How to Break Down Complex Tasks for Artificial Intelligence

You don’t need to be an AI agent to be agentic. No, that’s not an inspirational poster. It’s my research takeaway for how companies should build AI into their business. Agents are the equivalent of a self-driving Ferrari that keeps driving itself into the wall. It looks and sounds cool, but there is a better use for your money. AI workflows offer a more predictable and reliable way to sound super cool while also yielding practical results. Anthropic defines both agents and workflows as agentic systems, specifically in this way: 𝗪𝗼𝗿𝗸𝗳𝗹𝗼𝘄𝘀: systems where predefined code paths orchestrate the use of LLMs and tools 𝗔𝗴𝗲𝗻𝘁𝘀: systems where LLMs dynamically decide their own path and tool uses For any organization leaning into Agentic AI, don’t start with agents. You will just overcomplicate the solution. Instead, try these workflows from Anthropic’s guide to effectively building AI agents: 𝟭. 𝗣𝗿𝗼𝗺𝗽𝘁-𝗰𝗵𝗮𝗶𝗻𝗶𝗻𝗴:  The type A of workflows, this breaks a task down into sequential tasks organized and logical steps, with each step building on the last. It can include gates where you can verify the information before going through the entire process. 𝟮. 𝗣𝗮𝗿𝗮𝗹𝗹𝗲𝗹𝗶𝘇𝗮𝘁𝗶𝗼𝗻: The multi-tasker workflow, this separates tasks across multiple LLMs and then combines the outputs. This is great for speed, but also collects multiple perspectives from different LLMs to increase confidence in the results. 𝟯. 𝗥𝗼𝘂𝘁𝗶𝗻𝗴: The task master of workflows, this breaks down complex tasks into different categories and assigns those to specialized LLMs that are best suited for the task. Just like you don’t want to give an advanced task to an intern or a basic task to a senior employee, this find the right LLM for the right job. 𝟰. 𝗢𝗿𝗰𝗵𝗲𝘀𝘁𝗿𝗮𝘁𝗼𝗿-𝘄𝗼𝗿𝗸𝗲𝗿𝘀: The middle manager of the workflows, this has an LLM that breaks down the tasks and delegates them to other LLMs, then synthesizes their results. This is best suited for complex tasks where you don’t quite know what subtasks are going to be needed. 𝟱. 𝗘𝘃𝗮𝗹𝘂𝗮𝘁𝗼𝗿-𝗼𝗽𝘁𝗶𝗺𝗶𝘇𝗲𝗿: The peer review of workflows, this uses an LLM to generate a response while another LLM evaluates and provides feedback in a loop until it passes muster. View my full write-up here: https://lnkd.in/eZXdRrxz

We’re entering an era where AI isn’t just answering questions — it’s starting to take action. From booking meetings to writing reports to managing systems, AI agents are slowly becoming the digital coworkers of tomorrow!!!! But building an AI agent that’s actually helpful — and scalable — is a whole different challenge. That’s why I created this 10-step roadmap for building scalable AI agents (2025 Edition) — to break it down clearly and practically. Here’s what it covers and why it matters: - Start with the right model Don’t just pick the most powerful LLM. Choose one that fits your use case — stable responses, good reasoning, and support for tools and APIs. - Teach the agent how to think Should it act quickly or pause and plan? Should it break tasks into steps? These choices define how reliable your agent will be. - Write clear instructions Just like onboarding a new hire, agents need structured guidance. Define the format, tone, when to use tools, and what to do if something fails. - Give it memory AI models forget — fast. Add memory so your agent remembers what happened in past conversations, knows user preferences, and keeps improving. - Connect it to real tools Want your agent to actually do something? Plug it into tools like CRMs, databases, or email. Otherwise, it’s just chat. - Assign one clear job Vague tasks like “be helpful” lead to messy results. Clear tasks like “summarize user feedback and suggest improvements” lead to real impact. - Use agent teams Sometimes, one agent isn’t enough. Use multiple agents with different roles — one gathers info, another interprets it, another delivers output. - Monitor and improve Watch how your agent performs, gather feedback, and tweak as needed. This is how you go from a working demo to something production-ready. - Test and version everything Just like software, agents evolve. Track what works, test different versions, and always have a backup plan. - Deploy and scale smartly From APIs to autoscaling — once your agent works, make sure it can scale without breaking. Why this matters: The AI agent space is moving fast. Companies are using them to improve support, sales, internal workflows, and much more. If you work in tech, data, product, or operations — learning how to build and use agents is quickly becoming a must-have skill. This roadmap is a great place to start or to benchmark your current approach. What step are you on right now?

The pace of new developments in AI is faster than what most people can absorb. I created the BUILD Framework for myself to help me quickly understand what matters and why… BUILD stands for: 🧱 Base 🛠️ Upgrade 🧠 Improve 🎯 Lead 🤝 Delegate Each layer helps you understand how to build AI products that accomplish more complex objectives. It helps me answer questions like: ➔ What is it? ➔ Where does it fit? ➔ Why does it matter? 🧱 Base In the base layer you have foundational models. Most importantly, understand that they are non-deterministic which creates strengths and limitations. The base is like a new brilliant employee with zero work experience. They have raw horsepower, but lack access to tools, domain knowledge, reasoning, and more. To get them to do more you need to… 🛠️ Upgrade The next layer is upgrade - Knowledge, Reasoning, Tools, and Memory. Think about Tools like giving your new untrained employee access to Figma, Slack, etc so they can do their tasks more efficiently. Think about Knowledge as giving the new untrained employee historical company knowledge and domain information. For each upgrade there are multiple methodologies. For reasoning there is Chain of Thought, Tree of Thought, Graph of Thought, etc. For Memory there is summarization memory, entity memory, etc. 🧠 Improve What if your new untrained employee never got better? You’d fire them. The Improve layer is how you get your AI products to improve over time. This is done via Evaluations, Human Feedback, Reinforcement Learning. For example, Evaluations are like comparng your employee’s work against a known standard of what “good.” Next you realize your AI can execute tasks, but it can’t pursue goals ... 🎯 Lead What if you had to tell your employee every little individual task to do? Instead you want to give them a goal and have them execute the tasks. This is the core of Agents. Instead of giving tasks one by one, it’s how to give an AI a goal and it comes up with a task plan and cycles through those tasks until the goal is achieved. But there is a problem with Agents. The Compounding Error Problem (by Dharmesh Shah) Since AI models are non-deterministic they all have an error rate. Even if that error rate is small (say 5%) that error rate compounded across 12 tasks means a 54% chance of success. What products do customers use if they only succeed 54% of the time? Very few. The solution has been to create narrow specialist agents to reduce the error rate. But this leads to the next set of problems… 🤝 Delegate When you have a team of specialists, you need to coordinate them to achieve a bigger goal. This is what Delegate layer is about - how do you coordinate across multiple specialist agents and the techniques to achieve this. BUILD Every time a new AI development comes out, I immediately place it within this framework to understand - What is it? Where does it fit? Why is it important? Does it help me solve a problem with what we are building?

If you feel stuck or overwhelmed trying to use AI for a process -- don't do it all at once. Instead, use what I call the T Method. 1. Write out a high level outline of the tasks in a process 2. Go deep and teach AI to do the steps in each task, one at a time You'll move faster Be more agile and iterate And get the value out of each step along the way If you're trying to use AI for processes right now, blank page syndrome is so real. This has helped us and our clients, hope it's helpful for you. And let me know, where else have you gotten stuck?