AI Agent Features

If AI in your company still lives inside chat windows… you haven’t started the Agentic journey yet. Today’s Agentic AI systems don’t just answer questions. They observe signals, make decisions, trigger tools, coordinate workflows, and continuously improve outcomes. Instead of assisting humans one task at a time, these agents run end-to-end business operations across sales, support, finance, engineering, HR, and marketing. This is what production-grade Agentic AI actually looks like inside modern organizations: - Customer Support Agents Handle FAQs, resolve tickets, process refunds, update CRM systems, and escalate complex issues automatically. - Sales Ops Agents Qualify incoming leads, enrich prospect data, update pipelines, generate follow-ups, and notify sales teams in real time. - Marketing Automation Agents Plan campaigns, analyze audiences, generate content, schedule outreach, track performance, and optimize future runs. - Data Analysis Agents Convert business questions into SQL, clean datasets, analyze trends, generate insights, and deliver visual summaries. - Reporting Agents Pull metrics, validate data, create dashboards, write narratives, and distribute reports across stakeholders automatically. - QA / Testing Agents Generate test cases, execute regressions, detect failures, log bugs, and recommend fixes without manual intervention. - DevOps Agents Monitor infrastructure, detect anomalies, run diagnostics, apply rollbacks, notify teams, and assist deployments. - Finance Ops Agents Process invoices, categorize transactions, reconcile records, flag anomalies, and generate financial summaries. - HR Ops Agents Manage resume intake, screen candidates, schedule interviews, update HR systems, and respond to employee queries. - Research Agents Search documents and web sources, extract key findings, compare references, and summarize insights. - Content Creation Agents Outline topics, draft content, optimize for SEO and branding, publish assets, and track engagement end-to-end. - Internal Tools Agents Act as company copilots - understanding employee requests, calling internal APIs, executing actions, and confirming results. The real shift? These agents don’t just respond. They reason. They orchestrate tools. They execute workflows. They learn from feedback. They operate continuously. This is how organizations move from isolated automation to connected, outcome-driven AI systems. Not experiments. Not demos. Not pilots. Real production systems.

We used to browse the internet. Soon, it’ll browse for us. The AI browser wars are just beginning, with Chrome, Comet (Perplexity) and Atlas (OpenAI) competing for the future of work. The browser used to be a passive shell. You searched, clicked, and navigated. AI browsers act, infer, and execute. Under the hood, most of them still run on Chromium. The difference lies in memory, context, and orchestration. Arc is rebuilding the user experience: cleaner design, smart tabs, and adaptive workflows. Comet leans agentic. It reads, fills, books, and compares for you. Atlas pushes further with persistent memory and API-level autonomy, turning the web into a workspace. These browsers are trying to out-execute Google, making the web a programmable layer that agents can act on safely. This is the start of the agentic web, where AI systems transact across sites, compare, verify, and close the loop. Search collapses into action. Monetization shifts from ads to execution. The endgame is negotiation: AI will browse, transact, and orchestrate across the internet while you oversee outcomes, not clicks.

