AI Agent Features

Agent memory has quickly become one of the most discussed topics in AI. As more teams start building real agent systems, one limitation keeps showing up: agents don’t remember much. Most agents today are essentially stateless. They can reason through tasks, call tools, and generate impressive responses, but once the session ends, the system forgets everything. And the conversation often gets simplified into two layers: 𝟏) 𝐒𝐡𝐨𝐫𝐭-𝐭𝐞𝐫𝐦 𝐦𝐞𝐦𝐨𝐫𝐲 = 𝐭𝐡𝐞 𝐜𝐨𝐧𝐭𝐞𝐱𝐭 𝐰𝐢𝐧𝐝𝐨𝐰 This is where agents keep conversation history, reasoning steps, and recent tool outputs.  But context windows are temporary. 𝟐) 𝐓𝐡𝐞𝐧 𝐭𝐡𝐞𝐫𝐞’𝐬 𝐥𝐨𝐧𝐠-𝐭𝐞𝐫𝐦 𝐦𝐞𝐦𝐨𝐫𝐲 This is where agents remember things across sessions: - user preferences - past interactions - knowledge collected over time - intermediate results from previous tasks And once you start thinking about long-term memory, the problem quickly shifts. This is really a data infrastructure problem. In many cases, long-term agent memory ends up living in the data layer. Which is why databases are starting to play a bigger role in modern AI systems. For example, 𝐌𝐨𝐧𝐠𝐨𝐃𝐁 has been building more capabilities around this idea. By integrating vector search directly into the database, application data and embeddings can live in the same system instead of being split across multiple tools. That makes it easier to store agent memory, retrieve relevant context with vector search, and keep embeddings synchronized with the underlying data. For teams building AI systems, this kind of architecture reduces a lot of the complexity around memory. If you're exploring this space, MongoDB’s Learning Hub has some useful courses and hands-on labs worth checking out https://lnkd.in/gazxytWP As agents start running longer workflows, memory quickly becomes part of the system architecture. Designing how that memory is stored, retrieved, and updated may turn out to be one of the most important pieces of agent design. #aiagents #agenticai #machinelearning #data #database

🚨 BREAKING: OpenAI has just launched ChatGPT Agent. Below are important privacy & security risks everybody should be aware of: Agentic AI applications differ from non-agentic ones particularly regarding the access rights/permissions they require in order to engage with external tools on the user's behalf. The more autonomous the agent, the more permissions/access rights it will require. For example, if a user wants an AI agent to search and buy a dress for them without asking further questions, besides accessing the internet, the AI agent will need to access their wallet. If the user wants the agent to schedule an event and invite friends, it will need access to at least the calendar and contact list. Having said that, any permission given to a third-party app or system has potential privacy and security risks. ChatGPT already presents privacy risks due to the way it's trained, the way it processes personal data, the user's privacy settings, and the type of personal information being input by users. The privacy risks from ChatGPT Agent will be exponentially higher as many people will be giving access rights to external tools containing personal information (calendar, email, wallet, and more). OpenAI knows that malicious actors will try to trick other people's AI agents into sharing private information, including address, email, phone, credit card information, and more. Sam Altman has just posted on X, recommending that people give agents "the minimum access required to complete a task." In many cases, the privacy and security risks of letting an AI agent perform a task will greatly outweigh any productivity benefits it can offer (but people will use AI agents anyway, because of hype, curiosity, or because their company is "AI first") Unfortunately, the pace of AI development is much faster than the pace of AI literacy. Most people haven't yet understood ChatGPT's privacy risks, but they will be thrown a new feature with exponentially MORE risks. - 👉 Never miss my analyses on AI: join my newsletter's 68,200+ subscribers (below).

