AI Agent System Fundamentals

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?

Anthropic 𝗷𝘂𝘀𝘁 𝗿𝗲𝗹𝗲𝗮𝘀𝗲𝗱 𝗮 𝗱𝗲𝗻𝘀𝗲 𝗮𝗻𝗱 𝗵𝗶𝗴𝗵𝗹𝘆 𝗽𝗿𝗮𝗰𝘁𝗶𝗰𝗮𝗹 𝗿𝗲𝗽𝗼𝗿𝘁 𝗼𝗻 𝗵𝗼𝘄 𝘁𝗼 𝗯𝘂𝗶𝗹𝗱 𝗲𝗳𝗳𝗲𝗰𝘁𝗶𝘃𝗲 𝗔𝗜 𝗮𝗴𝗲𝗻𝘁𝘀 — 𝗽𝗮𝗰𝗸𝗲𝗱 𝘄𝗶𝘁𝗵 𝗲𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴 𝗶𝗻𝘀𝗶𝗴𝗵𝘁𝘀 𝗳𝗿𝗼𝗺 𝗿𝗲𝗮𝗹-𝘄𝗼𝗿𝗹𝗱 𝗱𝗲𝗽𝗹𝗼𝘆𝗺𝗲𝗻𝘁𝘀: ⬇️ Not just marketing, BUT a real, practical blueprint for developers and teams building AI agents that actually work. It explains how Claude Code (tool for agentic coding) can function as a software developer: writing, reviewing, testing, and even managing Git workflows autonomously. BUT in my view: The principles and patterns described in this document are not Claude-specific. You can apply them to any coding agent — from OpenAI’s Codex to Goose, Aider, or even tools like Cursor and GitHub Copilot Workspace. 𝗛𝗲𝗿𝗲 𝗮𝗿𝗲 7 𝗸𝗲𝘆 𝗶𝗻𝘀𝗶𝗴𝗵𝘁𝘀 𝗳𝗼𝗿 𝗯𝘂𝗶𝗹𝗱𝗶𝗻𝗴 𝗯𝗲𝘁𝘁𝗲𝗿 𝗔𝗜 𝗮𝗴𝗲𝗻𝘁𝘀 — 𝘁𝗵𝗮𝘁 𝘄𝗼𝗿𝗸 𝗶𝗻 𝘁𝗵𝗲 𝗿𝗲𝗮𝗹 𝘄𝗼𝗿𝗹𝗱: ⬇️ 1. 𝗔𝗴𝗲𝗻𝘁 𝗱𝗲𝘀𝗶𝗴𝗻 ≠ 𝗷𝘂𝘀𝘁 𝗽𝗿𝗼𝗺𝗽𝘁𝗶𝗻𝗴 ➜ It’s not about clever prompts. It’s about building structured workflows — where the agent can reason, act, reflect, retry, and escalate. Think of agents like software components: stateless functions won’t cut it. 2. 𝗠𝗲𝗺𝗼𝗿𝘆 𝗶𝘀 𝗮𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲 ➜ The way you manage and pass context determines how useful your agent becomes. Using summaries, structured files, project overviews, and scoped retrieval beats dumping full files into the prompt window. 3. 𝗣𝗹𝗮𝗻𝗻𝗶𝗻𝗴 𝗶𝘀𝗻’𝘁 𝗼𝗽𝘁𝗶𝗼𝗻𝗮𝗹 ➜ You can’t expect an agent to solve multi-step problems without an explicit process. Patterns like plan > execute > review, tool use when stuck, or structured reflection are necessary. And they apply to all models, not just Claude. 4. 𝗥𝗲𝗮𝗹-𝘄𝗼𝗿𝗹𝗱 𝗮𝗴𝗲𝗻𝘁𝘀 𝗻𝗲𝗲𝗱 𝗿𝗲𝗮𝗹-𝘄𝗼𝗿𝗹𝗱 𝘁𝗼𝗼𝗹𝘀 ➜ Shell access. Git. APIs. Tool plugins. The agents that actually get things done use tools — not just language. Design your agents to execute, not just explain. 5. 𝗥𝗲𝗔𝗰𝘁 𝗮𝗻𝗱 𝗖𝗼𝗧 𝗮𝗿𝗲 𝘀𝘆𝘀𝘁𝗲𝗺 𝗽𝗮𝘁𝘁𝗲𝗿𝗻𝘀, 𝗻𝗼𝘁 𝗺𝗮𝗴𝗶𝗰 𝘁𝗿𝗶𝗰𝗸𝘀 ➜ Don’t just ask the model to “think step by step.” Build systems that enforce that structure: reasoning before action, planning before code, feedback before commits. 6. 𝗗𝗼𝗻’𝘁 𝗰𝗼𝗻𝗳𝘂𝘀𝗲 𝗮𝘂𝘁𝗼𝗻𝗼𝗺𝘆 𝘄𝗶𝘁𝗵 𝗰𝗵𝗮𝗼𝘀 ➜ Autonomous agents can cause damage — fast. Define scopes, boundaries, fallback behaviors. Controlled autonomy > random retries. 7. 𝗧𝗵𝗲 𝗿𝗲𝗮𝗹 𝘃𝗮𝗹𝘂𝗲 𝗶𝘀 𝗶𝗻 𝗼𝗿𝗰𝗵𝗲𝘀𝘁𝗿𝗮𝘁𝗶𝗼𝗻 ➜ A good agent isn’t just a wrapper around an LLM. It’s an orchestrator: of logic, memory, tools, and feedback. And if you’re scaling to multi-agent setups — orchestration is everything. Check the comments for the original material! Enjoy! Save 💾 ➞ React 👍 ➞ Share ♻️ & follow for everything related to AI Agents!

