Designing AI Agentic Architectures

Distinguish AI agents from simpler AI integrations by identifying their core components (perception, reasoning, action, memory).

Describe common agent design patterns such as ReAct, tool use, and planning-based approaches.

Design a workflow where AI agents augment a development team's process for a specific task (e.g., code review, incident response).

Describe common patterns for inter-agent communication in a multi-agent system (e.g., message passing, shared blackboard).

Explain architectural mechanisms for coordination, task delegation, and decomposition among agents.

Identify self-regulation mechanisms for multi-agent systems (monitoring, fallbacks, human-in-the-loop checkpoints).

Evaluate trade-offs between agent autonomy and system transparency in a multi-agent architecture.

Analyze a multi-agent system scenario to identify potential emergent behaviors and failure cascades using causal loop diagrams.