As enterprises increasingly rely on distributed architectures, cloud platforms and AI-powered applications, system failures pose growing business risks. Traditional testing cannot fully replicate the unpredictability of production environments. This whitepaper explores how chaos engineering uses controlled failure experiments to uncover weaknesses, validate recovery capabilities and strengthen resilience before incidents affect customers.

AI introduces additional challenges, including silent model degradation, stale or corrupted data, pipeline failures and disruptions to external AI services. The whitepaper examines why resilience testing must extend beyond infrastructure availability to validate the reliability of AI outputs, data pipelines and automated decisions.

It outlines a structured framework combining failure mode and effects analysis (FMEA), risk prioritization, phased adoption and continuous validation. It also explores how AI and Model Context Protocol (MCP) can support experiment design, dependency discovery, controlled execution and analysis, with safeguards to limit impact.

Discover HCLTech eCHAOS approach to strengthening resilience across traditional applications and AI-powered systems. Download the whitepaper to learn how disciplined experimentation can improve operational readiness, reduce business risk and build confidence in how systems perform, fail and recover.