How to Understand Agentic Systems

AI is rapidly moving from passive text generators to active decision-makers. To understand where things are headed, it’s important to trace the stages of this evolution. 1. 𝗟𝗟𝗠𝘀: 𝗧𝗵𝗲 𝗘𝗿𝗮 𝗼𝗳 𝗟𝗮𝗻𝗴𝘂𝗮𝗴𝗲 𝗙𝗹𝘂𝗲𝗻𝗰𝘆 Large Language Models (LLMs) like GPT-3 and GPT-4 excel at generating human-like text by predicting the next word in a sequence. They can produce coherent and contextually appropriate responses—but their capabilities end there. They don’t retain memory, they don’t take actions, and they don’t understand goals. They are reactive, not proactive. 2. 𝗥𝗔𝗚: 𝗧𝗵𝗲 𝗔𝗴𝗲 𝗼𝗳 𝗖𝗼𝗻𝘁𝗲𝘅𝘁-𝗔𝘄𝗮𝗿𝗲 𝗚𝗲𝗻𝗲𝗿𝗮𝘁𝗶𝗼𝗻 Retrieval-Augmented Generation (RAG) brought a major upgrade by integrating LLMs with external knowledge sources like vector databases or document stores. Now the model could retrieve relevant context and generate more accurate and personalized responses based on that information. This stage introduced the idea of 𝗱𝘆𝗻𝗮𝗺𝗶𝗰 𝗸𝗻𝗼𝘄𝗹𝗲𝗱𝗴𝗲 𝗮𝗰𝗰𝗲𝘀𝘀, but still required orchestration. The system didn’t plan or act—it responded with more relevance. 3. 𝗔𝗴𝗲𝗻𝘁𝗶𝗰 𝗔𝗜: 𝗧𝗼𝘄𝗮𝗿𝗱 𝗔𝘂𝘁𝗼𝗻𝗼𝗺𝗼𝘂𝘀 𝗜𝗻𝘁𝗲𝗹𝗹𝗶𝗴𝗲𝗻𝗰𝗲 Agentic AI is a fundamentally different paradigm. Here, systems are built to perceive, reason, and act toward goals—often without constant human prompting. An Agentic system includes: • 𝗠𝗲𝗺𝗼𝗿𝘆: to retain and recall information over time. • 𝗣𝗹𝗮𝗻𝗻𝗶𝗻𝗴: to decide what actions to take and in what order. • 𝗧𝗼𝗼𝗹 𝗨𝘀𝗲: to interact with APIs, databases, code, or software systems. • 𝗔𝘂𝘁𝗼𝗻𝗼𝗺𝘆: to loop through perception, decision, and action—iteratively improving performance.    Instead of a single model generating content, we now orchestrate 𝗺𝘂𝗹𝘁𝗶𝗽𝗹𝗲 𝗮𝗴𝗲𝗻𝘁𝘀, each responsible for specific tasks, coordinated by a central controller or planner. This is the architecture behind emerging use cases like autonomous coding assistants, intelligent workflow bots, and AI co-pilots that can operate entire systems. 𝗧𝗵𝗲 𝗦𝗵𝗶𝗳𝘁 𝗶𝗻 𝗧𝗵𝗶𝗻𝗸𝗶𝗻𝗴 We’re no longer designing prompts. We’re designing 𝗺𝗼𝗱𝘂𝗹𝗮𝗿, 𝗴𝗼𝗮𝗹-𝗱𝗿𝗶𝘃𝗲𝗻 𝘀𝘆𝘀𝘁𝗲𝗺𝘀 capable of interacting with the real world. This evolution—LLM → RAG → Agentic AI—marks the transition from 𝗹𝗮𝗻𝗴𝘂𝗮𝗴𝗲 𝘂𝗻𝗱𝗲𝗿𝘀𝘁𝗮𝗻𝗱𝗶𝗻𝗴 to 𝗴𝗼𝗮𝗹-𝗱𝗿𝗶𝘃𝗲𝗻 𝗶𝗻𝘁𝗲𝗹𝗹𝗶𝗴𝗲𝗻𝗰𝗲.

If you want to understand how AI Agents actually work together… start by understanding their protocols. AI agents don’t collaborate magically. They communicate, share memory, negotiate tasks, and stay safe because a whole ecosystem of protocols makes it possible. Teams focus on models and tools. But it’s the protocol layer that decides whether your agents scale, or fail. This map breaks down the core building blocks every agentic system relies on: 1. Core & Widely Used Protocols These are the fundamental standards that let agents talk to each other, execute tasks, and interact with tools in a structured, predictable way. They form the backbone of any agent-based architecture. 2. Transport & Messaging This layer keeps agents connected. It handles event streams, async messaging, real-time communication, and reliable delivery - everything needed for fast, fault-tolerant workflows. 3. Memory & Context Exchange Agents can’t reason or collaborate without shared context. These protocols help them store state, exchange histories, and retrieve past knowledge so the system behaves consistently over time. 4. Security & Governance Every agent interaction must be audited, authorized, and safe. These standards ensure identity, access control, compliance, and safe execution, especially when agents touch production systems. 5. Coordination & Control This is the orchestration layer. It handles oversight, delegation, decision-making, and task handoffs - enabling multi-agent pipelines to work as one coherent system. - Why this matters As AI agents move from prototypes to production, understanding these protocol layers becomes essential. Models generate intelligence - but protocols create order, safety, and scale. If you want agents that can collaborate, negotiate, and execute reliably, this is the foundation to build on.

