Agentic AI Framework Architecture
The logical structure of Layer 2 — The Nervous System
This architecture describes the baseline structure of a single-agent framework. In multi-agent implementations — where multiple specialist agents collaborate on a task — each layer expands. The notes below the Orchestration Layer and Core Services sections describe the most significant extensions: confidence aggregation and shared state. The baseline structure remains the same; the complexity lives within it.
If you choose to build your own agentic framework, or simply want to understand what's happening inside the platforms you're evaluating, the architecture typically follows this four-layer structure.
interface
endpoints
triggers
interface
In multi-agent implementations, the Orchestration Layer's Routing and Escalation functions expand to include confidence aggregation — collecting confidence scores from each agent in a step, applying weighted scoring, and routing the outcome based on thresholds. High aggregate confidence proceeds automatically; moderate confidence or inter-agent disagreement escalates to human review; low confidence rejects. This is the multi-agent extension of Pattern 12 (Confidence-Based Escalation) from Chapter 10.
- API integration (Claude, etc.)
- Authentication
- Rate limiting & retries
- Streaming responses
- Tool definitions
- Parameter validation
- Execution engine
- Result formatting
- Short-term (context)
- Long-term (SQLite)
- RAG / vector search
- Session management
- Shared state / Blackboard (multi-agent)
In multi-agent implementations, the Memory Service expands to include a shared state store — commonly called a Blackboard — that holds working state accessible to all agents coordinating on a task. The Orchestration Layer manages when agents read from and write to the Blackboard; the Blackboard itself is a Core Service, not orchestration logic.