The latest articles on DEV Community by Sheikh Saif Ali (@sheikhsaifali_). https://dev.to/sheikhsaifali_ https://media2.dev.to/dynamic/image/width=90,height=90,fit=cover,gravity=auto,format=auto/https:%2F%2Fdev-to-uploads.s3.amazonaws.com%2Fuploads%2Fuser%2Fprofile_image%2F3894604%2Fe3c5afa4-dbea-48f7-a8c2-84815990baa0.jpg https://dev.to/sheikhsaifali_ en Sheikh Saif Ali Thu, 23 Apr 2026 18:14:04 +0000 https://dev.to/sheikhsaifali_/exploring-llm-based-deep-search-agents-4dc4 https://dev.to/sheikhsaifali_/exploring-llm-based-deep-search-agents-4dc4 <p>Hello everyone <br> I’m Sheikh Saif Ali, and in this second blog I am discussing:</p> <p>“A Survey of LLM-based Deep Search Agents (2026)”</p> <p>Tagging for feedback:<br> <a class="mentioned-user" href="https://dev.to/raqeeb_26">@raqeeb_26</a></p> <h2> What Are Deep Search Agents? </h2> <p>Deep Search Agents go further by:</p> <ul> <li>Understanding the question</li> <li>Searching multiple times</li> <li>Evaluating sources</li> <li>Producing final answers</li> </ul> <p>They behave more like a human researcher.</p> <h2> Goal of the Paper </h2> <p>The paper explains:</p> <ul> <li>How deep search agents work</li> <li>Architectures used</li> <li>Strengths of LLMs</li> <li>Current limitations The goal is to show how search is becoming more intelligent.</li> </ul> <h2> Connection with Our Course </h2> <p>Our course covers:</p> <ol> <li>Search algorithms</li> <li>A*</li> <li>Heuristics</li> <li>Intelligent agents</li> </ol> <p>This paper connects directly because:</p> <h4> Traditional A*: </h4> <p>Uses fixed heuristics</p> <h4> Deep Search Agents: </h4> <p>Learn better search strategies dynamically<br> So AI is moving beyond classical search techniques.</p> <h2> How Deep Search Works </h2> <p>The paper describes a cycle:</p> <h4> 1. Query Understanding </h4> <p>Agent interprets user intent.</p> <h4> 2. Planning </h4> <p>Decides what to search first.</p> <h4> 3. Retrieval </h4> <p>Collects information.</p> <h4> 4. Evaluation </h4> <p>Checks relevance.</p> <h4> 5. Refinement </h4> <p>Searches again if needed.<br> This creates a smarter search system.</p> <h2> My Personal Insight </h2> <p>While reading this paper and using NotebookLM, I learned:<br> Good AI search is not just finding information it is reasoning about information.<br> That idea changed my understanding of AI systems.</p> <h2> Challenges Mentioned </h2> <p>Some issues include:</p> <ul> <li>Wrong source selection</li> <li>Bias in retrieved data</li> <li>High token cost</li> <li>Slow performance</li> </ul> <p>These still need improvement.</p> <h2> Real Applications </h2> <p>Deep search agents can help in:</p> <ul> <li>Academic research</li> <li>Medical research</li> <li>Legal systems</li> <li>Software development</li> <li>Knowledge management</li> </ul> <h2> Conclusion </h2> <p>Deep Search Agents are changing the future of information retrieval.<br> Instead of showing information, AI can now understand, evaluate, and explain information.<br> This paper helped me see how search algorithms are evolving into intelligent systems.</p> agents ai llm nlp Sheikh Saif Ali Thu, 23 Apr 2026 18:06:56 +0000 https://dev.to/sheikhsaifali_/the-rise-of-agentic-ai-47gl https://dev.to/sheikhsaifali_/the-rise-of-agentic-ai-47gl <p>Hello everyone,<br> I’m Sheikh Saif Ali, a Computer Science student exploring modern Artificial Intelligence concepts. In this post, I am sharing my understanding of the paper:</p> <p>“The Rise of Agentic AI: A Review of Definitions, Frameworks, and Challenges (2025)”</p> <p>Tagging for feedback:<br> <a class="mentioned-user" href="https://dev.to/raqeeb_26">@raqeeb_26</a></p> <h2> What Is Agentic AI? </h2> <p>Traditional AI usually waits for a user prompt and then responds.</p> <p>Agentic AI is different because it can:</p> <p>Set goals Make plans Use tools Evaluate outcomes Improve actions</p> <p>This means AI is moving from being a simple assistant toward becoming an autonomous system.</p> <h2> Goal of the Paper </h2> <p>The main goal of this paper is to explain:</p> <p>What Agentic AI means How agentic systems work Their advantages Their challenges</p> <p>The researchers show that AI systems are becoming capable of independent problem solving.</p> <h2> Connection with Our Course </h2> <p>In our AI course we studied:</p> <p>Intelligent agents<br> Search methods<br> Problem solving systems</p> <p>This paper extends that concept.</p> <p>A normal agent:<br> Perceive → Decide → Act</p> <p>Agentic AI:<br> Perceive → Plan → Use Tools → Learn → Improve</p> <p>This makes the agent more advanced than traditional AI systems.</p> <h2> Main Components of Agentic AI </h2> <h3> 1.Planning </h3> <p>The agent breaks a task into smaller steps.</p> <h3> 2. Memory </h3> <p>Stores previous information for better decisions.</p> <h3> 3. Tool Usage </h3> <p>Can interact with:</p> <ul> <li>1. Search engines</li> <li>2. APIs</li> <li>3. External software</li> </ul> <h3> 4. Reflection </h3> <p>Checks its own answers and corrects mistakes.</p> <h2> My Personal Insight </h2> <p>After reading this paper manually and reviewing parts in NotebookLM, I noticed something interesting:</p> <p>The future of AI is not only answering questions but taking initiative.</p> <p>That was the most exciting idea for me.</p> <h2> Challenges Mentioned </h2> <p>The paper discusses:</p> <ol> <li>- Hallucination</li> <li>- Poor reliability</li> <li>- Safety concerns</li> <li>- High computing cost</li> </ol> <p>These challenges must be solved before wide adoption.</p> <h2> Real Applications </h2> <p>Agentic AI can be used in:</p> <ul> <li>Medical diagnosis</li> <li>Research assistants</li> <li>Coding assistants</li> <li>Business automation</li> </ul> <h2> Conclusion </h2> <p>Agentic AI represents a major shift in artificial intelligence. It transforms AI from a passive system into an active problem solver.</p> <p>This paper helped me understand how future AI systems may work independently in real-world environments.</p>