AI tasks that run too long or loop without progress waste money. Execution limits prevent that. Set a maximum duration per task (for example, 10 minutes), a retry cap (for example, 3 attempts), and a resource ceiling per employee or company-wide. If a task hits any limit, it stops cleanly, saves its progress, and notifies you.

Limits are especially important for scheduled and autonomous work. A task running at 3 AM without supervision should not burn through your entire credit balance because it entered a loop. Execution limits ensure that even unattended work stays within the boundaries you set.

When a task stops at a limit, the notification includes what happened, how much was consumed, and a suggested next step. You can increase the limit for that specific task, adjust the employee's configuration, or investigate the issue. Limits are guardrails, not dead ends. They protect you while giving you full visibility into what went wrong.

Prevent Runaway Tasks With Hard Execution Boundaries

Autonomous agents are powerful precisely because they keep working without constant supervision. But that same quality can become a liability if a task enters an unexpected loop, hits a rate-limited API repeatedly, or spawns more subtasks than intended. Execution Limits give you hard ceilings so no single agent task can consume unbounded resources.

You can set time limits (maximum wall-clock time for a task), retry caps (maximum attempts before a tool call is abandoned), step counts (maximum number of agent actions per run), and resource ceilings (token usage, API call counts). When a limit is reached, the agent stops, logs what it was doing, and notifies you, rather than silently failing or running forever.

Configure Limits Per Employee and Per Task Type

A research agent that synthesizes long documents legitimately needs more steps and more time than a scheduling agent booking a meeting. Execution Limits are configurable per employee and per task category, so you can give complex agents room to work while keeping simpler agents tightly scoped.

Default limits are set at the workspace level and apply to all new employees at hire. Individual employees can have their limits adjusted up or down from the workspace default. This layered approach means you always have a safety floor, with flexibility on top for agents that genuinely need it.

When a limit is triggered, the full execution trace up to that point is preserved in the Execution Inspector. You can review exactly what the agent was doing, why it hit the limit, and decide whether to adjust the limit, fix the task configuration, or intervene manually.

Why Execution Limits Are Non-Negotiable for Production AI

In production environments, runaway agent tasks are not just expensive. They can trigger cascading failures in downstream systems, exhaust third-party API quotas, or produce thousands of unwanted side effects before anyone notices. Execution Limits are the circuit breaker that keeps isolated agent failures from becoming systemic problems.

This is especially important as you scale your AI workforce. With five agents, you can watch every task manually. With fifty, you cannot. Execution Limits let you operate at scale with confidence that no single agent or task can damage the whole system, even if something unexpected happens.

Before execution limits, we had agents occasionally run for twenty minutes on a stuck tool call. Now they cap at three minutes and escalate. Our costs dropped immediately.