State across restarts

Long-running agents are expected to survive interruptions. Machines reboot, processes crash, and deployments change. If an agent forgets everything each time it starts, it cannot behave autonomously in any meaningful way.

Persisting state across restarts allows an agent to pick up where it left off. It turns a long-running process into a continuous system, even when execution is interrupted.

Identifying which agent state must survive restarts

Not all state is worth saving. Some values are incidental and can be recomputed. Others define what the agent is doing and why.

Persistent state usually includes things like outstanding tasks, progress markers, decisions already made, and records of completed work. The goal is to preserve intent and progress, not every variable in memory.

Persisting agent state to durable storage

Durable storage means something that outlives the process. Files are often enough, especially early on.

A common approach is to serialize agent state to a simple format such as JSON. This keeps the stored state readable and easy to inspect during development.

import json

state = {
    "current_task": "generate_planet_pages",
    "completed_pages": ["mercury.html", "venus.html"]
}

with open("agent_state.json", "w") as file:
    json.dump(state, file)

Restoring state when an agent restarts

When an agent starts, it should attempt to restore its previous state before doing any new work. This lets the agent resume its role instead of starting from scratch.

Restored state becomes the initial in-memory state that drives the agent’s next decisions.

import json

with open("agent_state.json", "r") as file:
    state = json.load(file)

Handling partially completed work after a restart

Restarts often happen mid-task. An agent may have produced some outputs but not finished the job.

Persisted state should make partial progress explicit. That allows the agent to decide whether to continue, retry, or skip work it already completed, instead of guessing.

Ensuring continuity of behavior across runs

Continuity is not about resuming execution at the exact same line of code. It is about preserving the agent’s understanding of where it is and what remains to be done.

When state is restored cleanly and used consistently, the agent’s behavior across multiple runs appears continuous, even though execution was interrupted.

Conclusion

Persisting state across restarts allows an agent to behave as a long-lived system rather than a fragile script. By identifying meaningful state, storing it durably, restoring it on startup, and accounting for partial work, we give agents the ability to survive interruptions without losing direction.

At this point, we are oriented to how autonomous systems maintain continuity over time.