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Your agent demo works. That's the trap.
Building AI agents for companies reveals a common problem: the gap between a successful demo and reliable production performance. This discrepancy is primarily due to compounding probability, not model limitations. Even with a high per-step reliability, chaining multiple steps significantly reduces end-to-end success rates. A demo typically showcases a single, ideal scenario, masking the real-world complexities of production.
Failures within an agent's steps often go unnoticed because they produce plausible-looking, though incorrect, outputs. Individual steps may appear sound in isolation, propagating errors silently through the chain. The common diagnosis of "hallucination" is frequently inaccurate, as models simply process the data they receive. Context quality, rather than sheer size, is a critical limiter for agent performance, with older information becoming buried.
To improve reliability, focus on robust system engineering rather than just prompt optimization. Implementing state checkpointing allows for resuming interrupted processes, avoiding costly restarts. Validating inputs and outputs at each step catches errors early, preventing them from corrupting downstream operations. Making side effects idempotent is crucial for handling retries with non-deterministic workers.
Integrating evaluation into the continuous integration pipeline treats agent behavior like code prone to regression. Ultimately, transforming a slick demo into a production-ready system requires unglamorous engineering disciplines like error handling and state management. The core issue is often treating an agent as a simple prompt instead of a complex system.
