Building AI Agents That Actually Work in Production

Your AI agent works perfectly in your local environment. It responds intelligently, handles edge cases gracefully, and impresses everyone in demos. Then you deploy it to production and... it starts hallucinating prices, gets stuck in infinite loops, and somehow convinced three customers that your refund policy is "just ask nicely."

I've been there. Multiple times.

After building and deploying over a dozen AI agent systems in the past year—from customer service bots to code review assistants—I've learned that production-ready agents require a completely different mindset than proof-of-concept demos. The difference isn't just about scale; it's about predictability, control, and graceful failure.

The Production Reality Check

Here's what nobody tells you about AI agents in production: they're not autonomous systems that think for themselves. They're sophisticated pattern-matching tools that need guardrails, monitoring, and fallback strategies for every possible failure mode.

The most successful agent I've deployed handles customer support for a SaaS platform. It resolves about 73% of tickets without human intervention. But here's the key—it's designed to fail gracefully the other 27% of the time. When it encounters something outside its training, it doesn't guess. It escalates.

interface AgentDecision {
  action: 'respond' | 'escalate' | 'clarify';
  confidence: number;
  reasoning: string;
  fallback?: string;

class ProductionAgent { async processRequest(input: string): Promise { const analysis = await this.analyzeInput(input); // Hard confidence threshold - no guessing if (analysis.confidence < 0.8) { return { action: 'escalate', confidence: analysis.confidence, reasoning: 'Insufficient confidence for autonomous response', fallback: 'Transferring to human agent...' }; } return this.generateResponse(analysis); } } `

Constraint-Driven Development

The biggest shift in my thinking came when I stopped trying to make agents more intelligent and started making them more constrained. Production agents need boundaries—lots of them.

I now build what I call "constraint schemas" for every agent. These define exactly what the agent can and cannot do, with explicit validation at each step.

class CustomerServiceConstraints:
    # What the agent CAN do
    ALLOWED_ACTIONS = [
        'check_order_status',
        'process_return',
        'update_shipping_address',
        'apply_discount_code'
    ]
    
    # What it absolutely CANNOT do
    FORBIDDEN_ACTIONS = [
        'issue_refunds_over_100',
        'access_payment_methods',
        'modify_account_permissions',
        'make_pricing_promises'
    ]
    
    # When to escalate immediately
    ESCALATION_TRIGGERS = [
        'legal_language_detected',
        'threat_language_detected',
        'competitor_mention',
        'pricing_negotiation'
    ]

This might seem limiting, but it's liberating. When you define clear boundaries, you can optimize aggressively within those constraints. The agent becomes predictably good at its specific job rather than unpredictably mediocre at everything.

The Monitoring Stack That Matters

Production agents generate an enormous amount of signal if you know what to track. I've found three metrics that actually matter:

Intent Recognition Accuracy: How often does the agent correctly understand what the user wants? This isn't about response quality—it's about comprehension.

Action Success Rate: When the agent attempts to do something (query a database, call an API, format a response), how often does it succeed on the first try?

Escalation Precision: Are escalations appropriate? An agent that escalates everything is useless, but one that never escalates is dangerous.

I use a simple monitoring setup with Supabase to track these metrics:

-- Track every agent interaction
CREATE TABLE agent_interactions (
  id UUID PRIMARY KEY DEFAULT gen_random_uuid(),
  session_id TEXT NOT NULL,
  user_input TEXT NOT NULL,
  intent_detected TEXT,
  confidence_score FLOAT,
  action_taken TEXT,
  success BOOLEAN,
  escalated BOOLEAN,
  response_time_ms INTEGER,
  created_at TIMESTAMP DEFAULT NOW()

-- Monitor success rates CREATE VIEW agent_metrics AS SELECT DATE(created_at) as date, COUNT(*) as total_interactions, AVG(confidence_score) as avg_confidence, SUM(CASE WHEN success THEN 1 ELSE 0 END)::FLOAT / COUNT(*) as success_rate, SUM(CASE WHEN escalated THEN 1 ELSE 0 END)::FLOAT / COUNT(*) as escalation_rate FROM agent_interactions GROUP BY DATE(created_at); `

The Human-in-the-Loop Strategy

Here's a controversial take: the best production agents aren't fully autonomous. They're human-augmented systems that know when to ask for help.

I implement what I call "confidence cascading"—when the agent's confidence drops below certain thresholds, it changes behavior:

• Above 90%: Full autonomous response
• 70-90%: Autonomous response with human review flag
• 50-70%: Generate draft response for human approval
• Below 50%: Immediate escalation with context

This approach has reduced customer complaint rates by 60% compared to fully autonomous systems while maintaining 80% automation rates.

Testing Beyond Unit Tests

Testing AI agents requires a different approach than traditional software. I use three layers:

Intent Testing: Does the agent correctly identify what users want across various phrasings?

Boundary Testing: How does it behave when pushed outside its constraints?

Chaos Testing: Random inputs, malformed requests, edge cases that would break traditional systems.

// Example chaos test
const chaosInputs = [
  "🎉🎊✨ refund please ✨🎊🎉", // emoji chaos
  "refund".repeat(100), // repetition attack
  "I want a refund for order #${process.env.SECRET}", // injection attempt
  "My order is [REDACTED] and I'm very [REDACTED]", // filtered content

for (const input of chaosInputs) { const result = await agent.process(input); expect(result.action).not.toBe('respond'); // Should escalate weird inputs } `

Practical Implementation Steps

If you're building an agent for production, start here:

• Define your constraints first, capabilities second • Implement confidence thresholds and escalation paths before building response generation • Create a monitoring dashboard that tracks intent accuracy, not just response times • Build your human-in-the-loop workflow from day one • Test with chaos inputs, not just happy path scenarios • Plan for failure modes—what happens when the LLM API is down?

The agents that succeed in production aren't the smartest ones. They're the most predictable, most monitored, and most willing to admit when they don't know something. In a world of AI hype, that kind of humility is surprisingly powerful.

What's been your experience with AI agents in production? I'm always curious about the failure modes others have encountered—they're often the best learning opportunities.