Retrying, escalating, and aborting

As agents become more autonomous, they inevitably encounter work that does not go as planned. In multi-agent systems, handling these situations well is part of maintaining control and reliability. This lesson focuses on what an agent should do after a failure is detected, so that work can continue safely and predictably rather than getting stuck or behaving erratically.

Retrying failed tasks

Retrying is often the simplest response to a failure. Some failures are temporary, and repeating the same task can succeed without changing anything else.

In practice, retries are usually conditional. An agent retries only when the failure fits a known pattern and when repeating the task is unlikely to cause harm or wasted effort. Retrying becomes a deliberate choice rather than an automatic reflex.

Retries are typically implemented by re-invoking the same step or tool after updating a small piece of state, such as a retry counter or timestamp.

result = worker.run(task)

if not result.success:
    retries += 1
    if retries <= MAX_RETRIES:
        worker.run(task)

This kind of retry logic allows progress while keeping behavior explicit and visible in code.

Limiting retries

Unlimited retries are dangerous in autonomous systems. Without limits, an agent can loop forever, consuming resources while making no progress.

A retry limit defines when persistence turns into failure. Once the limit is reached, the agent treats the task as genuinely unsuccessful and moves on to a different response.

Retry limits can be simple counters or more structured policies, such as retrying only within a time window or only when certain conditions change.

if retries > MAX_RETRIES:
    task.status = "failed"

By enforcing limits, the system remains predictable and avoids runaway behavior.

Escalating failures

When a task cannot be completed by a worker agent, escalation allows the problem to move upward rather than disappear. In manager–worker systems, escalation usually means reporting the failure to a manager agent.

Escalation is not about fixing the problem immediately. It is about transferring responsibility to an agent with broader context or authority. The manager can decide whether to reassign the task, modify the plan, or stop related work.

manager.notify_failure(task_id, reason)

This pattern keeps workers simple while allowing higher-level agents to coordinate recovery.

Aborting unsafe or unrecoverable tasks

Some failures should not be retried or escalated. If continuing a task would cause incorrect results, corruption, or unsafe behavior, the correct response is to abort.

Aborting marks a clear boundary: the task will not be completed, and no further attempts will be made. This prevents partial or misleading outcomes from leaking into the rest of the system.

Aborts are usually explicit state changes rather than silent exits.

task.status = "aborted"
task.reason = "inconsistent state detected"

Clear abort semantics make it obvious which work was intentionally stopped and why.

Recording failure outcomes

Failures are valuable signals. Recording what happened allows future decisions to be better informed.

Agents often store failure outcomes alongside task metadata, including the reason for failure and how it was handled. This information can influence later planning, retry policies, or task assignment decisions.

history.append({
    "task": task_id,
    "outcome": task.status,
    "reason": task.reason
})

By treating failures as data, the system becomes more adaptive without becoming unpredictable.

Conclusion

At this point, we are oriented around how agent systems respond after a failure occurs. Retrying, escalation, and aborting form a small but essential control layer that keeps autonomous work bounded and intentional. With these mechanisms in place, failures stop being dead ends and become managed outcomes that support stable, long-running systems.