AI Architecture
In 2026, AI architecture has shifted from monolithic chatbots to “Skill-based Agent Architecture” using frameworks like Google’s ADK (Agent Development Kit).
Skill-Based Architecture (ADK)
Section titled “Skill-Based Architecture (ADK)”Instead of a single “all-knowing” model, an agent is composed of modular “Skills.” This allows for better auditability and domain expertise.
Example: The Intake & Justification Agent
Section titled “Example: The Intake & Justification Agent”A governance agent built with these skills:
- Skill A (Context Miner): Conducts consultative interviews to extract human-task-replacement data.
- Skill B (Efficiency Calculator): Performs ROI calculations based on hours saved vs. token burn.
- Skill C (Legacy Re-addresser): Scans system history (Jira/GitHub) to prevent redundant development.
Strategic Design Questions (The “Truth-Telling” Set)
Section titled “Strategic Design Questions (The “Truth-Telling” Set)”When designing an agentic solution, the architect must ask:
- The Overkill Check: Can an API/UI fix this without an LLM?
- The Latency Tolerance: Can the process handle 15–30 seconds of “thought”?
- The Longevity Test: Is this a permanent operational need?
- The Human-in-the-Loop: Who is the final authority for verification?
Implementation Patterns
Section titled “Implementation Patterns”- Hybrid RPA: Using an LLM (e.g., Gemini Flash) for extraction and a deterministic script for data entry to minimize cost Intake Agent.
- Context Caching: Utilizing “warm” starts for large codebases to reduce cost and latency.