The browser: the front line in the race to train AI. Perplexity just launched Comet. OpenAI is about to release its own browser. Google is defending its core business from both. Three players. One battleground: the browsing layer: where user behaviour is captured, interpreted, and transformed into the training data that shapes tomorrow’s models. For years, Google controlled the loop: Chrome collected user activity → Search interpreted intent → Ads monetized attention → Models improved in the background. Now, that loop is being re-engineered: 🔸 Comet puts an AI assistant at the centre of the browsing: summarising, acting, executing on your behalf. 🔸 OpenAI will go further with Operator, an embedded agent that performs multi-step tasks inside webpages. 🔸 Both are designed to turn every interaction into a signal for model fine-tuning and product evolution. The goal is data capture at the edge: building closed feedback loops to train increasingly adaptive, agentic systems. And this raises a key question for Europe. Because these data flows are live, granular traces of thought, preference, and decision-making. From a GDPR perspective, this introduces deep friction. 🔹 Personal data used for model training must meet strict consent, purpose limitation, and transparency requirements - conditions U.S.-based models currently struggle to satisfy. 🔹 Real-time, agent-driven data collection raises hard questions about legal basis, user control, and cross-border data transfers. Which brings us to privacy and regulation. 📍Comet promises local data storage and a no-training-on-personal-data stance. 📍 OpenAI is explicitly building its browser to observe user interactions for feeding its AI models. The alternative? A European AI browser ecosystem grounded in privacy-by-design. Qwant (France-based), and OpenWebSearch.eu are building AI-enhanced tools aligned with EU values: ▫️Local or anonymous data processing ▫️ Opt-in model usage ▫️ User agency at the centre ❗️This is a battle over who trains on the world’s digital behaviour and under what conditions. The next generation of AI is being shaped in the browser. And how EU responds will define not just competition, but trust, control, and compliance. #AI #data #AIgovernance #GDPR #stratedge

The search bar is dead. And most e-commerce platforms don’t even know it yet. After working closely with AI systems and recommendation engines, I’ve learned one thing: “Personalized shopping” was never truly personal. It was pattern matching. It was collaborative filtering. It was reactive logic pretending to be intelligence. Now we’re entering a different era. → From personalized to personal → From search-based discovery to proactive intelligence → From browsing endlessly to AI agents working for you This is agentic commerce. Traditional e-commerce makes you do the heavy lifting: Search → Filter → Scroll → Compare → Hope Agentic commerce flips the entire model: Describe what you want → AI delivers with context One of the most interesting examples I’ve seen is Glance. They are not building another shopping app. They’re building a contextual, agentic AI commerce layer powered by multiple specialised agents working together. Instead of one algorithm guessing what you like, Glance deploys multiple AI agents working for you in parallel: → Weather Agent analysing real-time climate and fabric suitability → Trends Agent tracking global shifts and micro-trends → Occasions Agent anticipating upcoming events → Physical Agent understanding your skin tone, undertones, and body type → Lifestyle Agent decoding your aesthetic preferences All coordinated by an orchestrator that synthesises everything into a unified styling strategy. That’s not basic personalization. That’s contextual intelligence. And the most powerful shift? You see yourself in the generated looks. Not stock visuals. Not generic models. You. Commerce becomes a conversation instead of a search box. From personalized to personal. AI agents working for you. Learning with every interaction. Refining your style instead of just tracking clicks. This is the rise of agentic commerce. #Glance #AICommerce #AgenticAI