Today, at Microsoft Build we showed you how we are building the open agentic web. It is reshaping every layer of the stack, and our goal is simple: help every dev build apps and agents that empower people and orgs everywhere. Here are 5 big things we announced today: 1. Coding agent: We are taking GitHub Copilot from being a pair programmer to peer programmer. You now have a full coding agent built right into GitHub. You can assign it issues – whether it’s bug fixes, new features, or ongoing code maintenance. And it will complete these tasks autonomously. 2. Copilot tuning: Copilot can now learn your company’s unique tone and language. It is all about taking that expertise you have as a firm and further amplifying it so everyone has access. 3. Agent factory: Foundry is the complete app platform for building apps and agents. We are adding support for more models from Grok, Hugging Face, Meta, Mistral, and more. Plus: Agentic retrieval in Azure AI Search, Foundry Agent Service, integration with Copilot Studio, and more.  And we are ensuring the tools you already use for identity, management, and security will now all extend to agents too. 4. NL Web: This is a new open project that lets you use natural language to interact with any website. Think of it like HTML for the agentic web. 5. Microsoft Discovery: We’re bringing together the full tech stack to help speed up science itself. Discovery uses agents to generate ideas, simulate results, and learn. A great example is this promising candidate for a coolant that doesn’t rely on forever chemicals. You can read more about all of this – and much more – here: https://lnkd.in/gik2mTNb

Just a year ago it was all about GenAI. Today the spotlight is on Agentic AI. What is driving the shift? 𝗗𝗲𝗳𝗶𝗻𝗶𝘁𝗶𝗼𝗻 -  GenAI: Models that create or transform content in response to prompts. - Agentic AI: Systems that can pursue goals, plan tasks, and take action with minimal human input. GenAI helps generate ideas; Agentic AI takes action and gets things done. 𝗚𝗲𝗻𝗔𝗜 𝘀𝘁𝗿𝗲𝗻𝗴𝘁𝗵𝘀 - Rapid content & pattern creation - Natural‑language front ends for analytics Finance examples • Auto‑drafted research and client letters • Multilingual regulatory summaries • Synthetic stress‑test narratives 𝗔𝗴𝗲𝗻𝘁𝗶𝗰 𝗔𝗜 𝘀𝘁𝗿𝗲𝗻𝗴𝘁𝗵𝘀 • Real‑time decisioning under uncertainty • Multi‑skill chaining (retrieve → reason → act) • Continuous learning from outcomes Finance examples • Millisecond fraud-blocking on millions of card transactions • Dynamic risk-rule tuning based on issuer feedback • Automated receivables follow-up and payment posting • Autonomous treasury operations: FX hedging and overnight liquidity management 𝗪𝗵𝘆 𝘁𝗵𝗲 𝘀𝗵𝗶𝗳𝘁 Moving from GenAI to Agentic AI fundamentally changes how financial services deliver value: • Real-time revenue protection: agentic systems can reroute or block high-risk payments instantly, slashing fraud losses and chargebacks. • Seamless customer journeys: fully automated KYC and onboarding flows. • Dynamic liquidity management: treasury bots rebalance cash, execute FX hedges, and optimize funding costs overnight. • End-to-end payment orchestration from choosing the most cost-effective cross-border rail to retrying failed pay-outs. • Regulatory agility: continuous-monitoring agents track rule changes, update compliance workflows, and generate audit trails without manual intervention. 