Your AI agent can book your flight, order your groceries, and snag those concert tickets ... but how does it pay for it all securely? This has been the critical, unanswered question for AI-driven commerce. Traditional payment systems are built for humans to click "buy," creating a massive gap in trust, authorisation, and accountability for agents. That's why I believe that Google Cloud's Agent Payments Protocol (AP2) is such a big deal. Developed in collaboration with over 60 industry leaders (like Mastercard, PayPal, Adyen, and Salesforce), AP2 is an open protocol that acts as the new "trust layer" for agent transactions. Here's the core idea: It uses Mandates which are tamper-proof, cryptographically-signed contracts that provide verifiable proof of your instructions. This creates a secure audit trail from your initial request (the "Intent") to the final, approved cart (the "Cart Mandate") and the payment itself. This is the foundational plumbing we need to unlock secure, autonomous commerce. It's a huge step forward in building a future where we can confidently delegate tasks to AI. The protocol is open for collaboration on GitHub. What do you think? Is this the missing link for mainstream agent-led commerce?

This image captures a pattern I keep seeing in real AI projects. We blame AI for being unreliable, unpredictable, or hallucinating. In practice, it is usually doing exactly what we asked, just without the context we assumed was obvious. After years of working with automation, one thing has become very clear to me. AI agents are exceptional at execution, and terrible at inferring intent. We speak to them like humans. We skip assumptions. We expect mind reading. Then we are surprised when the system delivers something technically correct and practically useless. This is why so many AI initiatives disappoint. Not because the models are weak, but because the context is. The real skill shift is not better prompts. It is learning how to design context. So here is the question I keep coming back to. When AI fails, is it really the technology, or the way we explain the problem to it? #AI #ArtificialIntelligence #AIAgents #Automation #FutureOfWork #ContextEngineering #TechLeadership

Software development is quietly undergoing its biggest shift in decades. Not because of new frameworks. Not because of faster cloud. But because agents are entering the SDLC. Traditional development follows a slow, sequential loop: requirements → design → coding → testing → reviews → deployment → monitoring → feedback. Each step depends on human handoffs, manual fixes, delayed feedback, and long iteration cycles—often stretching from weeks to months. Agentic coding changes this entirely. Instead of humans writing everything line-by-line, developers express intent. Agents understand requirements, implement features, generate tests and documentation, deploy changes, monitor production, and even propose fixes. The lifecycle compresses from weeks and months into hours or days. Here’s what actually changes: • Sequential handoffs become continuous agent-driven flows • Humans shift from coding to guiding and reviewing • Documentation is generated inline, not after delivery • Testing happens automatically alongside implementation • Incidents trigger agent-assisted remediation • Monitoring feeds directly back into learning loops • Iteration becomes constant, not episodic In the Agentic SDLC: You describe outcomes. Agents execute workflows. Humans validate critical decisions. Systems learn continuously. The result isn’t just faster delivery. It’s a fundamentally different operating model for engineering—where feedback is immediate, fixes are automated, and improvement never stops. This is how software teams move from manual development pipelines to self-improving delivery systems.

