A PM's Guide to AI Agent Architecture
6 days ago
- #User Trust
- #AI Agents
- #Product Management
- AI agents often fail in adoption despite high accuracy due to inability to handle complex user issues.
- AI agent architecture consists of four key layers: Context & Memory, Data & Integration, Skills & Capabilities, and Evaluation & Trust.
- Context & Memory involves decisions on how much the agent remembers, impacting user perception of understanding.
- Data & Integration focuses on system connections, balancing usefulness with potential failure points.
- Skills & Capabilities determine the agent's unique value propositions and user dependency.
- Evaluation & Trust is about measuring success and communicating limitations to build user confidence.
- Four orchestration approaches for AI agents: Single-Agent, Skill-Based, Workflow-Based, and Collaborative Architectures.
- Trust in AI agents is built through confidence calibration, reasoning transparency, and graceful escalation, not just accuracy.
- Future considerations include agent autonomy decisions and governance challenges in AI agent deployment.