Tag: Agentic AI

  • “OpenAI Frontier”: What’s It About?

    “OpenAI Frontier”: What’s It About?

    OpenAI Frontier represents one of the most important shifts in artificial intelligence since the rise of conversational AI. While most AI products today function as assistants that respond to prompts, Frontier introduces something fundamentally different: autonomous AI agents capable of operating like coordinated digital coworkers. Built as an enterprise-scale AI orchestration platform, Frontier enables multiple AI agents to collaborate, share memory, execute workflows, and interact with software systems with minimal human intervention.

    What makes Frontier especially compelling is that it moves beyond the “chatbot” paradigm. Instead of simply generating text or answering questions, Frontier agents can conduct research, manage operations, create marketing campaigns, analyze business performance, coordinate tasks, and continuously update one another in real time.

    The result feels less like using a single AI tool and more like managing an intelligent organization powered by autonomous systems.


  • Google’s “Daily Brief”: A Fresh Spin On Agentic AI?

    Google’s “Daily Brief”: A Fresh Spin On Agentic AI?

    So for those in our community who may have missed this – Google has introduced a new feature today called “Daily Brief”, an AI-powered productivity agent within its Gemini app.

    The tool is designed to deliver personalized morning digests by scanning Gmail, Calendar, and Gemini chats to highlight urgent updates, prioritize tasks, and suggest next steps. Announced at Google I/O 2026, Daily Brief is now rolling out to US subscribers of Gemini Plus, Pro, and Ultra, marking a significant step in Google’s shift toward proactive AI assistance.

    But is it Different From the Rest of the Pack?

    So the real question here is – does this new agentic AI truly stand apart from other agentic AI tools already in the market? At its core, Daily Brief offers a personalized morning digest by pulling information from Gmail, Calendar, and Gemini chats, then suggesting immediate actions. But this is similar in spirit to Microsoft Copilot’s daily briefing emails, which summarize meetings, tasks, and emails, and maybe even to Apple’s rumored AI assistant, expected to integrate deeply with iOS productivity apps.

    Where Daily Brief differs, say some, is in its agentic design. Unlike Copilot, which primarily delivers static summaries, Google’s tool emphasizes proactive orchestration, from suggesting replies, scheduling events, and learning from user feedback to refine future briefs. It also integrates with Gemini Spark, a 24/7 agent capable of executing tasks across Google Workspace and third-party apps, positioning Daily Brief as part of a larger, continuous AI ecosystem rather than a standalone feature.

    However, the distinction may blur in practice. There are other assistants already offer contextual task suggestions, and startups like Notion AI and Reclaim provide similar proactive planning.

    Google’s edge lies in its “Neural Expressive design language”, which makes briefs visually dynamic with graphics and narration, potentially enhancing engagement.

    The Verdict For Now

    Ultimately, Daily Brief is less a radical departure than a polished iteration. Its success will depend on whether users see value in Google’s integrated, ecosystem-first approach compared to competitors’ offerings.

    Image credit: Google ‘The Keyword’

  • Part 15: Launching Your First AI Agent (deployment, testing, and continuous Improvement)

    Part 15: Launching Your First AI Agent (deployment, testing, and continuous Improvement)

    Building your AI agent is only half the journey.

    The real challenge begins when you move from prototype to real-world use.

    Because no matter how impressive your agent seems in development:

    If it fails in production, users won’t care how advanced it is.

    Deployment is where reliability, usability, and long-term value are proven.


  • Part 14: The Real Cost of Building Your First AI Agent (time, money, and expectations)

    Part 14: The Real Cost of Building Your First AI Agent (time, money, and expectations)

    By now, you’ve seen what AI agents can do.

    They can automate workflows, reason through tasks, interact with APIs, retrieve knowledge, and sometimes feel surprisingly capable.

    But here’s the reality:

    Building an AI agent is not magic. It’s an engineering project.

    And like any engineering project, success depends on understanding three things:

    • Cost
    • Effort
    • Realistic outcomes

    If you skip this conversation, you risk overspending, overbuilding, or expecting far more autonomy than current systems can reliably deliver.


  • Part 13: Common Mistakes People Make (and how to avoid them)

    Part 13: Common Mistakes People Make (and how to avoid them)

    By now, you’ve seen what’s possible.

    So naturally, the next step is:
    you try to build something.

    And this is where most people get stuck.

    Not because AI is hard.
    But because they make a few very predictable mistakes.

    Let’s go through them.


  • Part 11: Use Cases For Creators (how you can actually use this)

    Part 11: Use Cases For Creators (how you can actually use this)

    By now, you understand how to build simple AI agents.

    The next question is:

    Where do you actually use this in your day-to-day work?

    If you create content in any form — posts, newsletters, videos, communities — this is where agentic AI becomes immediately useful.

    Let’s look at practical ways you can apply it.


  • Part 10: The Tools You Can Use (no-code, simple, and enough to get started)

    Part 10: The Tools You Can Use (no-code, simple, and enough to get started)

    By now, you might be thinking:

    “This sounds good… but what do I actually use to build all this?”

    Good news.

    You don’t need complicated software.
    You don’t need to code.

    You just need a few simple tools that work well together.

    Let’s break this down.


  • Part 9: Turning A Basic Agent Into Something More Agentic

    Part 9: Turning A Basic Agent Into Something More Agentic

    By now, you’ve built something simple.

    Maybe it generates ideas.
    Maybe it drafts content.
    Maybe it helps with emails.

    That’s a great start.

    But right now, it’s still mostly reactive.

    You give an input → it gives an output.

    So the next step is this:

    How do you make it a little more “agentic”?

    Not complicated. Just smarter.

    Let’s build on what you already have.


  • Part 8: The Simplest Way To Get Started (build your first basic AI agent)

    Part 8: The Simplest Way To Get Started (build your first basic AI agent)

    Enough theory.

    Let’s build something simple.

    Not a complex system.
    Not something “fully agentic”.

    Let’s just build your first basic AI agent.

    The goal here is simple:


  • Part 6: The Risks And Limitations (where this can go wrong)

    Part 6: The Risks And Limitations (where this can go wrong)

    So far, this might sound exciting to you, right?

    AI that:

    • thinks in steps
    • makes decisions
    • handles tasks

    But let’s not get carried away.

    This is not magic.
    And it definitely doesn’t work perfectly.

    If you’re planning to build with AI, you need to understand where it breaks.

    Because it will.

    Let’s go through the real limitations.