Today, we’re bringing you the latest in AI-powered marketing and business strategies. Here’s what’s inside:

🚨 AI Top Story: AI memory is evolving fast—this week, we break down how smarter, context-aware agents are changing the way marketers build, automate, & collaborate.

🎥 AI YouTube Resource Of The Week: A Beginners Step-by-Step Tutorial For Botpress

🎯 AI Case Study Of The Week: How Best Buy Is Implementing AI to better their customer support calls

🌟 Creator Spotlight: Soha Elseidy breaks down the five types of AI agents and how they’re powering today’s smartest tech

Stop Your AI Agents From Resetting—Give Them Memory

What AI Can Remember, It Can Improve

AI agents are getting better—not just faster, but genuinely more capable. And the reason isn’t just bigger models or better prompts. It’s memory. Not in the technical, hard-drive sense, but in the way that actually makes agents feel more useful, more consistent, and frankly, more human. Memory is what allows an AI to remember what happened yesterday, understand what’s happening right now, and make smarter decisions tomorrow. And this shift is reshaping what it means to build and interact with intelligent systems.

AI agents have always had some form of memory—whether it’s storing variables during a session or referencing a limited context window to hold a conversation. But what’s happening now is different. Memory is evolving from a background feature into a central design principle. Agents are beginning to organise, retain, and reason with information across interactions in a way that’s more structured, more intentional, and a lot more powerful.

There’s a key distinction worth understanding here. Most AI systems today operate with what’s called limited memory—they can refer to recent interactions or store short-term details for the duration of a task. But as soon as that task is done, the context usually disappears. Now, we’re moving toward long-term, persistent memory. This kind of memory doesn’t just help agents complete a single request—it allows them to learn from past experiences, track patterns over time, and grow more capable the longer you use them.

So how does it actually work? At a high level, agent memory is built by capturing and storing previous interactions—things like user inputs, decisions made, summaries of conversations, or extracted key facts. This data can be stored in vector databases, structured documents, or knowledge graphs, depending on the agent’s complexity. From there, the agent uses retrieval techniques to surface the most relevant memories when a new task comes in. It’s less about remembering everything, and more about remembering what matters—and doing it quickly.

For users, the key to getting this right is intentionality. You need to decide what you want your agent to remember and when it should forget. That might mean prompting your agent to summarise each session at the end, tagging important pieces of information as “memorable,” or building workflows that automatically log milestones. Tools like LangChain and LangGraph give developers the building blocks to define how memory is captured, stored, and recalled—whether that’s short-term context for a single session, or long-term memory that spans an entire project or customer journey.

And here’s where things get really interesting: as memory improves, agents become more autonomous. They rely less on constant input and start connecting the dots on their own. That means they’re not just executing tasks—they’re anticipating needs, making decisions, and evolving in the background. For marketers, that opens the door to agents that don’t just react—they collaborate. Imagine working with an assistant that remembers the last five campaigns you ran, how each one performed, what your brand voice sounds like, and what you’re trying to achieve next.

Memory is the unlock that turns AI from a tool into a teammate. The better it remembers, the better it works with you—and the more powerful your marketing becomes as a result.

🤖 Europe, Meet Your Newest Assistant: Meta AI - Meta finally introduces its AI assistant across Europe, enhancing user interactions on WhatsApp, Facebook, Instagram, and Messenger.

✉️ Google is improving Gmail’s search with AI - Google enhances Gmail's search with AI, delivering more relevant email results based on user interactions.

🎙️ OpenAI upgrades its transcription and voice-generating AI models - OpenAI’s new models bring faster, more accurate transcription and ultra-realistic AI voices.

🧠 5 top business use cases for AI agents - Explore how AI agents are transforming software development, automation, customer service, content creation, and HR support.

Build AI Agents That Actually Understand Context With Botpress

Designing an AI agent that actually understands what’s going on—across multiple steps, sessions, and channels—used to be a patchwork of tools and workarounds. Now, it’s finally becoming seamless.

Botpress is a complete platform for building AI agents that go far beyond simple chatbots. With visual workflows, built-in memory, and deep LLM integration, it helps teams create AI agents that don’t just respond—they remember, reason, and take action.

Whether you're building a customer support assistant, a lead qualifier for your website, or an internal tool to automate team processes, Botpress gives you the structure, flexibility, and intelligence to build agents that feel intuitive, responsive, and ready for real-world use.

Key Features:

🧠 Agent Studio – Build complex, multi-step conversations using a visual no-code builder that supports logic, conditions, and memory.

📚 Knowledge Integration – Connect documents, websites, and PDFs so your agents can pull answers from your actual content—not generic scripts.

🔗 Multi-Channel Ready – Deploy across web, WhatsApp, Slack, and more, with native support for major messaging platforms.

💾 Conversational Memory – Retain context across interactions so agents can personalize responses and act with continuity.

🤖 LLM-Powered Autonomy – Let agents determine what to say or do next using powerful reasoning capabilities—not just predefined flows.

🛠️ Plug-and-Play Integrations – Connect to tools like Notion, HubSpot, Jira, Calendly, and others to turn conversations into actions.

Developer Tips:

Start with pre-built templates for common use cases like lead gen or FAQs.

Train your agents with domain-specific data to sharpen NLU accuracy.

Use the built-in emulator to test and refine workflows before going live.

Pricing:

Free – 1 bot with 500 messages/month.

Team – $495/month with advanced analytics and collaboration.

Enterprise – Custom plans for high-volume or specialised use.

🔎 Click Here To Explore 25 Use Cases 

👉 Click Here To Take Botpress For A Spin For Free

SOHA ELSEIDY - A Breakdown of the five types of AI agents and how they’re powering today’s smartest tech.

Tim Harris Gives Us A Beginners Step-by-Step Tutorial For Botpress

AI Summaries Are Changing the Support Game At Best Buy

The Overview

Best Buy is giving its customer support experience a major upgrade—with the help of generative AI. By using Contact Center AI, they’ve managed to reduce average call and post-call time by up to 90 seconds, all while making things smoother for both customers and agents.

More human conversations, less repetitive admin, and better outcomes for everyone involved.

The Implementation

At the heart of this transformation is real-time conversation summarisation. Instead of forcing agents to take notes while juggling support calls, the AI listens in and automatically generates a summary of each interaction as it happens. That frees up agents to focus entirely on the customer—no more multitasking, no more missing details.

For self-service needs, customers can also use a virtual assistant powered by the same AI tech to handle things like order tracking, membership questions, or simple troubleshooting. But when human help is needed, the AI doesn’t step out—it works behind the scenes, offering human customer support agents live suggestions, detecting customer sentiment, and keeping everything on track.

The Impact

This shift has made a real difference. Call times are shorter, after-call work is lighter, and both agents and customers are walking away more satisfied. Agents feel less burned out and better supported. Customers get faster resolutions and more attention. And from a business perspective, it’s a solid example of how AI can quietly—but powerfully—improve frontline operations without sacrificing the human touch.