AI Agents

1. Human-in-the-Loop Design

graph TD
    propose["AI Agent Proposes Action"] --> review{"Human Review"};
    review -->|Accepts| execute["Action Executed"];
    review -->|Rejects/Corrects| learn["AI Learns from Correction"];
    learn --> propose;
    execute --> goal["System Goal Achieved"];

2. Memory as an Architectural Decision

• User level: Preferences like chart types, writing tone.
• Team level: Recurring queries, dashboards.
• Org level: Institutional knowledge and policies.

mindmap
  root((Memory))
    (Architectural Decision)
      (UX)
      (Privacy)
      (System Implications)
    (Levels)
      (User)
        (Preferences)
        (Writing Style)
      (Team)
        (Recurring Queries)
        (Dashboards)
      (Org)
        (Institutional Knowledge)
        (Policies)
    (Implementation)
        (Context Layer)
        (Behavior Layer)

3. Multi-Model Inference and Orchestration

graph TD
    A[User Query] --> B{Model Routing Logic};
    B -->|Simple Task & Low Cost| C[Smaller Model];
    B -->|Complex Task & Higher Cost| D[Advanced Model];
    B -->|High Latency Sensitivity| E[Fastest Model];
    C --> F[Response];
    D --> F;
    E --> F;

4. The Importance of Trust

graph TD
    A[Trust] --> B[Consistent];
    A --> C[Explainable];
    A --> D[Auditable];

Sources:

• What Makes 5% of AI Agents Actually Work in Production?