Chunk 32.2

In this chunk, the assistant dramatically improved the agent's intelligence and the operational UI. The agent's critical flaw of over-provisioning was fixed by introducing `projected_proofs_hr` (running + loading capacity), rewriting the fast-path logic, and adding rate-limit awareness. The UI was significantly expanded with "Curio Demand" and "Agent Activity" panels (Actions, Alerts, Machine Perf tabs), keyboard shortcuts, and a machine notes system for persistent hardware annotations. The agent architecture was fundamentally redesigned from an ephemeral per-cron invocation to a persistent conversational runtime. A rolling conversation log in SQLite now maintains context across runs, with a 30k token window, LLM-based summarization, and truncated tool results to manage the budget. Human feedback from alert acknowledgments and config changes is injected directly into this thread as user messages, giving the agent genuine memory and the ability to learn from operator preferences. A dedicated `agent_knowledge` store was built to persist these preferences. Operational stability was hardened by fixing an SSH process pile-up in the cuzk-status proxy (adding a hard timeout) and a JavaScript variable mismatch that left the Agent Activity panel stuck on "Loading...". The user gained direct control over the agent's primary objective through an editable `target_proofs_hr` field in the UI summary cards, with changes automatically notified to the agent via the conversation thread. The chunk concluded with the user requesting further agent awareness of instance state transitions, setting the stage for reactive, event-driven agent behavior.

From Ephemeral to Conversational: Building an Autonomous LLM Fleet Agent with Persistent Memory 2688 words

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