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AI agents are easy to demo but hard to deploy at scale – learn the engineering frameworks for reliability, observability, and safety that make production-ready autonomous agents possible.
Building AI agents that work in demos is easy – making them production-ready is hard. João Freitas, VP of Engineering for AI And Automation at PagerDuty, shares hard-won lessons from deploying autonomous AI agents at scale. Discover the complexities and risks that derail most agent projects, and learn practical frameworks for reliability, observability, and evaluation that separate successful deployments from failures. From multi-agent architectures to guardrails against prompt injection, hear the engineering realities behind AI agents that diagnose incidents, automate remediation, and operate safely in enterprise environments. These are essential insights for anyone building agents beyond the prototype stage.
Key takeaways
Practical Engineering Realities: Real-world agent deployment for incident diagnosis and automated remediation demands specific technical approaches that separate successful implementations from failures.
Production vs. Prototype Gap: Building demo AI agents is straightforward, but production deployment requires robust frameworks for reliability, observability, and evaluation to handle real-world complexity and risk.
Multi-Agent Architecture & Safety: Successful enterprise AI agents need carefully designed multi-agent systems with guardrails against threats like prompt injection to operate safely at scale.
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