The development of multi-agentic workflows is not very different from microservices architecture, except for their non-deterministic behavior. Interestingly, as these systems expand, they start to feel increasingly similar. A significant portion of the effort goes into orchestrating, ensuring observability, logging, monitoring, availability, and routing. It’s far more than just working with large language models (LLMs). While some models have abstracted these layers through APIs, building interconnected systems still requires an additional abstraction layer to manage the complexity effectively. Although some platforms provide tools to connect agents, at an enterprise scale, it remains difficult to move away from the foundational principles of DevOps. Just as MLOps and AIOps evolved from DevOps when machine learning models started reaching production, "AgenticOps" will likely emerge as a new discipline. Managing agentic systems will require not only robust infrastructure but also a deeper focus on governance, reliability, and debugging in an environment where models interact dynamically. Unlike traditional software architectures, agentic workflows introduce an even higher level of non-determinism. The unpredictability of interactions, adaptive behaviors, and the real-time decision-making of agents will demand new operational frameworks. Agentic system evaluation is one way to compare it against ML systems. Here we talk more about right agent selection, routing, and request handling even before going to model-level metrics. This kind of non-determinism complexity goes exponentially higher along with depth of development. As enterprises scale these implementations, they will need strategies to ensure resilience, optimize costs, and balance control with flexibility. It’ll make AgenticOps a necessity rather than an option. #ExperienceFromTheField #WrittenByHuman

🚀 Google just dropped the blueprint for the future of agentic AI: Context Engineering, Sessions & Memory. If prompt engineering was about crafting good questions, context engineering is about building an AI’s entire mental workspace. Here’s why this paper matters 👇 What’s Context Engineering? LLMs are stateless, they forget everything between calls. 🔹Context engineering turns them into stateful systems by dynamically assembling: • System instructions (the “personality” of the agent) • External knowledge (RAG results, tools, and outputs) • Session history (ongoing dialogue) • Long-term memory (summaries and facts from past sessions) • It’s not prompt design anymore, it’s prompt orchestration. Think of sessions as your workbench, messy but active. Sessions manage short-term context and working memory. Think of memory as your filing cabinet, organized, persistent, and searchable. Memories persist facts, preferences, and strategies across time and agents. Together, they make AI personal, consistent, and self-improving. My Takeaways: Context is the new compute, your system’s intelligence depends on what it sees, not just the model you use. Memory isn’t a vector DB, it’s an LLM-driven ETL pipeline that extracts, consolidates, and prunes knowledge. Multi-agent systems need shared memory layers, not shared prompts. Procedural memory (the how) is the next frontier, agents learning strategies, not just storing facts. Building an “agent” today isn’t about chaining APIs together. It’s about context architecture to make models actually think across time. The future of AI won’t belong to those who fine-tune models, it’ll belong to those who engineer context. “Stateful AI begins with context engineering.” This might just be the new foundation of agentic systems.

Context engineering is increasingly the most critical component for building effective AI Agents in the enterprise right now. This will ultimately be the long pole in the tent for AI Agents adoption in most organizations. We need AI Agents that can deeply understand the context of the business process that they’re tied to. This means accessing the most important data for that workflow, using the appropriate tools at the right moment, having proper objectives and instructions, and understanding the domain that they’re in. Some of the big open items for anyone building enterprise agents are: * Narrow vs. General agents. The smaller the task, the easier it is to give the AI Agents the right context to be successful. But the smaller the task, the less value there will be. Finding the optimal task size for value generation will be an important factor for the next few years. * Getting data into an agent-ready system. Enterprise data is often fragmented between dozens or hundreds of systems, many of which are not prepared for a world of AI. Most companies will still need to modernize their data environments to get the full benefit of AI Agents. * Accessing the *right* data for the task is paramount. Even when you have data in a modern environment, getting access controls perfectly aligned to what the AI Agent is going to need access to is critical. Further, deciding what to do RAG on vs. just a general search vs. what to put fully into the context window will matter a ton per task. * Choosing what should be deterministic vs. non-deterministic. If you demand too much from the models, you’re likely to see some drop off in quality. Yet, if you have the model do too little, then you’re dramatically underutilizing what’s possible with AI. This of course is a moving target because the models themselves are improving at an accelerating rate. * The right user interface to get the AI Agents context deeply matters. Half of the problem for getting context to agents doesn’t look like an AI problem at all. It’s all about where the agents show up in the workflow and how the user interacts with them to provide them the context necessary to do the task. The race for the next few years in AI in the enterprise is to see who best to deliver the right context for any given workflow. This will determine the winners and losers in the AI race.

