Category
AI Agents
Autonomous agents, LLM applications, and intelligent systems
// 12 articles filed
// FIG. CAT-AI-A
PristrenPristrenAutonomous agents, LLM applications, and intelligent systems
// 12 articles filed
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LlamaIndex is purpose-built for RAG and document Q&A. Here is how its core components work and when to choose it over LangChain.
Mahmudul Haque Qudrati
CEO & ML Engineer
Deploying agents to production reveals failure modes that benchmarks never show. Here is what actually breaks and the patterns that keep agents stable under real conditions.
Mahmudul Haque Qudrati
CEO & ML Engineer
Task completion rate alone misses most of what matters in agent evaluation. Here is how to measure trajectory quality, cost efficiency, error recovery, and build your own eval suite.
Mahmudul Haque Qudrati
CEO & ML Engineer
Computer use agents can click, type, and navigate a real desktop. Here is what the technology can actually do, where it still fails, and when it beats a proper API integration.
Mahmudul Haque Qudrati
CEO & ML Engineer
Three tools claim to be AI software engineers. Here is an honest comparison of what each actually does well, what the benchmark numbers mean, and when to reach for each one.
Mahmudul Haque Qudrati
CEO & ML Engineer
Tool use is how LLMs take actions in the world. These design patterns make the difference between an agent that works reliably and one that hallucinates parameters and loops forever.
Mahmudul Haque Qudrati
CEO & ML Engineer
Agents without memory repeat themselves, forget context, and fail on multi-session tasks. Here is how short-term, long-term, and episodic memory work and how to implement each.
Mahmudul Haque Qudrati
CEO & ML Engineer
Browser agents let LLMs control a real web browser to navigate, click, fill forms, and extract data. Here is how they work, when they are worth the cost, and when they are not.
Mahmudul Haque Qudrati
CEO & ML Engineer
An AI agent is an LLM that can take actions and loop until a goal is achieved. The four components, the ReAct loop, what production agents actually do, and honest limits.
Mahmudul Haque Qudrati
CEO & ML Engineer
Building an AI agent requires an LLM with tool calling and a loop that runs until completion. Five steps with working code, common failure patterns, and when to use a framework vs. build from scratch.
Mahmudul Haque Qudrati
CEO & ML Engineer
Multi-agent systems coordinate specialized agents to handle tasks too complex for one agent. Four coordination patterns, real use cases, frameworks, and the hard problems that come with distribution.
Mahmudul Haque Qudrati
CEO & ML Engineer
Assistants respond to requests. Agents pursue goals autonomously. The technical differences, when you actually need an agent vs. an assistant, and an honest 2026 state-of-the-art.
Mahmudul Haque Qudrati
CEO & ML Engineer
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