AI is increasingly being integrated into modern, decoupled architectures, but most implementations still rely on short-lived, request-based interactions. While effective for many use cases, this approach can fall short when workflows require continuity, coordination, and ongoing decision-making.
This session introduces the concept of persistent AI agents as an emerging pattern for addressing these gaps.
Using examples built with amazeeclaw, we will explore how long-running agents can retain context, monitor systems, and interact with APIs and services over time. These agents operate alongside existing components in a decoupled architecture, enabling new approaches to workflow orchestration and system interaction.
The session will include practical demonstrations of agents performing tasks such as observing changes, applying rules, and coordinating actions across systems. Each example will be used to illustrate architectural considerations, including state management, system boundaries, and integration patterns.
In addition to what works, the session will also address current limitations and challenges, including reliability, security, and observability. The goal is to provide a balanced, practical view of where this approach fits today.
Attendees will leave with a clearer understanding of persistent AI agents, how they relate to existing architectural patterns, and how they might begin exploring this approach within their own environments.
Key Takeaways
- Understand the role of persistent AI agents within decoupled architectures
- See practical examples of agents interacting with APIs and systems over time
- Learn key architectural considerations, including state and system boundaries
- Gain awareness of current limitations and implementation challenges
Audience
Beginner
Session Category
Emerging Technologies
Speaker(s)