How to Deploy AI Agents in Production

AI agents are no longer just demos. Organizations are deploying autonomous agents that write code, manage infrastructure, respond to incidents, and automate complex business processes. But moving from prototype to production requires careful planning around security, governance, and operational concerns.

This guide covers everything you need to know about deploying AI agents in production, from architecture patterns to security best practices to monitoring strategies.

1. What Does "Production" Mean for AI Agents?

A production AI agent is fundamentally different from a demo or prototype. In production, the agent:

• Performs real actions — writes to databases, deploys code, sends emails, modifies infrastructure
• Operates autonomously — makes decisions without human intervention for routine tasks
• Handles real data — accesses sensitive information, customer data, credentials
• Has real consequences — mistakes cost money, time, reputation, or compliance standing

The gap between demo and production isn't just about reliability—it's about trust. Can you trust the agent to operate within acceptable boundaries? Can you prove what it did and why? Can you stop it when something goes wrong?

2. Production Architecture Patterns

There are several architectural patterns for deploying AI agents. The right choice depends on your security requirements, scale needs, and existing infrastructure.

Pattern 1: Direct Integration

The simplest pattern connects your AI agent directly to tools and APIs. The agent makes decisions and executes actions in a single flow.

User Request → LLM Agent → Tool APIs → Response
                  ↓
            Direct execution

Pros: Simple, low latency
Cons: No governance, no audit trail, hard to control
Use when: Internal tools, low-risk actions, prototypes

Pattern 2: Control Plane Architecture

A control plane sits between the agent and execution. Every action is evaluated against policies before it runs, creating a governance layer.

User Request → LLM Agent → Control Plane → Tool APIs
                              ↓
                        Policy Check
                        Approval Gate
                        Audit Log

Pros: Full governance, audit trails, human oversight
Cons: Additional complexity, slight latency
Use when: Production deployments, regulated industries, enterprise

Pattern 3: Orchestrated Multi-Agent

Multiple specialized agents coordinate through a central orchestrator. Each agent has specific capabilities and constraints.

                    ┌─→ Research Agent
User Request → Orchestrator ─→ Code Agent → Control Plane → APIs
                    └─→ Review Agent

Pros: Specialized capabilities, parallel execution
Cons: Complex coordination, higher cost
Use when: Complex workflows, diverse tool requirements

3. Governance and Policy Enforcement

Governance is the most critical aspect of production AI agents. Without it, you're essentially giving an unpredictable system unrestricted access to your infrastructure.

Policy-Before-Dispatch

The most effective governance pattern is policy-before-dispatch: every action is evaluated against configurable rules before it executes. This gives you four possible outcomes:

Example Policy Configuration

# safety-policy.yaml
rules:
  # Production writes always need approval
  - name: production-writes
    match:
      environment: production
      action_type: write
    decision: REQUIRE_APPROVAL

  # Limit deployment scale
  - name: deployment-limits
    match:
      capability: kubernetes
      action: deploy
    decision: ALLOW_WITH_CONSTRAINTS
    constraints:
      max_replicas: 10
      allowed_namespaces: [app, staging]

  # Block dangerous operations
  - name: no-delete-databases
    match:
      capability: database
      action: drop
    decision: DENY

4. Security Considerations

AI agents introduce unique security challenges. They combine the risks of API access, credential management, and autonomous decision-making.

Credential Management

• Never embed credentials in prompts or agent memory
• Use short-lived tokens with minimal scope
• Implement credential rotation
• Audit credential usage

Input Validation

• Validate all inputs before passing to tools
• Sanitize outputs before displaying to users
• Implement rate limiting on tool calls
• Set resource limits (tokens, API calls, compute time)

Prompt Injection Defense

• Separate system instructions from user input
• Validate tool parameters against schemas
• Use allowlists for permitted actions
• Monitor for anomalous behavior patterns

5. Monitoring and Observability

You can't manage what you can't measure. Production AI agents need comprehensive monitoring across multiple dimensions.

Key Metrics to Track

• Action success rate — What percentage of agent actions succeed?
• Policy decision distribution — How often are actions allowed vs. denied vs. requiring approval?
• Approval latency — How long do humans take to approve requests?
• Token usage — Cost and complexity of agent operations
• Error rates by tool — Which integrations are unreliable?
• Mean time to completion — How long do workflows take end-to-end?

Audit Trail Requirements

Every production AI agent needs an immutable audit trail that captures:

• What action was requested
• What policy decision was made (and why)
• Who approved (if applicable)
• What actually executed
• What the outcome was

6. Scaling AI Agents

As your AI agent usage grows, you'll face scaling challenges around concurrency, cost, and coordination.

Horizontal Scaling

• Use worker pools for parallel execution
• Implement job queues with backpressure
• Design for stateless agent execution
• Use distributed state storage (Redis, etc.)

Cost Management

• Cache common LLM responses
• Use smaller models for routine tasks
• Implement token budgets per workflow
• Monitor and alert on cost anomalies

7. Production Readiness Checklist

Before deploying an AI agent to production, ensure you have:

Next Steps

Ready to deploy AI agents in production? Cordum provides the governance layer you need—policy enforcement, approval gates, and audit trails built in.