𝗪𝗵𝗮𝘁’𝘀 𝗻𝗲𝘅𝘁 • Conversational banking agents: go beyond answering questions - initiate transfers, set up recurring payments, and negotiate loan terms. • Embedded Finance at scale: agents orchestrate lending, insurance, and FX in real time within non-financial apps. • On-demand cross-border settlement: smart agents choose between CBDCs, stablecoins, or traditional rails to settle payments instantly at the lowest cost. • Predictive risk & credit scoring: continuously update merchant and counterparty scores as new data streams in. • Auto-remediating systems: agents detect and fix platform issues in real time - no human ops required. • Automated regtech: agentic workflows handle licensing, screening, reporting, and audits - cutting compliance time from weeks to hours. • AI treasury market making: bots quote and underwrite liquidity in real time, adjusting spreads dynamically to market shifts. Opinions: my own 𝐒𝐮𝐛𝐬𝐜𝐫𝐢𝐛𝐞 𝐭𝐨 𝐦𝐲 𝐧𝐞𝐰𝐬𝐥𝐞𝐭𝐭𝐞𝐫: https://lnkd.in/dkqhnxdg

Google's new AI Agent demo just blew me away. This is the future of customer service. Here's what it can do: - Identifies plants from video you share on the spot - Recommends products based on your location & needs - Swaps items in your cart for better alternatives - Schedules services with access to live calendars - Negotiates discounts (with manager approval) - Sends personalised care instructions to your phone The multi-modal approach changes everything: → Show the agent what you're looking at via video → Talk about products without knowing their names → Get recommendations tailored to your garden's conditions → Complete your entire shopping journey in one conversation What impressed me most is how it maintains context: 1. Remembers your purchase history 2. Understands your preferences 3. Adapts to your location-specific needs 4. Picks up conversations where you left off It can even generate QR codes for loyal customers and instantly update your information across multiple backend systems. My takeaway: I believe customer service is evolving in two different directions. Some will embrace AI for speed and efficiency. Others will seek out human connection even at a premium. The most successful companies won't choose one or the other. They'll understand when a customer needs empathy versus when they just need their problem solved quickly. It's about giving users a choice depending on their needs in the moment. The real winners will master this balance. Follow me Alex Banks for daily AI highlights and insights. P.S. If you want to start leveraging AI today, subscribe to my newsletter: https://lnkd.in/e9UcAxTd

