Kushagra Sharma's Blog Posts

Kushagra Sharma

Kushagra Sharma is a Product Manager at Cisco working on AIOps products that help teams operate complex systems with intelligence and scale. He focuses on translating customer problems into practical, data-driven solutions at the intersection of AI, reliability, and software platforms. Passionate about clear thinking and strong product craft, Kushagra writes to share insights on technology, product strategy, and building systems that actually work in the real world.

Steganography: Hidden in plain sight
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5 Minute Read

Steganography: Hidden in plain sight

Learn how digital steganography allows attackers to hide malicious data in plain sight. Discover how this technique bypasses security and how to detect it using behavioral analysis.
F1 Score in AI Evaluation: How to Balance Precision and Recall
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8 minutes

F1 Score in AI Evaluation: How to Balance Precision and Recall

F1 score provides a more reliable performance metric than accuracy for imbalanced AI tasks, such as toxicity detection and safety classification.
10 Common Hallucinations: When Bad AI Impacts Trust and Revenue
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8 Minute Read

10 Common Hallucinations: When Bad AI Impacts Trust and Revenue

Hallucinations in autonomous agents create business liability. Learn how teams can verify model outputs against certified source data, in a systematic way.
7 LLM Metrics to Enhance AI Reliability
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7 Minute Read

7 LLM Metrics to Enhance AI Reliability

Measure LLM reliability and performance using seven key metrics to isolate system health from generation quality.
Why the Smartest Enterprise AI Strategy Starts with Tokenomics and Not Giant Models
Cio Office
8 Minute Read

Why the Smartest Enterprise AI Strategy Starts with Tokenomics and Not Giant Models

Discover why bigger is not always better in enterprise AI. Learn how tokenomics and specialized small language models help reduce cost and latency.
AI Agent Systems: A Field Guide To Types and Levels
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8 Minute Read

AI Agent Systems: A Field Guide To Types and Levels

This field guide categorizes AI agents across seven levels of autonomy, providing a roadmap for mapping system architectures against common failure modes and production-ready requirements.