Writing

Set Runtime Constraints on AI Agents Before They Hit Production

When we deploy AI agents into production, the infrastructure surprises aren't about model quality. They're about runtime behavior. An agent that autonomously queries databases, calls APIs, and retries on failure can generate a completely unpredictable pattern of load. Traditional systems expect requests to be defined, bounded, and idempotent. Agents break that assumption. The autonomy that makes an agent useful is exactly what makes it dangerous if left unbounded.

In plain English: an agent might retry a database call ten times with exponential backoff, not because it's malicious, but because it's trying to complete a task. It's doing what we asked it to do, just overzealously. Multiply that by dozens of concurrent agents, each with its own retry loop, and you have a self-inflicted DDoS. The originating request might be a single user action, but the downstream effect is a storm of internal calls that the system wasn't provisioned for.

I saw this play out in a proof-of-concept for invoice processing. A single misconfigured retry loop spiked database CPU to 100% and blocked the entire order pipeline. The agent wasn't broken; it was simply retrying a step that kept failing, each retry hammering the database harder. The fix wasn't a smarter model or better prompt engineering. It was a hard limit: three retries per step, and a maximum of twenty tool calls per run. That's it. A simple runtime constraint turned a dangerous agent back into a useful one.

Before scaling any AI workload, every team should set explicit runtime constraints. These aren't afterthoughts—they're non-negotiable safety nets. Retry budgets keep transient failures from becoming cascading outages. Step limits prevent an agent from wandering off into an infinite loop of tool calls. Concurrency caps stop a single agent from spawning too many parallel actions. Blast-radius isolation ensures that if one agent goes rogue, it doesn't take down shared infrastructure.

The pattern extends beyond retries. Any bounded context—token limits, execution timeouts, memory caps—can act as a circuit breaker. The key is to design these constraints into the agent's operating environment from the start. It's easy to focus on the clever reasoning and forget that agents are programs that consume real resources. When we forget, we build systems that work beautifully in demos and fail spectacularly under load.

The InfoQ article 'The hidden infrastructure problem behind production AI' captures this issue well. It's a reminder that scaling AI is as much about software engineering discipline as it is about models. Autonomy is a feature, but only with the right guardrails. Hard limits don't constrain usefulness—they protect it.