How AI Solves Complex Problems

A lot of people use ChatGPT or Claude every day without realizing that very different kinds of reasoning systems can be running underneath. They type a question, get an answer, and move on. But if you are building with AI, understanding what is happening under the hood actually matters. A simple way to think about it is in three layers, which you can see in this diagram. Layer 1: Statistical Pattern Matching (The Transformer Foundation) At the base level, LLMs rely on the transformer architecture and attention mechanisms. Tokens attend to each other through attention weights, allowing the model to capture relationships between words and generate coherent text. This layer is extremely good at pattern recognition and language generation. It powers fast responses and works well for tasks like summarization, translation, and straightforward questions. But pattern recognition alone does not guarantee deep reasoning. Layer 2: Explicit Reasoning Techniques (The “Thinking” Layer) To improve reasoning, newer systems add techniques like Chain-of-Thought prompting, process supervision using reward models, and exploration of multiple reasoning paths. Instead of jumping directly to an answer, the model breaks problems into intermediate steps and evaluates them along the way. This structured reasoning process significantly improves performance on complex tasks like math, logic, and multi-step analysis. Layer 3: Hybrid Reasoning Systems (Neuro-Symbolic + Tools) The most advanced systems combine LLM reasoning with external tools and symbolic methods. The model may call calculators, execute code, query knowledge graphs, or translate language into formal logic that a symbolic solver can evaluate. These hybrid architectures allow AI systems to handle problems that pure language models struggle with. The key takeaway is this: the same model can behave very differently depending on how much reasoning, compute, and tooling you allow it to use. If you are building AI systems, the question is not just which model you choose. It is how you design the reasoning pipeline around it. That is where most of the performance gains actually come from.

Ever wonder how AI agents solve problems one step at a time? 🤔 🔧 𝗧𝗵𝗲 𝗣𝗿𝗼𝗯𝗹𝗲𝗺: Traditional AI assistants often stumble on complex, multi-step issues – they might give a partial answer, hallucinate facts that don't exist, deliver less accurate results, or miss a crucial step. 🧠 𝗧𝗵𝗲 𝗦𝗼𝗹𝘂𝘁𝗶𝗼𝗻: Agentic AI systems with 𝘀𝗲𝗾𝘂𝗲𝗻𝘁𝗶𝗮𝗹 𝘁𝗵𝗶𝗻𝗸𝗶𝗻𝗴 to handle complexity by dividing the problem into ordered steps, assigning each to the most relevant expert agent. This structured handoff improves accuracy, minimizes hallucination, and ensures each step logically builds on the last. 📐𝗖𝗼𝗿𝗲 𝗣𝗿𝗶𝗻𝗰𝗶𝗽𝗹𝗲: By focusing on one task at a time, each agent produces a reliable result that feeds into the next—reducing surprises and increasing traceability. ⚙️ 𝗞𝗲𝘆 𝗖𝗵𝗮𝗿𝗮𝗰𝘁𝗲𝗿𝗶𝘀𝘁𝗶𝗰𝘀 • Breaks complex problems into sub-tasks • Solves step-by-step, no skipped logic • Adapts tools or APIs at each stage 🚦𝗔𝗻𝗮𝗹𝗼𝗴𝘆: - Think of a detective solving a case: they gather clues, then interview witnesses, then piece together the story, step by step. No jumping to the conclusion without doing the groundwork. 💬 𝗥𝗲𝗮𝗹-𝗪𝗼𝗿𝗹𝗱 𝗘𝘅𝗮𝗺𝗽𝗹𝗲 - 𝘊𝘶𝘴𝘵𝘰𝘮𝘦𝘳 𝘚𝘶𝘱𝘱𝘰𝘳𝘵 𝘚𝘤𝘦𝘯𝘢𝘳𝘪𝘰: A user contacts an AI-driven support agent saying, “My internet is down.” A one-shot chatbot might give a generic reply or an irrelevant help article. In contrast, a sequential-processing support AI will tackle this systematically: it asks if other devices are connected → then pings the router → then checks the service outage API → then walks the user through resetting the modem. Each step rules out causes until the issue is pinpointed (say, an outage in the area). This real-world approach mirrors how a human support technician thinks, resulting in far higher resolution rates and user satisfaction. 