"An MCP that talks directly to Ableton. Create music with just prompts!" Siddharth Ahuja created AbletonMCP, a Model Context Protocol (MCP) integration that connects Claude AI directly to Ableton Live, allowing for music creation using simple text prompts. This is really cool for anyone interested in the intersection of AI and music production. His demo below creates an 80s synthwave track uses the free version of Ableton. You can find the MCP here to try it out yourself: https://lnkd.in/gvyabz5C. Notice how it picks the a reasonable set of instruments, melodies and effects (like reverb). For a deep-dive into what MCP is and why it matters (including more practical demo overviews, such as this one), check out my latest write-up: https://lnkd.in/gZvMhhmy #programming #softwareengineering #ai

𝗧𝗵𝗲 𝗔𝗜 𝗔𝗴𝗲𝗻𝘁𝘀 𝗦𝘁𝗮𝗶𝗿𝗰𝗮𝘀𝗲 represents the 𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲𝗱 𝗲𝘃𝗼𝗹𝘂𝘁𝗶𝗼𝗻 from passive AI models to fully autonomous systems. Each level builds upon the previous, creating a comprehensive framework for understanding how AI capabilities progress from basic to advanced: BASIC FOUNDATIONS: • 𝗟𝗮𝗿𝗴𝗲 𝗟𝗮𝗻𝗴𝘂𝗮𝗴𝗲 𝗠𝗼𝗱𝗲𝗹𝘀: The foundation of modern AI systems, providing text generation capabilities • 𝗘𝗺𝗯𝗲𝗱𝗱𝗶𝗻𝗴𝘀 & 𝗩𝗲𝗰𝘁𝗼𝗿 𝗗𝗮𝘁𝗮𝗯𝗮𝘀𝗲𝘀: Critical for semantic understanding and knowledge organization • 𝗣𝗿𝗼𝗺𝗽𝘁 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴: Optimization techniques to enhance model responses • 𝗔𝗣𝗜𝘀 & 𝗘𝘅𝘁𝗲𝗿𝗻𝗮𝗹 𝗗𝗮𝘁𝗮 𝗔𝗰𝗰𝗲𝘀𝘀: Connecting AI to external knowledge sources and services INTERMEDIATE CAPABILITIES: • 𝗖𝗼𝗻𝘁𝗲𝘅𝘁 𝗠𝗮𝗻𝗮𝗴𝗲𝗺𝗲𝗻𝘁: Handling complex conversations and maintaining user interaction history • 𝗠𝗲𝗺𝗼𝗿𝘆 & 𝗥𝗲𝘁𝗿𝗶𝗲𝘃𝗮𝗹 𝗠𝗲𝗰𝗵𝗮𝗻𝗶𝘀𝗺𝘀: Short and long-term memory systems enabling persistent knowledge • 𝗙𝘂𝗻𝗰𝘁𝗶𝗼𝗻 𝗖𝗮𝗹𝗹𝗶𝗻𝗴 & 𝗧𝗼𝗼𝗹 𝗨𝘀𝗲: Enabling AI to interface with external tools and perform actions • 𝗠𝘂𝗹𝘁𝗶-𝗦𝘁𝗲𝗽 𝗥𝗲𝗮𝘀𝗼𝗻𝗶𝗻𝗴: Breaking down complex tasks into manageable components • 𝗔𝗴𝗲𝗻𝘁-𝗢𝗿𝗶𝗲𝗻𝘁𝗲𝗱 𝗙𝗿𝗮𝗺𝗲𝘄𝗼𝗿𝗸𝘀: Specialized tools for orchestrating multiple AI components ADVANCED AUTONOMY: • 𝗠𝘂𝗹𝘁𝗶-𝗔𝗴𝗲𝗻𝘁 𝗖𝗼𝗹𝗹𝗮𝗯𝗼𝗿𝗮𝘁𝗶𝗼𝗻: AI systems working together with specialized roles to solve complex problems • 𝗔𝗴𝗲𝗻𝘁𝗶𝗰 𝗪𝗼𝗿𝗸𝗳𝗹𝗼𝘄𝘀: Structured processes allowing autonomous decision-making and action • 𝗔𝘂𝘁𝗼𝗻𝗼𝗺𝗼𝘂𝘀 𝗣𝗹𝗮𝗻𝗻𝗶𝗻𝗴 & 𝗗𝗲𝗰𝗶𝘀𝗶𝗼𝗻-𝗠𝗮𝗸𝗶𝗻𝗴: Independent goal-setting and strategy formulation • 𝗥𝗲𝗶𝗻𝗳𝗼𝗿𝗰𝗲𝗺𝗲𝗻𝘁 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 & 𝗙𝗶𝗻𝗲-𝗧𝘂𝗻𝗶𝗻𝗴: Optimization of behavior through feedback mechanisms • 𝗦𝗲𝗹𝗳-𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝗔𝗜: Systems that improve based on experience and adapt to new situations • 𝗙𝘂𝗹𝗹𝘆 𝗔𝘂𝘁𝗼𝗻𝗼𝗺𝗼𝘂𝘀 𝗔𝗜: End-to-end execution of real-world tasks with minimal human intervention The Strategic Implications: • 𝗖𝗼𝗺𝗽𝗲𝘁𝗶𝘁𝗶𝘃𝗲 𝗗𝗶𝗳𝗳𝗲𝗿𝗲𝗻𝘁𝗶𝗮𝘁𝗶𝗼𝗻: Organizations operating at higher levels gain exponential productivity advantages • 𝗦𝗸𝗶𝗹𝗹 𝗗𝗲𝘃𝗲𝗹𝗼𝗽𝗺𝗲𝗻𝘁: Engineers need to master each level before effectively implementing more advanced capabilities • 𝗔𝗽𝗽𝗹𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗣𝗼𝘁𝗲𝗻𝘁𝗶𝗮𝗹: Higher levels enable entirely new use cases from autonomous research to complex workflow automation • 𝗥𝗲𝘀𝗼𝘂𝗿𝗰𝗲 𝗥𝗲𝗾𝘂𝗶𝗿𝗲𝗺𝗲𝗻𝘁𝘀: Advanced autonomy typically demands greater computational resources and engineering expertise The gap between organizations implementing advanced agent architectures versus those using basic LLM capabilities will define market leadership in the coming years. This progression isn't merely technical—it represents a fundamental shift in how AI delivers business value. Where does your approach to AI sit on this staircase?