🚀 𝗙𝗿𝗼𝗺 𝗢𝗻𝗲 𝗧𝗲𝘅𝘁 𝗕𝗼𝘅 𝘁𝗼 𝗮 𝗪𝗼𝗿𝗹𝗱 𝗼𝗳 𝗖𝗼𝗻𝘃𝗲𝗿𝘀𝗮𝘁𝗶𝗼𝗻𝘀. Remember when Google started with just one simple text box? You’d type a word, hit enter, and suddenly, Pandora’s box of information opened up. That single input field reshaped how the world accessed knowledge. Today, we’re on the edge of another transformation with Conversational UX and AI. We no longer just “search” or “click.” We ask, we interact, we converse. If we go back to classic UX heuristics, most digital experiences have always revolved around two modes: 𝗯𝗿𝗼𝘄𝘀𝗲 𝗼𝗿 𝘀𝗲𝗮𝗿𝗰𝗵. 👉 Browsing meant navigating categories, menus, filters. 👉 Searching meant typing and retrieving. Now, conversational AI blends these two, removing the friction of browsing while keeping the precision of search. The result? A dialogue where users don’t have to adapt to the system, the system adapts to them. At SilverFern Digital, we often ask ourselves: what is it that’s really making AI work today? And a funny observation came to my mind: AI isn’t inventing an entirely new way of interacting, it’s simply making the old way powerful again. We spent decades training people to use menus, buttons, filters, and forms and now we’re circling back to the most natural UX of all: just talking. Instead of spending 15 minutes setting filters on a travel website, you just say “Find me a beach destination under 4 hours away with flights under $300.” Instead of being overwhelmed by hospital portals, you ask “When’s my next appointment, and can I move it to next Tuesday?” Instead of scrolling through product reviews, you ask “Is this laptop good for video editing?” This is more than a shift in interface, it’s a shift in expectation. Users won’t tolerate complexity when a simple question can do the job. I can already imagine telling kids 10 years from now: “𝘞𝘦 𝘰𝘯𝘤𝘦 𝘩𝘢𝘥 𝘵𝘰 𝘴𝘤𝘳𝘰𝘭𝘭 𝘵𝘩𝘳𝘰𝘶𝘨𝘩 𝘩𝘶𝘯𝘥𝘳𝘦𝘥𝘴 𝘰𝘧 𝘎𝘰𝘰𝘨𝘭𝘦 𝘭𝘪𝘯𝘬𝘴 𝘵𝘰 𝘧𝘪𝘯𝘥 𝘢𝘯 𝘢𝘯𝘴𝘸𝘦𝘳, 𝘪𝘵 𝘸𝘢𝘴𝘯’𝘵 𝘦𝘢𝘴𝘺.” And they’ll laugh, because for them, the answer will always just be one conversation away.

Want to understand agentic commerce? This is a breakdown of the emerging stack and who does what. 𝟭. 𝗙𝗼𝘂𝗻𝗱𝗮𝘁𝗶𝗼𝗻 𝗺𝗼𝗱𝗲𝗹𝘀 Models such as OpenAI, Anthropic, Meta, xAI provide the reasoning layer that allows agents to interpret instructions, plan actions and make decisions. Without this layer, there are no autonomous agents. 𝟮. 𝗜𝗻𝗳𝗿𝗮𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲 Providers such as AWS, Google Cloud, Cloudflare, Akash supply the compute and networking needed to run models and agents continuously. This is the infrastructure layer of the agent economy. 𝟯. 𝗔𝗴𝗲𝗻𝘁 𝗳𝗿𝗮𝗺𝗲𝘄𝗼𝗿𝗸𝘀 Frameworks like MCP and A2A allow developers to build agents that can call APIs, access services and coordinate tasks. This layer enables models to operate as agents. 𝟰. 𝗔𝗴𝗲𝗻𝘁 𝗻𝗲𝘁𝘄𝗼𝗿𝗸𝘀 Protocols such as Virtuals Protocol, Bittensor or Heurist allow agents to collaborate and coordinate with other agents rather than operating individually. These networks provide shared environments where agents can exchange tasks and services. 𝟱. 𝗗𝗶𝘀𝗰𝗼𝘃𝗲𝗿𝘆 Before an agent can act, it must discover available services, APIs or resources. Tools like x402scan and Unicity Labs allow agents to discover APIs, services or payment endpoints across the ecosystem. 𝟲. 𝗜𝗱𝗲𝗻𝘁𝗶𝘁𝘆 & 𝘁𝗿𝘂𝘀𝘁 Agents must prove who they are and whether they can be trusted. Protocols such as ERC-8004, Cred Protocol, AgentProof provide identity and and verifiable credentials so agents can transact securely. 𝟳. 𝗙𝗮𝗰𝗶𝗹𝗶𝘁𝗮𝘁𝗼𝗿𝘀   Platforms like Stripe, Coinbase, Openx402, thirdweb connect agents to services, payments and workflows. They act as the execution layer that lets agents actually do things. 𝟴. 𝗪𝗮𝗹𝗹𝗲𝘁𝘀 & 𝗮𝗰𝗰𝗼𝘂𝗻𝘁 𝗶𝗻𝗳𝗿𝗮𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲 Solutions such as Privy, MetaMask, Fireblocks, Coinbase Wallet allow agents to hold assets, manage keys and sign transactions. Technologies like ERC-4337 simplify account management so agents can transact programmatically. 𝟵. 𝗣𝗮𝘆𝗺𝗲𝗻𝘁 𝗶𝗻𝗳𝗿𝗮𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲  Infrastructure such as x402, Stripe, Visa, Crossmint, Moonpay enables automated payments and settlement. This is what allows agents to pay for services or receive payments automatically. 𝟭𝟬. 𝗕𝗹𝗼𝗰𝗸𝗰𝗵𝗮𝗶𝗻𝘀  Networks like Base, Solana, Polygon, Avalanche, Arbitrum provide the settlement and execution environment where transactions are recorded. 𝟭𝟭. 𝗦𝘁𝗮𝗯𝗹𝗲𝗰𝗼𝗶𝗻𝘀  Assets such as USDC and USDT provide programmable digital money that agents can move instantly across networks. For many agent transactions, stablecoins act as the settlement asset. 𝟭𝟮. 𝗨𝘀𝗲𝗿 𝗶𝗻𝘁𝗲𝗿𝗳𝗮𝗰𝗲𝘀  Interfaces such as ChatGPT, Claude or Gemini are becoming the entry point where humans interact with agents and delegate tasks. These interfaces increasingly act as the control layer for agent activity. Opinions: my own, Graphic source: Artemis Analytics   𝐒𝐮𝐛𝐬𝐜𝐫𝐢𝐛𝐞 𝐭𝐨 𝐦𝐲 𝐧𝐞𝐰𝐬𝐥𝐞𝐭𝐭𝐞𝐫: https://lnkd.in/dkqhnxdg