🏭 𝗜𝗻𝗱𝘂𝘀𝘁𝗿𝘆 𝗨𝘀𝗲 𝗖𝗮𝘀𝗲 - 𝘐𝘛 𝘛𝘳𝘰𝘶𝘣𝘭𝘦𝘴𝘩𝘰𝘰𝘵𝘪𝘯𝘨: Tech companies are embedding sequential agents in IT helpdesk systems. For instance, to resolve a cybersecurity alert, an AI agent might sequentially: verify the alert details → isolate affected systems → scan for known malware signatures → quarantine suspicious files → document the incident. 📋 𝗣𝗿𝗮𝗰𝘁𝗶𝗰𝗮𝗹 𝗖𝗵𝗲𝗰𝗸𝗹𝗶𝘀𝘁 ✅ Great for complex problems that can be broken into smaller steps. ✅ Useful when you need an explanation or audit trail of how a decision was made. ✅ When workflows involve multiple dependencies that must be followed in a defined order. ❌ Inefficient for tasks that could be done concurrently to save time. ❌ Overkill for simple tasks where a direct one-shot solution works fine. #AI #SRE #AgenticLearningSeries

Yesterday, OpenAI released its latest model, O1, which marks a significant leap from chatbots to sophisticated reasoners. Named to indicate that it is resetting ‘the counter back to 1’, O1 pushes the industry beyond conversational AI to models capable of solving complex, multistep problems with near-human-level reasoning. 🧠 Reason: O1 excels at complex tasks. On a qualifying exam for the International Mathematics Olympiad, it correctly solved 83% of problems compared to GPT-4o’s 13%. In Codeforces programming contests, it ranks in the 89th percentile, with claims it will soon rival PhD students in physics, chemistry, and biology. 💸 Expensive: Quality comes at a cost. At 3-4x the cost of GPT-4o, O1-preview comes with a hefty price tag—$15 per million input tokens (vs $5), $60 per million output tokens (vs $15) 🐢 Slow: O1 mirrors human problem-solving, taking extra time to think through responses. This deliberate "thinking time" may slow performance but significantly enhances accuracy. 🚧 Limitations: O1 struggles with factual knowledge, lacks browsing capabilities, and can’t process images or files. While it reduces hallucinations, they’re not entirely gone. The cost-performance trade-off is real: not all tasks will need O1’s advanced reasoning, and its higher cost and latency might limit its applicability. But for use cases demanding deeper problem-solving, the potential is enormous. For those, the question isn’t how quickly AI can respond—it’s how deeply it can think. As one OpenAI researcher noted, O1 thinks in seconds—future models might think in hours, days, or even weeks, solving world-changing problems like curing cancer or innovating new battery technologies. It’s clear the future of AI isn’t about one model to rule them all. To each use-case, its own solution!

OpenAI's new BrowseComp benchmark reveals that while current AI systems struggle with finding information that challenges humans, specialized browsing agents like Deep Research now solve over half of these difficult problems. BrowseComp tests AI's ability to find answers requiring extensive searching across websites and connecting complex clues. The benchmark contains 1,266 questions deliberately crafted to be challenging—human testers were asked to create tasks difficult enough that others couldn't solve them within 10 minutes. The results reveal a clear progression: * "Regular AI" (GPT-4o): 0.6% accuracy * GPT-4o with basic browsing: 1.9% accuracy * OpenAI o1 (no browsing, better reasoning): 9.9% accuracy * Deep Research (specialized browsing agent): 51.5% accuracy Human testers given up to 2 hours solved only 29.2% of these problems. When they solved problems, it took varying amounts of time—some under an hour, others the full 2 hours.

