AI Agent Features

As we move from LLM-powered chatbots to truly 𝗮𝘂𝘁𝗼𝗻𝗼𝗺𝗼𝘂𝘀, 𝗱𝗲𝗰𝗶𝘀𝗶𝗼𝗻-𝗺𝗮𝗸𝗶𝗻𝗴 𝘀𝘆𝘀𝘁𝗲𝗺𝘀, understanding 𝗔𝗜 𝗮𝗴𝗲𝗻𝘁𝘀 becomes non-negotiable. Agentic AI isn’t just about plugging an LLM into a prompt—it’s about designing systems that can 𝗽𝗲𝗿𝗰𝗲𝗶𝘃𝗲, 𝗽𝗹𝗮𝗻, 𝗮𝗰𝘁, 𝗮𝗻𝗱 𝗹𝗲𝗮𝗿𝗻 in dynamic environments. Here’s where most teams struggle:  They underestimate the 𝗮𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲 required to support agent behavior. To build effective AI agents, you need to think across four critical dimensions: 1. 𝗔𝘂𝘁𝗼𝗻𝗼𝗺𝘆 & 𝗣𝗹𝗮𝗻𝗻𝗶𝗻𝗴 – Agents should break down goals into executable steps and act without constant human input. 2. 𝗠𝗲𝗺𝗼𝗿𝘆 & 𝗖𝗼𝗻𝘁𝗲𝘅𝘁 – Agents need long-term and episodic memory. Vector databases, context windows, and frameworks like Redis/Postgres are foundational. 3. 𝗧𝗼𝗼𝗹 𝗨𝘀𝗮𝗴𝗲 & 𝗜𝗻𝘁𝗲𝗴𝗿𝗮𝘁𝗶𝗼𝗻 – Real-world agents must invoke APIs, search tools, code execution engines, and more to complete complex tasks. 4. 𝗖𝗼𝗼𝗿𝗱𝗶𝗻𝗮𝘁𝗶𝗼𝗻 & 𝗖𝗼𝗹𝗹𝗮𝗯𝗼𝗿𝗮𝘁𝗶𝗼𝗻 – Single-agent systems are powerful, but multi-agent orchestration (planner-executor models, role-based agents) is where scalability emerges. The ecosystem is evolving fast—with frameworks like 𝗟𝗮𝗻𝗴𝗚𝗿𝗮𝗽𝗵, 𝗔𝘂𝘁𝗼𝗚𝗲𝗻, 𝗟𝗮𝗻𝗴𝗖𝗵𝗮𝗶𝗻, and 𝗖𝗿𝗲𝘄𝗔𝗜 making it easier to move from prototypes to production. But tools are only part of the story. If you don’t understand concepts like 𝘁𝗮𝘀𝗸 𝗱𝗲𝗰𝗼𝗺𝗽𝗼𝘀𝗶𝘁𝗶𝗼𝗻, 𝘀𝘁𝗮𝘁𝗲𝗳𝘂𝗹𝗻𝗲𝘀𝘀, 𝗿𝗲𝗳𝗹𝗲𝗰𝘁𝗶𝗼𝗻, and 𝗳𝗲𝗲𝗱𝗯𝗮𝗰𝗸 𝗹𝗼𝗼𝗽𝘀, your agents will remain shallow, brittle, and unscalable. The future belongs to those who can 𝗰𝗼𝗺𝗯𝗶𝗻𝗲 𝗟𝗟𝗠 𝗰𝗮𝗽𝗮𝗯𝗶𝗹𝗶𝘁𝗶𝗲𝘀 𝘄𝗶𝘁𝗵 𝗿𝗼𝗯𝘂𝘀𝘁 𝘀𝘆𝘀𝘁𝗲𝗺 𝗱𝗲𝘀𝗶𝗴𝗻. That’s where real innovation happens. 2025 will be the year we go from prompting to architecting.

