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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.
Layer Responsibilities
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
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
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
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
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
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
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
Architectural Principles
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.