🔥 Why Half of Agentic Projects Still Fail (And the 4 Patterns That Actually Work) The future is agentic but without the right architecture, you're setting up for disappointment. Quick pattern design framework to execute successful AI @ work Pattern #1: The Self-Checking System - The problem: AI confidently delivers wrong answers. - The solution: Build in quality checks. How it works: After generating output, the AI reviews its own work with prompts like "Check this response for accuracy" or "What assumptions might be incorrect?" Apply here: Content teams use this for fact-checking articles. Legal teams apply it to contract reviews. Marketing teams validate campaign copy. Try this: Add "Please review your answer for potential errors" to any complex AI request. Pattern #2: The Connected Intelligence - The problem: Your AI operates in a data vacuum. - The solution: Connect it to live systems and APIs. How it works: AI agents call external tools; web search for research, databases for current information, APIs for system integration. Apply here: Customer service bots that check order status, scheduling assistants that access calendars, research tools that pull live market data. Try this: Start by connecting your AI to one external data source this week. Pattern #3: Planner Approach - The problem: AI jumps to conclusions without thinking through the process. - The solution: Force systematic planning before execution. How it works: Before starting, the AI creates a step-by-step approach: define objectives → gather requirements → outline methodology → execute → review. Apply here: Financial modeling (plan analysis framework first), content strategy (outline before writing), project management (break down complex tasks). Try this: Ask "What's your step-by-step plan to solve this?" before any multi-part request. Pattern #4: Multi-agent collaboration - The problem: One AI trying to be everything to everyone. - The solution: Deploy specialized agents for different capabilities. How it works: Different agents handle their areas of expertise; one for data analysis, another for writing, another for fact-checking and then consolidate their outputs. Apply here: Research projects using separate agents for data gathering, analysis and report writing. Product development with agents for market research, technical feasibility and competitive analysis. Multi-agent approach is more complex to manage but often superior results for multifaceted challenges. Most successful implementations combine patterns: • Customer support: Tool Use (CRM access) + Reflection (response validation) • Content creation: Planning (strategy first) + Reflection (quality check) • Business analysis: Multi-agent (specialists) + Tool Use (data sources) + Planning (structured approach) Pick the pattern that addresses your biggest AI challenge. Test it on one workflow this week. Success isn't about the latest AI model; it's about thoughtful architectural choices. #AIinWork

How to build a cost-effective AI system that can reason and compute (with code) ⬇️ 𝗧𝗵𝗲 𝗽𝗿𝗼𝗯𝗹𝗲𝗺 Last week, while my 4-year-old Kai was munching nectarines, I pitched him what I thought was a fun little problem: "Imagine you and Sofia are at the beach. Sofia swims at 2 km/h while you paddle at 4 km/h in the same direction. How far apart will you be in 2 hours?" His response? "Daddy, can we just go to the actual beach instead?" 😂 Fair point, kid. But this got me thinking about AI reasoning capabilities. 𝗧𝗵𝗲 𝗥𝗲𝗮𝗹 𝗖𝗵𝗮𝗹𝗹𝗲𝗻𝗴𝗲: 𝗦𝗰𝗮𝗹𝗶𝗻𝗴 𝗖𝗼𝗺𝗽𝗹𝗲𝘅 𝗣𝗿𝗼𝗯𝗹𝗲𝗺-𝗦𝗼𝗹𝘃𝗶𝗻𝗴 I decided to test Large Language Models with a more sophisticated version: "Kai and Sofia start at the same beach point. Sofia swims toward a buoy 7 km offshore at a 41° angle, moving at 2 km/h but ocean currents push her sideways at 2 km/h perpendicular to her direction. Kai paddles along the shoreline at 5 km/h for the first hour, then turns and paddles directly toward Sofia's current position at 1 km/h. After 2 total hours, what's the distance between them?" Now we're talking! This requires: > Trigonometry skills for tracking Sofia's actual path with currents > Multi-step calculations for positions at different time intervals > Conditional logic for Kai's direction change > Vector mathematics for component calculations > Distance formulas for final separation 𝗪𝗵𝘆 𝗧𝗵𝗶𝘀 𝗠𝗮𝘁𝘁𝗲𝗿𝘀 𝗳𝗼𝗿 𝗠𝗟 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝘀 Sure, GPT-4 could probably solve this. But what if you need to solve thousands of these problems? What if you're building a tutoring system or mathematical reasoning benchmark? This is exactly the challenge I'm tackling in my latest project series. Here's my approach: 1. Set Up the Right Tools Using BAML for structured LLM outputs and Opik for evaluation - essential for building reliable systems that actually use LLM output effectively. 2. Generate Evaluation Dataset Before diving into complex agent workflows, establish clear success criteria. I built a Python function that maps problem parameters to exact solutions. 3. Build Strong Baselines Started with Claude Sonnet 4 as a baseline. No external data needed, pure reasoning and arithmetic. 4. Measure What Matters Implemented two key metrics: > RelativeErrorMetric: How far off is the answer? > WithinBoundsMetric: How close to acceptable range? 𝗧𝗵𝗲 𝗥𝗲𝗮𝗹 𝗤𝘂𝗲𝘀𝘁𝗶𝗼𝗻 It's not whether AI can solve complex problems. It's whether we can build practical, scalable solutions that deliver value without destroying budgets. Your users won't be happy with 10-second response times. You won't be happy burning cash to deliver that experience. 𝗡𝗲𝘅𝘁 𝘄𝗲𝗲𝗸 Building something better, faster, and more cost-effective. The Github repo is linked in the comments below ---- Follow Pau Labarta Bajo for high-signal hands-on LLM engineering.

