Long-running agents beyond prompt engineering

Reliable long-running AI agents require deterministic engineering around language models rather than reliance on prompting, self-review, or LLM-based evaluation, which can introduce hallucination and drift. The discussion distinguishes models, which generate text, from agents, which execute tasks through a harness that controls tool use, validation, scheduling, context, and state. It recommends actively managing limited context through compaction, resets, and durable, preferably append-only storage that preserves plans, progress, and execution records across sessions. Durable agents can hibernate between events, resume through callbacks, polling, or workflow engines, and recover from failures using checkpoints, idempotency, event sourcing, and replayable logs. Progress should be defined in advance through explicit checklists and completion criteria, then validated with deterministic gates such as response codes, schemas, state changes, tests, state machines, sandboxing, and anomaly detection rather than asking another model to judge success. LLM evaluation may still be useful for narrowly scoped classification tasks, but it should remain constrained by predefined rules, permissions, identities, and observable execution data.

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