We Spent $47,000 Running AI Agents in Production. Here's What Nobody Tells You About A2A and MCP.
The team deployed a multi-agent LangChain system to production believing it would run smoothly, but no guardrails existed to detect runaway agent behavior, and two agents entered an infinite loop that went undetected for 11 days.
Two agents in the production multi-agent system got stuck in an infinite conversation loop for 11 days, accumulating $47,000 in API costs before the team shut it down.
The team shut down the system after $47,000 in total API costs, with weekly costs escalating from $127 in week 1 to $18,400 in week 4.
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Frequently asked questions
What did this team achieve with this AI workflow?
The team shut down the system after $47,000 in total API costs, with weekly costs escalating from $127 in week 1 to $18,400 in week 4.
What tools did this team use?
LangChain, A2A, MCP.
What results were reported?
Total API costs: $47,000; Week 1 API costs: $127; Week 2 API costs: $891; Week 3 API costs: $6,240 (source-reported, not independently verified).
What failed first in this deployment?
Two agents in the production multi-agent system got stuck in an infinite conversation loop for 11 days, accumulating $47,000 in API costs before the team shut it down.
How is this workflow AI workflow structured?
Multi-agent system deployment → Agent-to-agent coordination → Infinite loop failure → System shutdown.