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AISA Architecture
This document outlines the layered architectural model introduced in AISA — Agentic AI Systems Architecture.
AISA models agentic AI systems as composed systems in which behavior emerges from the interaction between reasoning, execution, infrastructure, evaluation, and governance.
Layer Responsibilities (Summary)
Governance, Ethics & Policy Layer
- AI policies and transparency standards
- Fairness and bias mitigation
- Privacy and data protection
- Accountability frameworks and human-in-the-loop governance
- Regulatory compliance and ethical reflection
Development & Deployment Layer
- Version control of agents and artifacts
- Continuous integration and deployment
- Performance benchmarking and A/B testing
- Cost–latency trade-off management
- Security, access control, and lifecycle tracking
Evaluation & Feedback Layer
- Component-level evaluations
- Behavioral monitoring and quality metrics
- Error analysis and prioritization
- Human-in-the-loop evaluations
- Automated regression testing
Agentic Infrastructure Layer
- Workflow orchestration and coordination
- Multi-agent communication patterns
- State coordination and observability
- Logging, monitoring, and dashboards
- Latency, cost optimization, and EvalOps pipelines
Cognitive Agent Layer
- Task planning and decomposition
- Reflection loops and self-improvement
- Memory management
- Multi-turn reasoning and goal tracking
- Integration of external feedback
Tool & Environment Layer
- Tool creation and structured syntax
- Code execution and sandboxing
- Safe function calling and MCP support
- Error handling and retries
- Permission, rate control, and structured I/O
LLM Foundation Layer
- Tokenization and inference
- Prompt engineering and instruction tuning
- LLM APIs and adapters
- Context window optimization
- Fine-tuning, alignment, and safety grounding
Architectural Principles
1. Separation of Concerns
Clear separation between system responsibilities to prevent mixing reasoning, permissions, and orchestration logic.
2. Assurance-by-Design
Evaluation, monitoring, and governance are built into the architecture from the start, not added after deployment.
3. Dual-Plane Design
A clear distinction between the data plane (runtime execution) and the control plane (policies, permissions, and budgets).
4. Contract-Driven Interfaces
Structured and machine-checkable interfaces reduce ambiguity and improve testing and auditability.
5. Continuous Improvement Loop
Agent behavior evolves continuously through feedback-driven updates to prompts, tools, and policies.
6. Practical Deployability
The architecture accounts for real-world constraints such as cost, latency, observability, access control, and versioning.