One of the most significant papers last month came from Meta, introducing 𝐋𝐚𝐫𝐠𝐞 𝐂𝐨𝐧𝐜𝐞𝐩𝐭 𝐌𝐨𝐝𝐞𝐥𝐬 (𝐋𝐂𝐌𝐬). While LLMs have dominated AI, their token-level focus limits their reasoning capabilities. LCMs present a new paradigm, offering a structural, hierarchical approach that enables AI to reason and organize information more like humans. LLMs process text at the token level, using word embeddings to model relationships between 𝐢𝐧𝐝𝐢𝐯𝐢𝐝𝐮𝐚𝐥 𝐰𝐨𝐫𝐝𝐬 𝐨𝐫 𝐬𝐮𝐛𝐰𝐨𝐫𝐝𝐬. This granular approach excels at tasks like answering questions or generating detailed text but struggles with maintaining coherence across long-form content or synthesizing high-level abstractions. LCMs address this limitation by operating 𝐨𝐧 𝐬𝐞𝐧𝐭𝐞𝐧𝐜𝐞 𝐞𝐦𝐛𝐞𝐝𝐝𝐢𝐧𝐠𝐬, which represent entire ideas or concepts in a high-dimensional, language-agnostic semantic space called SONAR. This enables LCMs to reason hierarchically, organizing and integrating information conceptually rather than sequentially. If we think of the AI brain as having distinct functional components, 𝐋𝐋𝐌𝐬 𝐚𝐫𝐞 𝐥𝐢𝐤𝐞 𝐭𝐡𝐞 𝐬𝐞𝐧𝐬𝐨𝐫𝐲 𝐜𝐨𝐫𝐭𝐞𝐱, processing fine-grained details and detecting patterns at a local level. LCMs, on the other hand, 𝐟𝐮𝐧𝐜𝐭𝐢𝐨𝐧 𝐥𝐢𝐤𝐞 𝐭𝐡𝐞 𝐩𝐫𝐞𝐟𝐫𝐨𝐧𝐭𝐚𝐥 𝐜𝐨𝐫𝐭𝐞𝐱, responsible for organizing, reasoning, and planning. The prefrontal cortex doesn’t just process information; it integrates and prioritizes it to solve complex problems. The absence of this “prefrontal” functionality has been a significant limitation in AI systems until now. Adding this missing piece allows systems to reason and act with far greater depth and purpose. In my opinion, the combination of LLMs and LCMs can be incredibly powerful. This idea is similar to 𝐦𝐮𝐥𝐭𝐢𝐬𝐜𝐚𝐥𝐞 𝐦𝐨𝐝𝐞𝐥𝐢𝐧𝐠, a method used in mathematics to solve problems by addressing both the big picture and the small details simultaneously. For example, in traffic flow modeling, the global level focuses on citywide patterns to reduce congestion, while the local level ensures individual vehicles move smoothly. Similarly, LCMs handle the “big picture,” organizing concepts and structuring tasks, while LLMs focus on the finer details, like generating precise text. Here is a practical example: Imagine analyzing hundreds of legal documents for a corporate merger. An LCM would identify key themes such as liabilities, intellectual property, and financial obligations, organizing them into a clear structure. Afterward, an LLM would generate detailed summaries for each section to ensure the final report is both precise and coherent. By working together, they streamline the process and combine high-level reasoning with detailed execution. In your opinion, what other complex, high-stakes tasks could benefit from combining LLMs and LCMs? 🔗: https://lnkd.in/e_rRgNH8