MCP is to AI what HTTP was to the internet — a simple standard with massive impact. It’s the bridge that connects AI with the systems we all use every day. 𝗧𝗵𝗲 𝗽𝗿𝗼𝗯𝗹𝗲𝗺 Today, AI is good at producing answers but remains cut off from the apps, data, and systems people rely on. Companies have to build custom connections one by one — a slow, costly process that adds complexity and risk. For example, if you ask AI to pull last quarter’s sales figures, it can’t simply reach into your company’s database or ERP system. 𝗪𝗵𝗮𝘁 𝗶𝘀 𝗠𝗖𝗣 This is the gap the Model Context Protocol (MCP) was designed to solve. Introduced by Anthropic in November 2024, MCP provides a shared set of rules for connecting AI with the tools and systems we use — from databases and files to business apps and APIs. A simple analogy we all understand: MCP is like USB for computers — one standard that lets us plug in many different devices. 𝗛𝗼𝘄 𝗠𝗖𝗣 𝘄𝗼𝗿𝗸𝘀 Instead of one-off, custom integrations, MCP creates a single, consistent bridge. This allows AI to pull information, trigger actions, and deliver results in a controlled, auditable way. To build on the earlier example: rather than building a special connector just to fetch last quarter’s sales figures, MCP gives AI a standard way to access that data — and the same approach works whether the source is a CRM, a file system, or a payments API. 𝗜𝗺𝗽𝗹𝗶𝗰𝗮𝘁𝗶𝗼𝗻𝘀 ·      AI becomes actionable — able to interact with real systems, data, and processes, making it useful in everyday life. ·      Multi-agent systems (MAS) become scalable, as agents can coordinate through a shared protocol across many tools. ·      Greater trust and accountability, with activity easier to monitor, audit, and control — essential for safety and regulation. ·      Ecosystem-wide acceleration, similar to the internet’s growth after HTTP, as one standard lowers barriers for developers, platforms, and institutions. 𝗔𝗱𝗼𝗽𝘁𝗶𝗼𝗻 ·      In just months, MCP has become the default way leading AI platforms connect to external systems. ·      OpenAI has integrated it into ChatGPT, the Agents SDK, and the Responses API. ·      Google DeepMind and Microsoft have announced support in Gemini and Copilot Studio. ·      Hundreds of open-source MCP servers now connect to services and platforms like GitHub, Slack, Postgres, and Stripe. ·      Real-world use cases are emerging: payments providers use it to let users generate PayByLinks through natural language, and Windows apps like Perplexity can now search files or perform system tasks through MCP. 𝗖𝗵𝗮𝗹𝗹𝗲𝗻𝗴𝗲𝘀 Security gaps, limited authentication and permissions, reliance on local servers, and immature tooling remain the biggest obstacles to large-scale deployment — hurdles that must be addressed before MCP can reach mainstream adoption. Opinions: my own, Graphic source: BCG Subscribe to my newsletter: https://lnkd.in/dkqhnxdg

I just published a deep dive on Mastercard Verifiable Intent vs Visa Trusted Agent Protocol. Agentic commerce breaks a core assumption in payments: a human is present at checkout. When AI agents browse and transact on behalf of users, merchants, issuers, and networks lose the simplest trust signal — the customer clicked buy. Two different approaches are emerging. -> Mastercard Verifiable Intent treats authorization as a cryptographic delegation chain. -> User identity, purchase intent, and agent execution are linked through layered credentials, creating machine-verifiable evidence of what the user actually authorized. Visa Trusted Agent Protocol focuses on the merchant interaction layer. It uses HTTP message signatures and identity tokens so merchants can verify that automated traffic comes from a legitimate shopping agent rather than a bot. My takeaway from the research: Verifiable Intent → authorization evidence Trusted Agent Protocol → interaction trust Both address different failure points in agentic commerce, which is why they will likely coexist. In the article, I break down the protocol architecture, trust models, and what this means for builders working on agent payments and autonomous commerce.