This week’s Spotlight is: The Future of Sales and Role of the CRO The CRO role is being redesigned. For decades, revenue leadership meant managing pipelines, arguing over forecast math, judgment calls, and carrying a number into a board meeting. The CRO was the overall quota owner and enforcer. AI agents change that entirely. When agents absorb the invisible work of selling, the Orchestrator role emerges: designing an intelligent revenue system where humans and agents co-own outcomes. The future CRO becomes the Chief Revenue Orchestrator. In the agentic era, the CRO becomes the orchestrator of the revenue system and owns these 4 roles: 1. Chief Growth Systems Designer 2. Chief Forecast Intelligence Officer 3. Chief Agent Governor 4. Chief Revenue Connector The Revenue Orchestrator’s role is intense, but controlled. They have earlier visibility, fewer surprises, and authority rooted in evidence. I’m optimistic about the future of SaaS in the AI era. The revenue teams that lean into this transition gain a structural advantage: faster learning cycles, better decisions, and more predictable outcomes. This shift is already visible across this week’s signals. Anthropic’s expansion of enterprise-grade agents and managed deployments points to more structured, controllable AI systems inside organizations. Atlassian's embedding of AI agents directly into workflows reflects how sales and collaboration are becoming system-driven. At the infrastructure layer, massive compute commitments from Meta and others reinforce that the foundation for always-on, agent-led revenue systems is being built now. Full Weekend Edition below. 👇

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.

I woke up at 6am to a message from an AI agent. It had gone through two years of our internal reports overnight, built an analysis framework, and was asking me follow-up questions about our revenue mix. Not a summary — a working model of our business I could actually use. Let me back up. Last week, me and my cofounders sat down for our annual strategy offsite. We'd planned to map out 2026. Instead, we spent the days deploying AI agents across our operations using OpenClaw. Sales, finance, content, project management — all running on our own infrastructure, our data staying exactly where it should. The first two days, honestly, felt like we were working for the AI. Setting up connectors, explaining how we make decisions, feeding it context about clients and processes. You're essentially onboarding a new team member — except this one never forgets and never sleeps. Day three, something flipped. Agents that needed hand-holding on Monday were autonomously executing by Wednesday. One connected our sales pipeline to project tracking and started flagging overdue follow-ups. Another drafted a partnership analysis better than what most consultants would deliver. By Friday we'd stopped thinking of them as tools and started treating them as colleagues. The compound effect is what gets you. These agents build institutional knowledge that grows daily. The gap between companies deploying this now and those that start in six months isn't about productivity — it's structural. We're a boutique outfit. We now operate with the bandwidth of a team twice our size. And every week the multiplier grows. Going to share what we're learning — what works, what breaks, the decisions that matter. Follow along if you're building something similar. #AI #Agents #OpenClaw #Leadership #Future

A new open-source Python library called TinyTroupe is here to redefine how we simulate human behavior using LLMs, advancing the field of AI agents. TinyTroupe allows you to create TinyPersons – simulated agents with unique personalities, goals, and interests – capable of interacting within custom TinyWorld environments. Unlike other LLM-based simulation approaches that focus on gaming, this library targets business scenarios, creating and interacting with AI-powered personas to test products, ads, and ideas before spending real money. Think running a focus group with AI-powered physicians, lawyers, or knowledge workers. The library enables diverse applications, from evaluating digital campaigns with simulated audiences and running AI-powered focus groups at scale to generating realistic test inputs for software, collecting requirements from specific personas, and creating domain-specific training datasets. This work could accelerate research in autonomous AI agents by providing a controlled environment to study agent-to-agent and human-to-agent interactions, such as in customer support and sales. Code and examples https://lnkd.in/g9TqYiVZ P.S. I've just open-sourced Voice Lab, a framework to evaluate LLM-powered agents across different models, prompts, and personas https://lnkd.in/gAaZ-tkA

