Production Safety

How to Control AI Agents in Production

Practical strategies for deploying autonomous AI agents safely.

The Problem:

In development, mistakes are learning opportunities. In production, one uncontrolled action can cause irreversible damage.

Risks of Uncontrolled Agents

What can go wrong when AI agents operate without proper controls.

Data Loss

Agents with database access can execute destructive queries. A 'helpful' cleanup might delete critical records.

Impact:

  • Permanent data deletion
  • Broken reporting
  • Customer trust damage

Financial Impact

Agents can trigger expensive API calls, provision cloud resources, or initiate transactions.

Impact:

  • Runaway costs
  • Budget overruns
  • Unauthorized payments

Security Breaches

Agents can be manipulated through prompt injection to access unauthorized systems.

Impact:

  • PII exposure
  • Compliance violations
  • Regulatory fines

Strategy 1: Runtime Guardrails

Enforce controls at the moment an agent attempts to act. Unlike prompts, runtime guardrails cannot be bypassed.

Action-Level Control

Evaluate each action individually before execution, not just overall intent.

Real-Time Decisions

Decisions in milliseconds. No impact on agent throughput.

Strategy 2: Action Validation

Action Classification — Categorize by type (read, write, delete) and target (database, API, cloud)

Policy Matching — Apply rules: allowed, blocked, or requires approval

Risk Scoring — Calculate score based on severity, sensitivity, context

Context Evaluation — Who initiated? What are the consequences?

Strategy 3: Human-in-the-Loop

Not every action should be fully autonomous. High-stakes decisions pause for review.

ALLOWLow-risk actions execute immediately
REQUIRE_APPROVALMedium-risk actions pause for human review
BLOCKHigh-risk actions are blocked entirely

This maintains efficiency for routine operations while ensuring critical decisions get oversight.

Strategy 4: Runtime Enforcement

It's not enough to suggest what an agent should do—you need to control what it can do.

Runplane guard() example

await runplane.guard(
  "delete_records",
  "production_database",
  { table: "users", count: 1500 },
  async () => {
    // Only executes if ALLOWED
    await db.deleteRecords()
  }
)

Runplane Performance

<50ms

Latency

Per decision

100%

Audit

All actions logged

3

Outcomes

ALLOW / BLOCK / APPROVAL

Key Principles

Visibility Over Trust

Don't trust agents will behave. Verify every action.

Fail Closed

When in doubt, block. Require explicit allowance.

Performance Matters

Controls must be fast. Under 50ms.

Implementation Checklist

Inventory all agent capabilities and tool access
Classify actions by risk (safe, dangerous, needs review)
Define policies for each action type and target
Implement runtime enforcement with guard()
Set up approval workflows for high-risk actions
Enable audit logging for all decisions
Monitor, review logs, and iterate on policies

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