AISA Reference Architecture
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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Layer Responsibilities
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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.