If you’re an aspiring AI engineer trying to understand how the industry is moving beyond LLMs, here’s a quick eagle’s-eye view of one of the most fascinating frontiers in AI today: 𝗔𝗴𝗲𝗻𝘁𝗶𝗰 𝗔𝗜 𝗦𝘆𝘀𝘁𝗲𝗺𝘀. We’ve reached a point where large language models can generate text, summarize papers, write code, and even reason, but that’s not enough anymore. The next leap isn’t about bigger models. It’s about autonomy, with systems that can not only generate but also decide, act, and adapt in the real world. That’s where Agentic AI Systems come in. These are goal-driven, adaptive platforms capable of orchestrating complex workflows, making independent decisions, and using memory to 𝗥𝗲𝗮𝘀𝗼𝗻 → 𝗔𝗰𝘁 → 𝗔𝗱𝗮𝗽𝘁. Instead of just prompting a model for a single response, you’re designing a network of intelligent components that: → Understand goals and constraints → Plan actions through orchestration frameworks → Execute via tools, APIs, or other agents → Observe results, learn, and improve over time This shift, from intelligence to autonomous intelligence, is why agentic systems have become one of the most important topics for modern AI engineers. 𝗪𝗵𝘆 𝗧𝗵𝗶𝘀 𝗠𝗮𝘁𝘁𝗲𝗿𝘀 → For AI Engineers: Agentic architectures are redefining how applications are built- from RAG pipelines and copilots to autonomous research or data systems. Understanding gateways, planners, orchestrators, memory layers, and evaluation loops will become a must-have skill set. → For Tech Leaders: If you’re leading teams or evaluating where AI fits into your business, this is your blueprint for understanding how next-gen systems will operate- safely, scalably, and with clear policy and observability layers. Happy learning & Happy Building 🚀

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

This is high potential. Researchers have generated an "agent bank" of over 1000 AI agents that each accurately simulate a real human. Extended interviews and effective agent design enabled 85% predictive accuracy for replicating attitudes and behaviors using the General Social Survey. The agents replicated humans results in most behavioral experiments, with effect sizes showing a correlation of 0.98 to human participants, who themselves showed a 0.99 internal consistency. Interestingly the lead author Joon Sung Park created the viral "town of agents" last year where some agents ran for mayor as they self-organized (link in comments). Stanford University is now making this "agent bank" available to approved researchers to conduct social science experiments. This could have great value not only for research, but also for policy makers in understanding the sometimes-unintended social consequences of initiatives. There are of course a range of risks in making the agent bank available, and while Standford is providing open access to aggregated responses, it is reviewing requests to access individual responses. There are of course a whole set of broader implications from creating human-based agent environments. I'll discuss some of these in upcoming posts.