The model is the least interesting part of your AI system. What truly matters is how AI is designed, connected, and deployed inside real systems. Modern AI success comes from architecture - how data flows, agents act, and intelligence operates at scale. This guide breaks down the core AI architectures powering today’s intelligent applications and real-world AI systems. Inside this guide, you’ll learn: • RAG - retrieves external knowledge to generate accurate contextual responses • Fine-Tuning - adapts models using specialized datasets for domain-specific performance • Agentic Workflows - enables AI systems to plan and execute multi-step tasks • Multi-Agent Systems - multiple AI agents collaborate to solve complex problems • Event-Driven AI - reacts instantly to triggers and real-time operational signals • Streaming AI - processes continuous data streams for real-time intelligence • Edge AI - runs models locally for low-latency and offline capabilities • Hybrid AI Systems - combines rules, ML models, and LLM reasoning together • AI Pipelines - structures workflows from data preparation to deployment stages • Tool-Augmented LLMs - connects AI with APIs, databases, and external systems • Autonomous Agents - independently plan, reason, and execute long-running goals • Human-AI Collaboration - integrates human oversight for reliability and accountability AI systems don’t scale because of bigger models. They scale because of better architectures. Save this guide for your AI learning and system design reference.

𝘏𝘶𝘮𝘢𝘯 𝘪𝘯𝘵𝘶𝘪𝘵𝘪𝘰𝘯 𝘩𝘢𝘴 𝘭𝘰𝘯𝘨 𝘣𝘦𝘦𝘯 𝘵𝘩𝘦 𝘣𝘢𝘤𝘬𝘣𝘰𝘯𝘦 𝘰𝘧 𝘴𝘤𝘪𝘦𝘯𝘵𝘪𝘧𝘪𝘤 𝘴𝘰𝘧𝘵𝘸𝘢𝘳𝘦 𝘥𝘦𝘷𝘦𝘭𝘰𝘱𝘮𝘦𝘯𝘵. 𝘉𝘶𝘵 𝘸𝘩𝘢𝘵 𝘪𝘧 𝘵𝘩𝘪𝘴 𝘢𝘱𝘱𝘳𝘰𝘢𝘤𝘩 𝘭𝘦𝘢𝘷𝘦𝘴 𝘵𝘩𝘦 𝘣𝘦𝘴𝘵 𝘴𝘰𝘭𝘶𝘵𝘪𝘰𝘯𝘴 𝘶𝘯𝘥𝘪𝘴𝘤𝘰𝘷𝘦𝘳𝘦𝘥? A new paper from Google Research and Google DeepMind demonstrates an AI system that not only automates this process but achieves superhuman performance. This is crucial because the slow, manual creation of code for computational experiments severely limits the hypotheses scientists can explore, creating a major bottleneck in the cycle of discovery. The paper, "𝐀𝐧 𝐀𝐈 𝐬𝐲𝐬𝐭𝐞𝐦 𝐭𝐨 𝐡𝐞𝐥𝐩 𝐬𝐜𝐢𝐞𝐧𝐭𝐢𝐬𝐭𝐬 𝐰𝐫𝐢𝐭𝐞 𝐞𝐱𝐩𝐞𝐫𝐭-𝐥𝐞𝐯𝐞𝐥 𝐞𝐦𝐩𝐢𝐫𝐢𝐜𝐚𝐥 𝐬𝐨𝐟𝐭𝐰𝐚𝐫𝐞," introduces a system that tackles this challenge. It reframes software development as a "scorable task." The core methodology combines a Large Language Model (LLM) for intelligent code rewriting with a Tree Search (TS) algorithm. The TS intelligently navigates the vast space of possible solutions, guiding the LLM to iteratively refine code to maximize a quality score. It's not just generating code; it's