Engineering Reliable Autonomy

Despite impressive benchmark scores and polished demos, many AI agents fail when deployed into real-world, continuous environments.

Why? Because traditional evaluation methods measure isolated performance—not systemic resilience.

In this webinar, we unpack the growing gap between “demo-grade” intelligence and “system-grade” assurance. Drawing on emerging research and enterprise case studies, we explore the hidden failure mode known as the snowball effect, where small early-stage errors cascade into large-scale system breakdowns over time.

At the core of this discussion is a new way of thinking about risk in agentic systems, including the Cascading Risk Priority Number:
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This model highlights how upstream failures amplify downstream impact—shifting the key question from “Did the agent complete the task?” to “Did the agent degrade the system?”

We will also introduce practical governance approaches, including:

• Behavioral Entropy and the need to manage distributions of outcomes, not single outputs
• The BME Metric Suite for measuring system-level reliability
• The T0–T5 Safety Switch framework for real-time intervention and control
• The critical distinction between Harness Engineering and true Agentic Engineering

Whether you are deploying AI in regulated industries or scaling autonomous workflows, this session will challenge conventional thinking and provide a roadmap for building AI systems that don’t just perform—but endure.

Key takeaway: Success in AI is no longer about model accuracy—it’s about whether your system can survive its own intelligence.