AI agents — autonomous systems that can plan, use tools, and execute multi-step workflows — have become the hottest category in enterprise software. But our survey of 200 engineering leaders reveals a more nuanced picture than vendor marketing would suggest.
What's Actually Deployed
78% of respondents have deployed at least one AI agent in production. But dig deeper and the picture shifts: the vast majority (65%) are 'simple' agents — essentially LLM-powered chatbots with access to internal knowledge bases and a few API integrations. True autonomous agents that execute multi-step workflows with minimal human oversight account for only 12% of deployments.
The Use Cases That Work
Three categories dominate successful deployments: customer support triage (41% of respondents), code review and documentation (33%), and internal knowledge search (29%). These share common traits — well-defined scope, easy-to-verify outputs, and graceful degradation when the agent fails.
The Reliability Gap
The biggest challenge cited by 72% of respondents: reliability at scale. An agent that works 95% of the time in demos fails spectacularly when processing thousands of requests daily. The 5% failure rate becomes hundreds of broken workflows, confused customers, or incorrect actions. Teams that succeed invest heavily in guardrails, output validation, and human-in-the-loop checkpoints.
Advice from the Trenches
Start narrow, validate obsessively, and build your monitoring before your agent. The teams with the best outcomes treat agent development more like deploying a new hire than shipping a feature — with onboarding, supervision, and gradual autonomy increases.