evolving it. The results are stunning: - In bioinformatics, it discovered 40 novel methods for single-cell data analysis that outperformed all top human-developed methods on a public leaderboard. - For epidemiology, it generated 14 forecasting models that were more accurate than the official CDC ensemble for predicting COVID-19 hospitalizations. This represents a fundamental shift from scientists manually coding solutions to defining "scorable problems" and letting an AI discovery engine find the optimal software. By systematically exploring and even recombining complex research ideas, this system can uncover novel "needle-in-a-haystack" solutions that humans might never find. It could accelerate progress in fields from genomics to climate science by automating one of the most tedious parts of research. #AI #MachineLearning #ScientificDiscovery #GenerativeAI #Research

I've watched countless AI demos with flashy interfaces fail in the real world. The winners? 𝗕𝗼𝗿𝗶𝗻𝗴 𝗲𝗻𝘁𝗲𝗿𝗽𝗿𝗶𝘀𝗲 𝘀𝗼𝗹𝘂𝘁𝗶𝗼𝗻𝘀 𝘁𝗵𝗮𝘁 𝘀𝗼𝗹𝘃𝗲 𝗮𝗰𝘁𝘂𝗮𝗹 𝘄𝗼𝗿𝗸𝗳𝗹𝗼𝘄 𝗽𝗿𝗼𝗯𝗹𝗲𝗺𝘀. Take financial data extraction. The 𝗹𝗼𝘀𝗶𝗻𝗴 approach builds another generalized LLM wrapper with a beautiful UI. The 𝘄𝗶𝗻𝗻𝗶𝗻𝗴 approach utilizes small language models, business rules, and robust evaluation frameworks that are embedded directly into existing workflows. The difference is a 𝗯𝘂𝘀𝗶𝗻𝗲𝘀𝘀-𝗱𝗿𝗶𝘃𝗲𝗻 focus. Those "𝗯𝗼𝗿𝗶𝗻𝗴" solutions succeed because they involve 𝘀𝘂𝗯𝗷𝗲𝗰𝘁 𝗺𝗮𝘁𝘁𝗲𝗿 𝗲𝘅𝗽𝗲𝗿𝘁𝘀 𝗶𝗻 𝘁𝗵𝗲 𝗹𝗼𝗼𝗽. They understand the business rules. They build guardrails that actually work because humans who know the domain helped create them. This is what business-driven AI actually looks like in enterprise settings. It's not about building the most sophisticated model. It's about embedding the people who understand the problem into the solution itself. The most successful AI implementations prioritize workflow integration over technical sophistication. 𝗦𝗽𝗲𝗲𝗱 𝗮𝗻𝗱 𝗮𝗰𝗰𝘂𝗿𝗮𝗰𝘆 matter more than model size when you're solving real problems. The future belongs to AI builders who understand this. 𝗧𝗲𝗰𝗵𝗻𝗶𝗰𝗮𝗹 𝗯𝗿𝗶𝗹𝗹𝗶𝗮𝗻𝗰𝗲 𝘄𝗶𝘁𝗵𝗼𝘂𝘁 𝗱𝗼𝗺𝗮𝗶𝗻 𝗲𝘅𝗽𝗲𝗿𝘁𝗶𝘀𝗲 𝗮𝗻𝗱 𝗵𝘂𝗺𝗮𝗻 𝗳𝗲𝗲𝗱𝗯𝗮𝗰𝗸 𝗹𝗼𝗼𝗽𝘀 𝗰𝗮𝗻 𝗰𝗿𝗲𝗮𝘁𝗲 𝘀𝗼𝗹𝘂𝘁𝗶𝗼𝗻𝘀 𝘁𝗵𝗮𝘁 𝗮𝗽𝗽𝗲𝗮𝗿 𝗶𝗺𝗽𝗿𝗲𝘀𝘀𝗶𝘃𝗲 𝗶𝗻 𝗱𝗲𝗺𝗼𝘀 𝗯𝘂𝘁 𝗳𝗮𝗶𝗹 𝘄𝗵𝗲𝗻 𝗱𝗲𝗽𝗹𝗼𝘆𝗲𝗱. Business problem-driven builders will define AI's future because they know the secret: the best technology disappears into workflows so seamlessly that users forget they're using AI at all. What boring problem in your workflow needs an AI solution that actually works? #AI #EnterpriseAI #WorkflowAutomation #BusinessDriven #PracticalAI #AIImplementation ✍🏽 I share lessons learned from building AI systems in the field. Follow for more #AIexperiencefromthefield