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The buzz around Generative AI (GenAI) is undeniable. From crafting compelling marketing copy to generating intricate code, Large Language Models (LLMs) are rapidly transforming workflows. However, beneath the surface of impressive text generation lies a critical challenge: reliability and factual accuracy. Standalone LLMs, trained on vast but static datasets, can hallucinate or provide outdated information,
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The rise of Agentic AI represents a paradigm shift in artificial intelligence—from passive tools to proactive, autonomous entities capable of planning, adapting, and collaborating. As foundational models become more capable, the next frontier lies in building super agents—AI systems that don’t just answer queries, but manage workflows, make decisions, learn from experience, and work alongside
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Machine learning in production isn’t just about better models — it’s about better data pipelines. And at the heart of these pipelines lies the feature store — the place where ML features live. But in today’s real-time world, batch-driven feature stores fall short. You can’t power fraud detection, recommendations, or personalization with yesterday’s data. Enter:
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As AI agents evolve from simple task performers to autonomous collaborators, they require more than just prompt chaining or API access — they need cognition. Just as humans rely on memory, attention, planning, and reflection to navigate the world, agents too must coordinate multiple mental faculties to act intelligently. This is where cognitive architectures come
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Apache Kafka is the heartbeat of modern data platforms — powering everything from payment systems to recommendation engines. But while getting Kafka running is easy, scaling it to handle billions of events per day — without falling over — takes strategy, precision, and real-world experience. In this post, we’ll go beyond the basics and share
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Welcome back to AI Agents Unleashed! As we build more autonomous, proactive AI agents, memory becomes a central challenge — not just what to remember, but how to do it efficiently. Storing every interaction, document, and data point isn’t just expensive — it’s unnecessary. Instead, the best agents are those that remember the right things
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Apache Kafka has become the backbone of real-time data architectures. At its core lies Kafka Streams, a powerful client library that enables real-time processing of data streams directly within your applications. But one fundamental decision developers face is: Should I build my stream processing logic as stateless or stateful? Understanding the tradeoffs between stateless and
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Hey everyone, and welcome back to “AI Agents Unleashed”! We’ve talked a lot about the incredible potential of AI agents – those proactive, intelligent systems that can plan, execute, and even learn. But what truly elevates an AI agent from a sophisticated program to a truly “smart” and adaptable collaborator? The answer lies in their
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As agentic AI matures—from experimental assistants to autonomous, task-oriented agents—enterprises are eager to unlock its potential. Yet, large-scale deployment across complex business environments remains elusive. The promise of intelligent automation, workflow orchestration, and decision support often clashes with entrenched systems, regulatory expectations, and organizational inertia. This article explores the key barriers to enterprise-scale agentic workflows
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