You see abstract AI agent architectures everywhere, But how to build them in practice? We often use terms like AI tool, chatbot, and #AIagent interchangeably. But underneath the surface, there’s a major architectural difference and it’s reshaping how we build and interact with #intelligentsystems. 🔴 𝗪𝗵𝗮𝘁 𝗶𝘁 i𝘀 𝗻𝗼𝘁: A standard #LLM pipeline is reactive. It takes input → generates a response → maybe uses a tool → and returns output. There’s no memory. No goal-directed behavior. No feedback loops. It’s powerful — but passive. 🔴 𝗪𝗵𝗮𝘁 𝗶𝘁 𝗶𝘀: #AIagents are software systems that use AI to pursue goals and complete tasks on behalf of users. They reason, plan, and act — with memory and autonomy. And they operate in a continuous loop: 1️⃣ Think – Process data and context 2️⃣ Plan – Decide how to achieve the goal 3️⃣ Act – Execute via tools, APIs, or interfaces 4️⃣ Reflect – Evaluate results and adapt This feedback loop makes agents adaptive, iterative, and capable of learning. It’s not just generating text — it’s thinking in steps, remembering context, choosing tools, and deciding what to do next. It has three key traits: 𝟭.𝗠𝗲𝗺𝗼𝗿𝘆 – Retains past actions and results to inform future steps 𝟮.𝗗𝗲𝗰𝗶𝘀𝗶𝗼𝗻-𝗺𝗮𝗸𝗶𝗻𝗴 – Determines what to do, not just what to say 𝟯.𝗙𝗲𝗲𝗱𝗯𝗮𝗰𝗸 𝗹𝗼𝗼𝗽𝘀 – It learns and adapts within a single task or across multiple sessions 💡 This shift from static responses to autonomous reasoning is foundational in: • Autonomous coding agents • Workflow orchestrators • Multi-modal assistants • Task-solving copilots The age of prompt in → answer out is fading. We’re entering the era of goal in → strategy → action → result. And the difference between the two? It’s not just output. It’s autonomy. 𝗛𝗼𝘄 𝗔𝗴𝗲𝗻𝘁𝘀 𝗪𝗼𝗿𝗸: ➜ You delegate a task ➜ The agent takes autonomous action ➜ It connects to tools, APIs, or the web — uses memory, adapts to input ➜ You’re still in control — but it runs on its own Think of it as a smart intern that never sleeps — and keeps improving. 𝗧𝘆𝗽𝗲𝘀 𝗼𝗳 𝗔𝗜 𝗔𝗴𝗲𝗻𝘁𝘀: Different agents, different strengths — just like any team: ➜ Simple Reflex Agents = rule-based triggers ➜ Model-Based = uses memory to guide decisions ➜ Goal-Based = acts with outcomes in mind ➜ Utility-Based = weighs options and tradeoffs ➜ Learning Agents = continuously improve You wouldn’t run a business with just one intern — same goes for agents. #𝗔𝗴𝗲𝗻𝘁𝗔𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲𝘀 : How you structure your agents matters just as much as what they can do: ➜ Single Agent = task-specific assistant ➜ Multi-Agent = agents coordinate and collaborate ➜ Human-Machine = agents work with humans in the loop And this is where most enterprises still struggle — not with the technology, but with governance, security, and trust. As this paradigm evolves, companies that understand and apply these concepts will redefine efficiency, automation, and intelligence at scale.

Everything You Wanted to Know About AI Agents and Agentic AI (But Were Afraid to Ask) The Agentic AI market is on the brink of exponential growth—projected to rise from USD 5.2 billion in 2024 to USD 196.6 billion by 2034, growing at a CAGR of 43.8%. As the demand for autonomous, intelligent systems surges, understanding the terminology and architecture behind them becomes increasingly important. The Basics: Architecture vs. Components: Agentic AI vs AI Agents. A recent paper from researchers at Cornell University explores this distinction in depth, shedding light on key differences, real-world applications, emerging challenges, and future directions. While seasoned AI practitioners may be familiar with these concepts, the paper serves as an accessible starting point for anyone eager to dive deeper. ⸻ AI Agents: Specialized and Task-Oriented AI Agents are modular, single-entity systems, often powered by large language models (LLMs). They’re designed for narrow, well-defined tasks and operate with a degree of autonomy. Typical applications include: • Email triage • Report summarization • Customer service automation These agents are highly efficient and reactive but typically lack deep reasoning, long-term planning, or collaborative capabilities. ⸻ Agentic AI: A Systemic, Collaborative Intelligence Agentic AI represents a paradigm shift. Rather than a single agent handling isolated tasks, Agentic AI systems coordinate multiple agents with shared memory, adaptive task planning, and orchestration layers. Real-world use cases include: • Coordinated research assistants • Clinical decision support in ICUs • Robotic systems for precision agriculture These architectures are built for complex, critical workflows, but they come with their own set of challenges: coordination errors, system instability, scalability bottlenecks, and security risks. ⸻ What’s Next? AI Agents • Becoming more proactive • Learning continuously • Designed for greater safety and contextual awareness Agentic AI • Scaling up multi-agent systems • Leveraging simulation-based planning • Implementing domain-specific governance frameworks ⸻ The Cornell paper offers a timely and valuable lens into the evolving world of AI Agents and Agentic AI. As businesses and technologists navigate an increasingly autonomous future, understanding the distinction isn’t just helpful—it’s essential. Paper: https://lnkd.in/evu4d6Kt