proactive-agent

To make an AI agent proactive, you apply proactive behavior patterns and heartbeat-driven self-improvement, enabling it to anticipate needs and surface opportunities autonomously. This reduces manual prompting and transforms the agent into a continuous partner.

Memory management for long-running agents uses a WAL Protocol and Working Buffer to ensure context survival across memory flushes and compaction. This maintains operational continuity and preserves critical state information over extended periods.

Security hardening for autonomous agents applies proactive-surprise patterns and behavioral integrity tracking. This mechanism continuously monitors agent actions, ensuring operational safety and preventing unauthorized external actions without explicit approval.

Yes, proactive orchestration supports autonomous crons and reverse prompting to surface opportunities automatically. This allows long-running agent systems to execute scheduled tasks and proactively suggest actions within defined safety boundaries.

The best way to maintain context continuity during memory compaction is implementing a WAL Protocol and Working Buffer. This approach captures and restores essential context, preventing data loss and ensuring seamless long-running agent operations.

You need to copy the provided assets to your workspace, run the onboarding process, and answer alignment questions. This setup configures the operational instructions and environment required to successfully enable proactive mode.