IT

Choosing the Right AI Agent Architecture Is Now a Business Decision, Not a Technical One Most teams jump straight to “LLM agents,” but every agent type solves a different problem. Pick the wrong one → you get failures, hallucinations, and systems that don’t scale. Pick the right one → you get reliability, automation, and enterprise-grade performance. Here’s the breakdown - 1. Rule-Based Agents Great for predictable workflows and compliance-heavy systems. Fast, stable, cheap - but zero...

Most businesses say they’re “doing AI”. But they’re not talking about the same thing. Some mean forecasting. Some mean chatbots. Some mean systems that actually run work end to end. That confusion is why AI projects stall. Here’s the 5-step ladder I use to explain it: (See the visual for more details 👇) Most teams are mixing step 3 expectations with step 1 foundations. That’s where budgets disappear.

AI automation workflow, simplified: follow this no-code setup map to spot the right tasks, map “as-is” steps, pick a tool, add triggers + AI actions, connect your apps, and track KPIs so your automation actually saves time. #AIAutomation #NoCode #Workflow #Productivity

While you build chatbots, they are building AI Agent ecosystem But how… | Rakesh Gohel | 31 comments

AI governance is no longer about ethics on paper, it’s about systems that can withstand pressure, scrutiny, and drift. This 8-point checklist outlines how mature organizations now operationalize trust, model risk logs, explainability reports, bias testing, and automated audit trails built into every workflow. The difference between compliant and future-proof governance is discipline. Not promises, but documented proof of control, explainability, and human oversight at every node. Follow...

AI agents are not just the future—they are the operating system of tomorrow’s digital world. But the ecosystem is complex and evolving rapidly. To make sense of it, I created this AI Agent Metro Map —a structured way to navigate the landscape. Here’s how it breaks down: 🔴 A1 – Core Building Blocks Programming skills, frameworks, foundation models, embeddings, tokenization, vector databases—these are the rails every agent runs on. 🟢 A2 – Agent Training & Reasoning From prompt engineering and...

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AI Agents have moved from simple scripts to multi-agent systems Understanding the stages helps save time and cost... Multiple reports suggest the issue isn’t AI workflows themselves, but how people… | Rakesh Gohel | 38 comments

What is actually an Agentic Al?

Google is building an AI Agent-enabled e-commerce ecosystem and UCP might be their biggest innovation in that process... Currently, agentic commerce is flawed. Every AI agent needs custom… | Rakesh Gohel | 44 comments

[Sưu tầm] Nhiều người thường nhìn **Trí tuệ nhân tạo (AI)** như một phép màu. Nhưng thực ra, nó chỉ là “người khổng lồ đứng trên vai” của **Toán học và...

How AI is Orchestrated: A Guide to Building Agentic Systems | Pavan Belagatti posted on the topic | LinkedIn

Must read the terminologies if you want to start learning AI Agents Here are the first 20 you should try learning... 1. Agent: An Autonomous entity that utilizes prompts and the environment to perceive and act on a goal. 2. Environment: Context or sandbox where an AI agent operates and interacts with other tools. 3. Perception: The AI Agent’s ability to understand and interpret environmental data. 4. Action: The current process performed by an AI agent, given a prompt or feedback by the...

Not everything connected with an LLM is an AI Agent Let us learn the core components that make the difference... Since LLMs like GPT emerged, many have asked me what truly defines an AI Agent. Though I have tackled this question many times through different posts, Today, I will explicitly compare major Agent-like architectures to explain the core differences among them. 📌 LLM Chatbot: - User sends a query → LLM processes it using internal knowledge or enterprise data. - LLM generates a...