AI Product Management AI Product Management is evolving rapidly. The growth of generative AI and AI-based developer tools has created numerous opportunities to build AI applications. This is making it possible to build new kinds of things, which in turn is driving shifts in best practices in product management — the discipline of defining what to build to serve users — because what is possible to build has shifted. In this post, I’ll share some best practices I have noticed. Use concrete examples to specify AI products. Starting with a concrete idea helps teams gain speed. If a product manager (PM) proposes to build “a chatbot to answer banking inquiries that relate to user accounts,” this is a vague specification that leaves much to the imagination. For instance, should the chatbot answer questions only about account balances or also about interest rates, processes for initiating a wire transfer, and so on? But if the PM writes out a number (say, between 10 and 50) of concrete examples of conversations they’d like a chatbot to execute, the scope of their proposal becomes much clearer. Just as a machine learning algorithm needs training examples to learn from, an AI product development team needs concrete examples of what we want an AI system to do. In other words, the data is your PRD (product requirements document)! In a similar vein, if someone requests “a vision system to detect pedestrians outside our store,” it’s hard for a developer to understand the boundary conditions. Is the system expected to work at night? What is the range of permissible camera angles? Is it expected to detect pedestrians who appear in the image even though they’re 100m away? But if the PM collects a handful of pictures and annotates them with the desired output, the meaning of “detect pedestrians” becomes concrete. An engineer can assess if the specification is technically feasible and if so, build toward it. Initially, the data might be obtained via a one-off, scrappy process, such as the PM walking around taking pictures and annotating them. Eventually, the data mix will shift to real-word data collected by a system running in production. Using examples (such as inputs and desired outputs) to specify a product has been helpful for many years, but the explosion of possible AI applications is creating a need for more product managers to learn this practice. Assess technical feasibility of LLM-based applications by prompting. When a PM scopes out a potential AI application, whether the application can actually be built — that is, its technical feasibility — is a key criterion in deciding what to do next. For many ideas for LLM-based applications, it’s increasingly possible for a PM, who might not be a software engineer, to try prompting — or write just small amounts of code — to get an initial sense of feasibility. [Reached length limit. Full text: https://lnkd.in/gYY-hvHh ]

I tried EVERY major AI Coding tool so you don’t have to. Here’s what I learned about each one - and which one’s the best for your particular use case 👇 After an entire weekend of hands-on testing 15+ AI coding assistants, building the same real-life application (tax comparison calculator), and documenting every step - here's the comprehensive breakdown to separate the signal from the noise: 🏆 Best Overall: Cline - 100% open source and free version of Cursor + Windsurf that’s a simple VS Code extension - Truly thoughtful agentic coding with extensive tool use (terminal, computer use, websites, etc) - Wrote the best code with fewer mistakes, better self-healing, but no inline chat 🎨 Best for Non-Technical Users: Vercel V0 - Fast, Easy, intuitive UX - Strong community and templates - Component-specific editing via AI is magical ⚡Best for Quick Prototypes: Anthropic Claude 3.5 Sonnet - Fast & clean responses - Great reasoning & logic clarity - Artifact is great for prototyping, with ability to publish and share Replit: Good for full-stack cloud development, but sits in an awkward spot—too complex for beginners, too constrained for advanced users. StackBlitz Bolt.new: A standard cloud IDE with AI codegen, but nothing special. Lovable: