AI Agents in Production: Beyond the Hype

August 21, 2025

Last month, I burned through $3,400 in API costs with a runaway customer service agent. Today, that same system handles 1,000+ daily queries for under $50. Here's what I learned the hard way.

The Reality Check

Everyone's building AI agents. Few are talking about what happens when they meet real users. After deploying agents across three production systems, I've collected some battle scars worth sharing.

The promise is seductive: autonomous systems that think, act, and adapt. The reality? Most agents are expensive while loops with delusions of grandeur.

The $3,400 Mistake

Our first agent used LangChain with GPT-4. Simple architecture:

1. Analyze user query
2. Fetch relevant docs
3. Generate response
4. Validate accuracy
5. Refine if needed

Seemed bulletproof. Until a user asked about "all possible product variations."

The agent entered a recursive loop, generating variations, validating them, finding issues, and generating more. 47 minutes. 2.3 million tokens. $3,400.

Lesson learned: Agents need circuit breakers, not just guardrails.

What Actually Works

After three months of iteration, here's our production architecture:

1. Token Budget Management

class AgentExecutor:
    def __init__(self, max_tokens=50000, max_iterations=5):
        self.token_budget = max_tokens
        self.iteration_limit = max_iterations

Every agent gets a hard token limit. Period. No exceptions.

2. Tiered Model Strategy

• Classifier: Claude Haiku ($0.25/1M tokens) - Routes queries
• Simple queries: GPT-3.5 Turbo - Handles 70% of requests
• Complex tasks: GPT-4 - Only when necessary
• Validation: Gemini Flash - Cost-effective double-checking

This cut costs by 85% without degrading quality.

3. State Persistence That Works

Forget complex state machines. We use simple JSON checkpoints:

{
  "stage": "data_retrieval",
  "tokens_used": 12500,
  "iterations": 2,
  "context": {...},
  "can_resume": true
}

If something fails, we resume from checkpoint, not from scratch.

The Metrics That Matter

Vanity metrics won't save you. Track these instead:

• Cost per successful resolution: Ours dropped from $8.50 to $0.12
• Timeout rate: Should be under 2%
• Human handoff rate: We're at 18% (was 65%)
• P95 response time: 4.2 seconds (users tolerate up to 5)

Three Non-Obvious Insights

1. Determinism beats intelligence

Smart agents are unpredictable. Dumb agents with good rails are profitable. We replaced our "reasoning" agent with a decision tree + LLM combo. Better results, 90% less cost.

2. Streaming saves more than money

Users abandon after 3 seconds of silence. Streaming responses reduced abandonment by 60%. The psychological impact matters more than the technical elegance.

3. Failure modes are features

Our agent now says "I need human help" faster. User satisfaction increased. Counter-intuitive but true: users prefer quick escalation over lengthy failed attempts.

The Framework That Scales

Here's our current stack:

• Orchestration: Temporal (not LangChain)
• Vector DB: Qdrant (self-hosted)
• Monitoring: OpenTelemetry + Grafana
• Circuit breaker: Custom Python middleware
• Testing: Proprietary golden dataset (1,000 real queries)

What's Next?

The agent gold rush is real, but most teams are optimizing for demos, not production. The winners will be those who understand that agents are tools, not magic.

My prediction: 2025 will be the year of "boring" agents - specialized, predictable, and profitable. The AGI dream can wait; businesses need solutions that work today.

Key Takeaways

✅ Start with a narrow use case
✅ Add constraints, not capabilities
✅ Measure everything
✅ Always, always have a kill switch

Currently building: An agent cost prediction model. Follow for updates on real-world AI engineering.