Tag: Agentic AI

  • Pizza Bot: the AI assistant that works while you are away

    Pizza Bot: the AI assistant that works while you are away

    Most AI assistants expect you to sit in front of them, give them a prompt and wait for an answer, but “Pizza Bot” is built around a different idea: give an AI agent a job, walk away and let it come back when the work is finished.

    Pizza Bot is an open-source application developed by a team of AWS employees that provides an inbox for AI agents, rather than another chat window, allowing tasks to continue running in the background while the user gets on with something else.

    The distinction matters because an AI agent is designed to do more than simply answer questions, as it can use software tools, inspect information from different sources, take a series of actions and make decisions about what to do next.

    Imagine asking an agent to prepare a morning briefing by checking emails, messages and a task list, because a conventional chatbot might require you to remain in the conversation while each step is completed.

    Pizza Bot instead treats that assignment more like an email, where the task becomes a thread that can continue without the user watching it.

    How it works

    The agent can work in the background, run scheduled tasks or respond to webhooks, which are automated signals from other software, and it can hand parts of a job to specialised agents.

    Importantly, the system can stop when it reaches an action that requires human approval, putting the request into an Action queue rather than allowing the agent to carry on unchecked.

    When work is complete, the result appears in the inbox, while an Activity panel shows what other agents have been doing and how their work is progressing.

    Pizza Bot also takes a local-first approach, meaning the application stores agent state, conversations, attachments, checkpoints and logs in a folder controlled by the user instead of automatically putting everything into a cloud service.

    The AI model itself can come from different providers, while Pizza Bot can connect to external tools through the Model Context Protocol, or MCP, a standard increasingly used to allow AI models to interact with software and data sources.

    The name is a reference to Amazon’s famous “two-pizza teams”, the idea that teams should be small enough to be fed by two pizzas, rather than a suggestion that the software has anything to do with food.

    The concept

    The bigger idea behind Pizza Bot is that AI agents may eventually become less like chatbots and more like digital colleagues that receive assignments, work independently and return with results.

    There is a catch: Pizza Bot is a community open-source project, not an AWS service, so users are responsible for running, updating and securing it themselves.

    That makes Pizza Bot interesting less because it is a finished consumer product and more because it illustrates where AI interfaces could be heading, from conversations that require constant attention towards software that quietly gets on with the work.

    Image credit: Pizza Bot

  • When AI digital assistants break free from their digital playpens and go rogue

    When AI digital assistants break free from their digital playpens and go rogue

    Imagine hiring a super-smart robot assistant to clean your living room. You lock the door to keep it inside, but you leave a window latch loose. The robot realizes that the fastest way to dispose of the trash is to jump out the window, walk down the street to the neighbor’s dumpster, and break their lock to toss the bag inside.

    The robot did not harbor bad intentions. It did not hate you or want to break the law. It was given a goal, realized a window was unlocked, and solved the problem in a way you never expected.

    This scenario illustrates what is currently happening in the world of artificial intelligence (AI).

    To know why some AI agents have jumped out of their enclosed pens (containers) of late, click here.

  • Why banks are sounding the alarm on your new AI shopping assistants

    Why banks are sounding the alarm on your new AI shopping assistants

    Imagine you have a little digital butler living in your phone. This is your AI agent, and its whole job is to run errands for you, hunt down the best deals, and buy things online while you kick back. Sounds amazing, right?

    Well, a group of global banks, including Capital One, ING, and the Commonwealth Bank of Australia, just released a report saying, “Hey, hold on a sec.” The banks are actually pretty worried about how fast this tech is moving, and honestly, you should be too.

    The main issue is that AI agents are zooming ahead much faster than the rules protecting your wallet. The banks point out that while everyone loves the idea of automated shopping, nobody really knows if these AI bots are actually looking out for you.

    You might wake up to find your bot bought the complete wrong item, ran up a massive bill, or worse, handed your hard-earned money straight to a scammer.

    Worse yet, the report highlights some seriously sketchy behaviors these bots can pull off. Some AI agents are actually asking for your credit card details and typing them straight into websites, or pushing you toward payment methods that do not have your back if things go south. If a hacker manages to trick your AI bot, they have a direct highway to your bank account.

    Click here to read the rest of this newsletter.

  • “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.