Workflow Agents in #Oracle_Fusion_AI_Agent_Studio are redefining what “#Enterprise_AI_automation” actually means. Most tools can run steps. Some tools can call an LLM. But Workflow Agents do something much bigger---->> they combine deterministic control flow, reasoning, memory, and multi-agent orchestration directly inside the systems that run the business. Here are 4 patterns that give them some real power: 1. Chaining — Step-by-step intelligence Every step interprets context, transforms data, and feeds the next. Perfect for real enterprise flows with dependencies: onboarding, validation, document-to-decision processes, and month-end close. 2. Parallel — Collective decisioning at speed Multiple branches run at once: diagnostics, policy checks, data lookups, history, extraction. Everything merges into a single, high-quality decision. Faster outcomes with better signal coverage. 3. Switch — Context-aware routing without rule bloat Instead of giant rule trees, the workflow adapts to user, policy, intent, and application state on the fly. Same entry point, personalized paths. Automation that’s flexible, not fragile. 4. Iteration — Goal-seeking refinement Great for scheduling, planning, allocation, cost modeling. The agent loops intelligently until constraints are met. Not “first viable answer” — the right answer. This is only one layer of the bigger story. Fusion supports the full spectrum of AI automation: - Workflows for structure. - Workflow Agents for structure with reasoning. - Agent Teams for autonomous digital workers that pursue outcomes. And because all of this lives inside Oracle Fusion Applications, the automation is grounded in real Fusion data, policies, security, and transactions from the start. Enterprise AI that actually does the work — #built_in_not_bolted_on.

