How to Use AI Agents for Business Value Creation

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?

Agents will unlock the next wave of productivity gains for the enterprise...but they also have their own unique set of operational challenges Let's check the lifecycle for AI Agentic development 𝗗𝗲𝘀𝗶𝗴𝗻: 1. Define agent use case, detailed workflow and KPIs to align with business goal. 2. Identify data sources (tools) available to validate feasibility of project. 3. Select/fine-tune appropriate model to suit the agentic workflow. 4. Define appropriate architecture & patterns (framework & libraries) to enable reasoning, planning, self-improvement, tool usage. 5. Design underlying infrastructure to optimize cost-effectiveness. 𝗕𝘂𝗶𝗹𝗱 & 𝗗𝗲𝗽𝗹𝗼𝘆: 1. Integrate agentic workflow with LLM inference provider. 2. Integrate service with data sources (tools) across environments. 3. Simulate and debug service behavior. Guardrail actions and outputs. 𝗖𝗼𝗻𝘀𝘂𝗺𝗲 & 𝗠𝗼𝗻𝗶𝘁𝗼𝗿: 1. Deploy agentic workflow as API endpoint. Ensure access control and security. 2. Integrate agentic workflow with application services (UI, etc.). 3. Monitor agentic workflow KPIs & logs to ensure optimized results, provide transparency & explainability. AI agents need supporting enterprise capabilities to overcome adoption barriers and be deployed at scale.

After building 58 AI Agents, here are 12 essential tips (steal these and get ahead): 1. Delegate. - Stop thinking, “What manual processes can I automate?” - Instead, ask, “If I had a marketing agency, what would I want them to handle?” - Think bigger—AI isn’t just a time-saver, it’s a workforce multiplier. 2. Automation vs. AI Automation vs. AI Agents. - Automation: A series of steps executed automatically. - AI Automation: The same, but with an AI step. - AI Agents: Decide how to act, what to do, and what data to use. 3. AI Agents go beyond chat. 3 ways to trigger an Agent: - On demand (chat or button click). - On a webpage (via Chrome extension). - Via an event (just like an automation). 4. Use ChatGPT (or similar) to build. - Writing clear instructions (“prompts”) is harder than it looks. - Determining an Agent’s decision-making process is even harder. - ChatGPT is an essential tool for thinking through both. 5. There’s a fine line between useful and over-engineered. - Simple Agents get used. Complex ones get abandoned. - Start small—iterate later. - Traditional automation is no different. 6. Stronger use cases I’ve found: - Prioritizing feature requests based on product strategy - Pulling insights from a Zapier Table of consolidated data (cost savings, top-performing areas, etc.). - Researching a company, person, or product—then structuring the data and determining when to notify someone. 7. Use decision-making frameworks. - AI Agents, like humans, need structured decision-making. - MoSCoW, Eisenhower Matrix, SWOT—pick one and embed it. - You’ll understand why your Agent made a decision, not just what it did. 8. Data sources are the most powerful component. - Agents process large data sets instantly—that’s their edge. - The better your data, the better your Agent. - Build robust databases, and your Agents will thrive. 9. Agents need systems (just like you). - The future isn’t just Agents—it’s Agents + Tables + Workflows + Interfaces. - You’re not just automating—you’re designing an AI-powered organization. - Systems > Standalone Agents. 10. Two essential skills for building. - Delegating future work (that you've already done before). - Pushing the Agent to tackle tasks that haven’t been done before. - Both require serious brainpower and take time to master. 11. Set guardrails while also allowing for mistakes. - Restrict access in integrated apps to avoid risk. - Be okay with the Agent making some mistakes. - Master the balancing act to become an expert Agent builder. 12. The biggest bottleneck is you. - Are you clear on priorities? Goals? Expectations? - An Agent can only be as clear as you are. - Get your own systems right, and your AI will follow. One of the best skills you can learn in 2025 is Agent building. Models are getting better every. single. day. They'll do more and be smarter. Best way to learn: start building. Let's all learn together 💪 Consider subscribing to my newsletter: https://lnkd.in/gtxpSwap