Keeping agents running

Long-running autonomous agents are useful when work does not arrive all at once. In multi-agent systems, some agents are expected to stay alive, watch for changes, and respond over time. This lesson exists to orient us to what it means to keep an agent running without turning it into an uncontrolled or fragile process.

Designing agents intended to run indefinitely

Some agents are designed to start once and continue running until they are explicitly stopped. These agents do not have a fixed “end” like a script that generates a page and exits. Instead, they maintain state and repeatedly decide what to do next.

In practice, this usually means designing the agent so that it can sit idle without consuming excessive resources, while still being ready to act when needed. The agent’s structure matters more than any single action it performs.

Structuring main loops for long-lived execution

A long-running agent almost always revolves around a main loop. The loop provides a predictable place where sensing, deciding, acting, and updating can repeat over time.

A minimal long-lived loop often looks like this:

while True:
    check_for_work()
    do_background_tasks()

The loop itself is simple. The discipline comes from keeping the work inside the loop small, clear, and repeatable so the agent can run for hours or days without drifting into an inconsistent state.

Waiting for and reacting to external events

Long-running agents usually spend most of their time waiting. They may wait for user input, new files to appear, messages from other agents, or scheduled times to arrive.

A common pattern is to block briefly or sleep between checks so the agent does not spin continuously:

import time

while True:
    process_messages()
    time.sleep(5)

This allows the agent to react to events without wasting CPU, while still maintaining a regular rhythm of observation and response.

Periodically performing background work

Some work does not need to happen immediately. An agent might periodically regenerate static HTML pages, refresh cached data, or clean up old artifacts.

This work is often triggered by simple time-based checks inside the main loop:

import time

last_run = time.time()

while True:
    if time.time() - last_run > 3600:
        generate_planet_pages()
        last_run = time.time()

    time.sleep(10)

This keeps background tasks predictable and easy to reason about, even as the agent continues running.

Deciding when an agent should pause or terminate

Even agents designed to run indefinitely need clear stopping points. An agent may pause itself while waiting for human input, or terminate cleanly when it receives a shutdown signal.

Termination is usually handled explicitly, rather than by letting the program crash or exit unexpectedly:

running = True

while running:
    command = read_control_signal()
    if command == "stop":
        running = False

Clear pause and stop decisions make long-running agents safer to operate and easier to control.

Conclusion

At this point, we are oriented to what it means to keep an agent running over time. We have seen how indefinite execution is structured around simple loops, how agents wait for events, perform periodic work, and decide when to pause or stop. This is enough context to recognize and reason about long-running autonomous agents without getting lost in implementation detail.