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Avoid the "AI Slop" trap. From runaway costs to memory poisoning, here are the 7 most common failure modes of Agentic AI (and how to fix them).
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Highlights deployment patterns that consistently deliver value: start assistive then automate, use specialised multi-agent teams, and go event-driven
Details common failure modes: unclear goals, over-promising capabilities, messy data, integration gaps, runaway token costs – and how to mitigate them
Provides a checklist to stress-test agent projects before scaling, so you can avoid being part of the “cancelled by 2027” statistic