasoning, planning, and decision-making.

- Task planning and decomposition  
- Memory management and reflection loops  
- Multi-turn reasoning and goal tracking  
- Integration of external and human feedback  

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### Agentic Infrastructure Layer
Orchestration, coordination, and runtime control.

- Workflow orchestration and coordination  
- Multi-agent communication patterns  
- State management and observability  
- Logging, monitoring, and cost–latency optimization  

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### Evaluation & Feedback Layer
Continuous assessment of behavior, quality, and safety.

- Component-level and behavioral evaluations  
- Monitoring, metrics, and error analysis  
- Human-in-the-loop evaluation  
- Automated regression and quality testing  

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### Development & Deployment Layer
Lifecycle management and controlled system evolution.

- Version control of agents and artifacts  
- CI/CD pipelines and deployment strategies  
- Benchmarking, A/B testing, and performance tracking  
- Security, access control, and lifecycle management  

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### Governance, Ethics & Policy Layer
System-wide constraints, oversight, and accountability.

- AI policies and transparency standards  
- Fairness, bias mitigation, and privacy protection  
- Human-in-the-loop governance frameworks  
- Regulatory compliance and ethical oversight  

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## Architectural Principles

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**1. Separation of Concerns**  
Clear separation between reasoning, execution, orchestration, and governance responsibilities.

**2. Assurance-by-Design**  
Evaluation, monitoring, and governance are embedded into the system architecture from the outset.

**3. Dual-Plane Design**  
A strict distinction between the data plane (runtime execution) and the control plane (policies, permissions, and budgets).

**4. Contract-Driven Interfaces**  
Structured, machine-checkable interfaces that reduce ambiguity and improve testability and auditability.

**5. Continuous Improvement Loop**  
Agent behavior evolves through feedback-driven updates to prompts, tools, evaluations, and policies.

**6. Practical Deployability**  
Explicit consideration of real-world constraints including cost, latency, observability, access control, and versioning.