02. Always-On Architecture

Chapter 2 of 24 · 10 min

KEY INSIGHT

Always-on operation requires careful handling of resource usage, state management, and graceful degradation under varying system conditions. An always-on agent must coexist with other system operations without consuming excessive resources. CPU usage should remain minimal during idle periods. Memory consumption must stay bounded as the agent accumulates information over months or years of operation. Disk usage requires monitoring to prevent unbounded growth. The architecture separates concerns into distinct processes. The agent process handles reasoning and response generation. Worker processes manage tool execution in isolation. The memory service provides data access without blocking the main loop. This separation prevents tool execution failures from affecting agent stability. Graceful Degradation System resources are finite, and an always-on agent must handle resource constraints gracefully. Under high load, the agent prioritizes essential functions over background tasks. Memory pressure triggers aggressive cleanup of transient caches. Extended inactivity activates power-saving modes. The agent maintains health metrics and logs operational status. Monitoring these metrics helps identify degradation before failures occur. Automatic restart mechanisms recover from unexpected crashes without manual intervention. State Persistence Continuous operation means the agent must persist state across process restarts and system reboots. Critical state includes conversation history, learned preferences, scheduled tasks, and tool configurations. The persistence layer writes state to durable storage at regular intervals and during significant events. Crash recovery reconstructs the agent's operational state from persisted data. The reconstruction process validates data integrity and recovers to a consistent point. Some recent state may be lost during recovery, requiring graceful acknowledgment of incomplete information.

Local verification checkpoint

Run the smallest example from this chapter in a local workspace and record the package version, runtime, data path, and observed output. If the result depends on model size, vector count, CPU/GPU backend, or available memory, note that constraint beside the exercise so the lesson remains reproducible.

EXERCISE

Design a health monitoring system for an always-on agent. Identify five critical metrics to track, specify acceptable thresholds for each, and describe the recovery actions that should trigger when thresholds are exceeded.