u/Comfortable_Way8312

I'm doing research into production AI agent systems and trying to separate real-world problems from demo-level success.

A lot of agent demos look impressive until they hit:

• long-running workflows
• inconsistent tool outputs
• permission boundaries
• retries/recovery
• memory drift
• context loss
• hidden hallucinations
• orchestration complexity

What surprised me is that the actual “reasoning” often isn’t the biggest problem.

The bigger issues seem to be:

• reliability
• state management
• workflow continuity
• evaluation/testing
• governance
• infrastructure costs

For people actually running agents in production (or even serious internal tooling):

• what stack are you using?
• what works better than expected?
• what constantly breaks?
• what problem became bigger than you originally thought?

Especially curious about:

• memory systems
• multi-agent coordination
• long-term context
• human approval flows
• observability/debugging

Would love to hear real experiences rather than hype.
Even failed experiments are useful.