80% of companies have deployed generative AI in some form. Yet roughly the same percentage report no material impact on their business.

The numbers get worse when you look at AI agents specifically. Only 15% of technology leaders are actually deploying autonomous agents in production. The rest are stuck in pilot mode, running demos that look impressive but never make it past testing.

Today's LLMs are remarkably capable, so the gap between demo and production is about reliability.

Everything comes down to math, specifically, the math of compounding errors.

The math behind agent failures

Small error rates at each step multiply into large failure rates overall.

More than half your operations fail before they complete.

Even at 99% reliability per step, which is extremely optimistic, a 20-step process only succeeds 82% of the time. One in five attempts fails.

This is why demos look great but production breaks.

What makes production different

How to build reliable agents

The teams successfully deploying AI agents in production do a few things differently.

1. Design for human-in-the-loop

Not all decisions should be autonomous. The key is identifying which actions can run automatically and which need human approval.

Build escalation paths from day one. The agent should know when it's uncertain and when to hand it off to a human. This caps the blast radius of errors.

2. Reliability as the core metric

Success rate matters more than feature count. Measure how often the entire process succeeds, not just individual steps.

Track:

• End-to-end success rate: What percentage of operations complete successfully?
• Failure modes: Where and why are things breaking?
• Time to resolution: How long does recovery take when things fail?

Don't add new capabilities until existing ones work reliably. A simple agent that works 95% of the time is more valuable than a sophisticated agent that works 60% of the time.

3. Observability from day one

With non-deterministic systems, you need to see exactly what happened at each step to debug failures.

Log:

• Every decision the agent makes
• Every tool call and its result
• Every escalation to humans
• Timestamps and latency for each step
• Full input/output pairs

When something breaks and often it does, you need a complete trace to understand “why.”

4. Buy the infrastructure, build the logic

Building orchestration, deployment, and monitoring from scratch is a distraction. Use existing platforms for the generic infrastructure pieces.

Focus engineering effort on what makes your product unique, the domain-specific intelligence that creates value. For debt collection, that means building agents that understand compliance rules, negotiation dynamics, and payment behavior patterns.

Start narrow, expand carefully

Don't try to build a general-purpose agent that handles everything. Start with one specific use case where:

• Success is clearly measurable
• The process has a manageable number of steps
• Failure has limited consequences
• There's a clear baseline to beat (usually humans doing the work manually)

Build it, measure it, iterate on reliability. Only after achieving 90%+ success rate on the narrow use case should you consider expanding scope.

As you add new capabilities:

• Add them one at a time
• Measure impact on overall reliability
• Build escalation paths for the new capability
• Ensure observability covers the new steps

The path forward

AI agents will become fully autonomous as models get better, reliability improves, and the compounding error problem gets solved.

But getting there requires solving the reliability gap first. The teams building successful AI agents are doing it incrementally, starting with selective autonomy, measuring what works, and expanding the agent's scope as reliability improves.

Full autonomy is the destination, but selective autonomy is the path for now, to get there without breaking the production along the way.