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We were promised autonomous agents that could replace entire departments. Instead, I got an infinite loop, a $2,400 cloud bill, and a harsh lesson in engineering reality.
If you are building AI agents in 2026 using the "loop until solved" method, you are walking into a trap. Here is the post-mortem of a production failure and the architecture we used to fix it.
The "Infant with a Chainsaw" Problem
I recently audited a system where a simple task—extracting an address from a PDF—turned into a financial disaster.
The AI Agent (built on a popular framework) got confused between billing_address and address_billing. instead of failing gracefully, it entered a "Reasoning Loop":
- Check field.
- "Oh, I missed it."
- Try again.
- "Wait, let me think step-by-step."
- *Repeat 500 times.
The result?** A $2,400 API bill in one night. Not because the AI wasn't smart, but because LLMs are probabilistic, not deterministic. Relying on them for logic flow is like letting an infant play with a chainsaw.
Why "Context" is a Lie 🤥
We tend to trust the "100k context window," but research (like the Lost in the Middle paper) proves that LLMs are terrible at retrieving specific instructions buried in long conversation histories.
As your agent loops, the context grows. As the context grows, the AI's IQ drops. It's a self-fulfilling prophecy of failure.
The Fix: Workflows > Agents
We scrapped the "Autonomous" model and moved to Deterministic Workflows.
We stopped asking the AI to "figure it out" and started using it as a dumb, isolated processing unit within a strict code structure.
In my full technical breakdown, I explain:
The "Circuit Breaker" pattern to kill loops before they burn cash.
Structured Outputs: Why JSON enforcement is your only safety net.
The Architecture: A whiteboard sketch of how we moved from chaos to a stable production pipeline.
🛑 Stop burning credits. Read the full architecture breakdown on my blog:
👉 Read: Agentic AI Is a Mess (And What Actually Works in 2026)
P.S. If you've ever had an AI agent go rogue in production, let me know in the comments. I need to know I'm not alone!
Top comments (1)
I'm curious to hear from the community: Have you ever experienced an 'infinite reasoning loop' with agents like CrewAI or LangChain that burned more tokens than expected? How did you solve it? Was it a circuit breaker or a architectural shift?