The End of Websites as We Know Them? What happens when AI does the searching and the browsing for you? For decades, the internet was built on one assumption: users visit websites to find what they need. That’s about to change. They’ll say websites will always be the foundation of the internet. They’ll argue businesses need direct traffic to survive. Maybe that was true—before AI became the new front door to the web. 🚨 The Old Web Model: • Users search → Click a link → Navigate a website • Businesses fight for SEO rankings to drive traffic • Monetisation depends on ads and direct engagement ✅ The AI-Powered Internet: • Users ask AI → AI pulls answers without needing a click • AI acts as an intermediary, filtering and synthesising content • Traffic bypasses traditional websites altogether This shift will break entire industries built on website visits: - SEO & digital marketing → Optimising for Google may be irrelevant if AI controls discovery - Advertising models → If fewer people visit websites, ad revenue collapses - E-commerce → AI-driven purchasing decisions could cut brands out of the equation - Media & publishing → News aggregators on steroids, but AI-generated So, what’s next? If AI is the interface, businesses must rethink their entire digital strategy. Instead of fighting for traffic, they’ll need to focus on being the source AI trusts and references. 🔹 Content must be structured, machine-readable, and high-authority 🔹 APIs over webpages—businesses will integrate directly into AI agents 🔹 Trust, credibility, and brand authority will become the ultimate competitive advantage The web is no longer a collection of pages—it’s becoming an AI-driven knowledge fabric. Are we ready for a world where websites are just the backend, and AI is the only “user” that actually visits?

Have you noticed how AI companies are suddenly launching their own browsers? Yes, this is a significant trend. OpenAI recently introduced Atlas and Perplexity launched Comet. At first glance, it might seem odd. Why would AI companies build something as “basic” as a browser? If you look closer, the answer becomes clearer. A browser is still the gateway to the internet. Even in a world of apps, most of our access to the web begins here. We search, read, and switch between tabs within it. By owning the browser, an AI company captures that gateway. Atlas aims to become your personal assistant across tabs, while Comet focuses on AI-driven productivity by integrating tasks, search, and summaries in one seamless flow. Here’s the fundamental reason. If users set your browser as default, and your search or assistant sits front and center, you are no longer just a tool, you become the platform. You start controlling the context of use, the data flow, and the entry point. From there, you can embed your AI, understand user behavior, and personalise experiences. But it all begins with owning the access gate. Of course, there are secondary reasons too. Embedded AI can summarise tabs, personalise content, automate workflows, and improve productivity. Features like memory, agents, and context awareness are how these browsers evolve into ecosystems. But without that “default browser” status, these features would remain at the edges. So next time a browser prompts you to “Make this your default browser,” take note. It’s not just about convenience. It’s the first step in making you part of a tech ecosystem, where the company behind the browser doesn’t just sit on top of the web, it lives inside it. Chrome still holds the largest market share among browsers, and it will be interesting to see how that changes or how Chrome itself evolves in this new wave of AI browsers.

Perplexity just announced they're building their own web browser called Comet for Agentic Search. Well done, Aravind Srinivas & team Perplexity! 👏🏽👏🏽👏🏽 This is a fascinating strategic move that has significant implications for the AI search wars - and it's also classic vertical integration. Perplexity is trying to move from being just an AI search application to controlling the full browsing experience. It's reminiscent of what we've seen repeatedly in tech: Amazon going from marketplace to logistics; Apple controlling both hardware and software; Tesla building their own batteries. The browser market is incredibly crowded, dominated by Chrome with established players like Safari, Firefox, and Edge. We're also seeing innovation from newcomers like The Browser Company's Arc and their upcoming Dia browser. But Perplexity has a unique advantage - they've already built a substantial user base doing over 100M weekly queries. They're attempting to leverage this to gain browser market share in a way that enhances their core search product. Perplexity’s product portfolio is growing at a rapid clip. Just this month, the company released a “deep research” product to rival offerings from OpenAI, Google, and xAI. That followed on the heels of two big debuts in January: an AI-powered assistant for Android and an API for AI search. What's clear is that Perplexity recognizes the browser is the gateway to search. By controlling this entry point, they can create a more seamless AI-powered experience while reducing their dependence on other browsers' limitations. Their browser strategy could either exacerbate tensions with publishers (who are suing them) or potentially give them new ways to address publisher concerns with built-in attribution and revenue sharing mechanisms. This move highlights that the real battleground for AI isn't just about building the best models - it's about controlling the user experience layer. Whoever owns the interface through which people access information will have tremendous influence over the #AI ecosystem. Are you AI ready?