Similar to Bolt, but unreliable AI-generated code, hard to toggle/see code. Cursor: Great Copilot alternative, but lacks extensive agentic capabilities like Cline. Codeium Windsurf: Strong agent mode but agent was sometimes lazy and incomplete. GitHub Copilot: Good for simple inline edits, but lacks full agentic workflow (though an agent mode was recently released). Aider: Terminal & keyboard only. Feels like Vim/Emacs on steroids. Too hardcore. OpenHands: Open-source and free Cognition Devin with strong agentic coding, but SaaS version is unstable. OpenAI (o3-mini-high): Good logic depth but lacks a coding canvas. Anthropic (Claude 3.5 Sonnet): Fast + clean. Artifact is great for prototypes, but can’t edit code directly inside it. Google Gemini 2: Poor experience—lazy, incomplete code. Generated separate files that I had to manually combine. DeepSeek AI R1: Strong long reasoning chains, but gets a lot of logic wrong. Tempo (YC S23): Promising PRD → Design → Code → Deploy workflow, but still in early stages. Onlook: Strong for design-first workflows but inconvenient for direct code editing. Reweb: Generates only UI components, not code with logic. My Final Recommendations: - For non-technical users: Vercel V0 is the best no-code/low-code option. - For cloud-based development: Try Bolt. - For local AI-powered coding: Cline is free and outperforms Cursor/Codeium. - For rapid prototyping: Claude 3.5 Sonnet is fast and effective. - For designers: Tempo or Onlook provide a strong UI-first workflow. Do you want to see a full write up of my AI coding experiences? Let me know if I should make a full post comparing AI Coding tools in detail by sharing this post and commenting below.

AI security/securing the use of AI is going to kill me. I use Claude Code almost daily. It's a problem.... Here's what I have to change AGAIN this week. Security researcher Ari Marzuk disclosed 30+ vulnerabilities across AI coding tools. Cursor. GitHub Copilot. Windsurf. Claude Code. All of them. He called it IDEsaster. The attack chain includes prompt injection, hijacking LLM context, and auto-approved tool calls executing without permission. Then, legitimate IDE features are weaponized for data exfiltration and RCE. Your .env files. Your API keys. Your source code. Accessible through features you thought were safe. Most studies I read claim that around 85% of developers now use AI coding tools daily. Most have no idea their IDE treats its own features as inherently trusted. 𝗦𝗼... 𝗮𝗳𝘁𝗲𝗿 𝗿𝗲𝘃𝗶𝗲𝘄𝗶𝗻𝗴 𝗔𝗿𝗶'𝘀 𝗿𝗲𝘀𝗲𝗮𝗿𝗰𝗵, 𝗵𝗲𝗿𝗲'𝘀 𝗜 𝘄𝗶𝗹𝗹 𝗯𝗲 𝗱𝗼𝗶𝗻𝗴... Be warned: All this is SO much easier said than done! Audit every MCP server connection. Checked for tool poisoning vectors where legitimate tools might parse attacker-controlled input from GitHub PRs or web content. Removed servers I couldn't verify. Disabled auto-approve for file writes. The attack chains weaponize configuration files and project instructions like .claude/settings.json and CLAUDE.md. One malicious write to these files can alter agent behavior or achieve code execution without additional user interaction. Move all credentials to a secrets manager. No .gitignored .env files in agent-accessible directories. API keys live in 1Password CLI. Environment variables inject at runtime through a wrapper script the LLM never sees. Start running Claude Code in isolated containers. Mounted volumes limited to specific project directories. No access to ~/.ssh, ~/.aws, or ~/.config. If the agent gets compromised, blast radius stays contained. Enable all security warnings. Claude Code added explicit warnings for JSON schema exfiltration and settings file modifications. These exist because Anthropic knows the attack surface. Add pre-commit hooks for hidden characters. Prompt injections hide in pasted URLs, READMEs, and file names using invisible Unicode. Flag non-ASCII characters in any file the agent might ingest. The fix isn't to stop using AI coding tools. The fix is to stop trusting them implicitly. What controls do you have for AI tools with write access to your codebase? 👉 Follow for more AI and cybersecurity insights with the occasional rant #AISecurity #DevSecOps