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Agentic systems in production: planning loops, tool calling, MCP, memory, orchestration, and the failure modes that only show up once real users arrive.
10 articles tagged ai agents. Related categories: Artificial Intelligence, Natural Language Processing, Career Advancement.
The evolution from static chatbots to autonomous agents requires a shift in how we handle external data. This article explores agentic workflows using Model Context Protocol (MCP) and tool calling to build reliable systems that can interact with complex environments, emphasizing architecture, security, and the reduction of hallucination through structured protocols.
· 10 min read
Multimodal AI has transitioned from experimental research to a core component of enterprise architecture. This technical guide explores how to integrate documents, audio, and visual data into production pipelines, focusing on model selection, vector database orchestration, and the practical trade-offs between late fusion and joint-embedding architectures in 2026 systems.
· 9 min read
Moving beyond anecdotal testing is the first hurdle in production LLM development. This guide outlines how to build a robust offline evaluation suite using deterministic checks, model-graded metrics, and golden datasets. Learn to implement scoring functions that provide consistent, reproducible signals for your RAG pipelines and agentic workflows.
· 9 min read
Modern data science roles in 2026 have shifted from basic model building to production-grade system design. This article breaks down the essential skill stack, focusing on LLM orchestration, vector databases, and the move toward compound AI systems that hiring managers prioritize in technical interviews and portfolio reviews.
· 10 min read
A technical deep dive into the architecture of AI agents, moving beyond basic LLM wrappers. We examine the mechanics of planning, tool-calling, and state management, providing data scientists with the architectural patterns and evaluation strategies required to build reliable, autonomous systems for production environments.
· 11 min read
In this article, we will explain what an AI agent is, how it works, and why it’s driving the next big wave in tech innovation. You’ll also learn what sets AI agents apart from traditional automation and how to get started with hands-on learning through an AI agent bootcamp. Whether you’re a…
· 4 min read
Introduction Adaptive agents are revolutionizing the way tasks are performed in artificial intelligence (AI). These intelligent systems are designed to learn, evolve, and respond dynamically to changing environments, making them invaluable for solving complex, real-world problems. Unlike static…
· 10 min read
Large Language Models (LLMs) have transformed artificial intelligence by enabling natural language understanding, text generation, and automated decision-making. However, one of their biggest challenges is hallucination—a phenomenon where AI generates incorrect, misleading, or entirely…
· 11 min read
Over the last decade, Artificial Intelligence (AI) has been significantly reshaped, and now multi-agent AI systems take the lead as the most powerful approach to solving complex problems. They are based on a system that features multiple autonomous agents cooperating in enhancing reasoning,…
· 9 min read
Businesses are leaning heavily on AI agents these days. According to a study, 77% of companies are either currently using AI in their businesses or exploring its use cases. This number shows how AI agents are changing the business landscape. But building reliable AI agents is a huge challenge.…
· 10 min read
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