Agents need limits. Most teams write their own.
We spoke with teams shipping AI agents, from small startups to large regulated companies. They named the same three problems. None of them are about the model. They are about what happens while the agent runs.
Every project gets its own guardrails
Step caps, timeouts, regex checks, cost limits. Teams write them again for each agent. They drift apart and nobody owns them.
Each team hand-codes loop caps, timeouts and SQL filters inside the agent. A new project starts from zero.
Set your limits once in SteerPlane. Every agent, in every framework, gets the same checks.
Wrong behaviour shows up late
An agent repeats a call, picks the wrong tool, or does something the business did not expect. Someone finds out days later.
Problems surface in user testing, a thumbs-down, or a support ticket. By then the run is long over.
Each step is checked as it happens. Repeats are stopped and every step is logged for review.
One bad call can be expensive
A runaway loop burns credits overnight. A delete or drop reaches production. Logs tell you after the damage is done.
Spend is checked on the monthly bill. Dangerous calls are blocked only where someone remembered to add a check.
A dollar ceiling per run, and deny rules for calls like delete_* or drop_*. Both are enforced before the step runs.
Teams with agents that touch real systems
Startups shipping agents
Your agent writes to real systems. You want limits in place before a customer finds the gap.
Platform teams
Several teams build agents. You want one set of rules instead of one per project.
Regulated enterprises
You need control and an audit trail, and your data cannot leave your environment.
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