Everyone is talking about AI agents, but very few people actually break down the technical architecture that makes them work. To make sense of it, I put together the 7-layer technical architecture of agentic AI systems. Think of it as a stack where each layer builds on top of the other, from the raw infrastructure all the way to the applications we interact with. 1. Infrastructure and Execution Environment This is the foundation. It includes APIs, GPUs, TPUs, orchestration engines like Airflow or Prefect, monitoring tools like Prometheus, and cloud storage systems such as S3 or GCS. Without this base, nothing else runs. 2. Agent Communication and Networking Once you have infrastructure, agents need to talk to each other and to the environment. This layer covers frameworks for multi-agent systems, memory management (short-term and long-term), communication protocols, embedding stores like Pinecone, and action APIs. 3. Protocol and Interoperability This is where standardization comes in. Protocols like Agent-to-Agent (A2A), Model Context Protocol (MCP), Agent Negotiation Protocol (ANP), and open gateways allow different agents and tools to interact in a consistent way. Without this layer, you end up with isolated systems that cannot coordinate. 4. Tool Orchestration and Enrichment Agents are powerful because they can use tools. This layer enables retrieval-augmented generation, vector databases such as Chroma or FAISS, function calling through LangChain or OpenAI tools, web browsing modules, and plugin frameworks. It is what allows agents to enrich their reasoning with external knowledge and execution capabilities. 5. Cognitive Processing and Reasoning This is the brain of the system. Agents need planning engines, decision-making modules, error handling, self-improvement loops, guardrails, and ethical AI mechanisms. Without reasoning, an agent is just a connector of inputs and outputs. 6. Memory Architecture and Context Modeling Intelligent behavior requires memory. This layer includes short-term and long-term memory, identity and preference modules, emotional context, behavioral modeling, and goal trackers. Memory is what allows agents to adapt and become more effective over time. 7. Intelligent Agent Application Finally, this is where it all comes together. Applications include personal assistants, content creation tools, e-commerce agents, workflow automation, research assistants, and compliance agents. These are the systems that people and businesses actually interact with, built on top of the layers below. When you put these seven layers together, you can see agentic AI not as a single tool but as an entire ecosystem. Each layer is necessary, and skipping one often leads to fragile or incomplete solutions. ---- ✅ I post real stories and lessons from data and AI. Follow me and join the newsletter at www.theravitshow.com

Why everyone’s chasing smarter #AIagents But why do most fail at scale? If you want agents that: • Make decisions • Coordinate across systems • Work in real-time environments • Respect rules, context, and security Start by understanding this 4-layer architecture. It’s not just technical plumbing, it’s what makes AI agentic. The 4-layer architecture that makes agents truly autonomous. Most AI efforts stop at the model or interface. But real autonomy doesn’t happen at the surface. It happens underneath across four deeply integrated layers. Let’s break down the full stack that powers #AgenticAI: 𝟭. 𝗜𝗻𝗳𝗿𝗮𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲 𝗟𝗮𝘆𝗲𝗿: 𝗕𝗿𝗮𝗶𝗻𝘀 & 𝗠𝘂𝘀𝗰𝗹𝗲𝘀 → Foundation Models provide reasoning (OpenAI, Claude, Gemini, etc.) → Compute gives real-time performance (Cloud, Edge, AI chips) → Communication Infra ensures connectivity (wireless + wired) → Data & Knowledge: Business data, public data, prompts, knowledge graphs, this is the fuel that feeds agents Without this layer, agents can’t think, act, or even exist. 𝟮. 𝗔𝗴𝗲𝗻𝘁 𝗠𝗮𝗻𝗮𝗴𝗲𝗺𝗲𝗻𝘁 𝗟𝗮𝘆𝗲𝗿: 𝗖𝗼𝗿𝗲 𝗼𝗳 𝘁𝗵𝗲 𝗔𝗴𝗲𝗻𝘁 → Each agent is a loop of Perception → Planning → Action → Memory → Supports both Virtual and Embodied Agents (think robots, drones, cars) → Manages identity, registration, capabilities, and access control This is where agents are “born” and with autonomy, context, and purpose. 𝟯. 𝗔𝗴𝗲𝗻𝘁 𝗖𝗼𝗼𝗿𝗱𝗶𝗻𝗮𝘁𝗶𝗼𝗻 𝗟𝗮𝘆𝗲𝗿: 𝗧𝗲𝗮𝗺𝘄𝗼𝗿𝗸 𝗘𝗻𝗴𝗶𝗻𝗲 → Enables multi-agent orchestration, task matching, and collaboration → Implements protocols for trust, security, privacy, and incentives → Handles conflicts, negotiations, and delegation between agents Think of this layer as the social operating system for AI. 𝟰. 𝗔𝗽𝗽𝗹𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗟𝗮𝘆𝗲𝗿: 𝗥𝗲𝗮𝗹-𝗪𝗼𝗿𝗹𝗱 𝗜𝗺𝗽𝗮𝗰𝘁 → Powers real-world use cases: smart homes, autonomous driving, healthcare, cities, factories → Connects with real-world systems via modality, semantics, and interface alignment This is where users experience the magic, but it only works if the 3 layers beneath are sound. 𝗪𝗵𝘆 Does 𝗶𝘁 𝗺𝗮𝘁𝘁𝗲𝗿: • You can’t duct-tape a model into an #autonomousAgent. • You need a full-stack architecture with governance, cognition, collaboration, and infrastructure. Are you designing for autonomy or still building traditional automation?

Agentic AI in 2026 = The biggest upgrade to how software is built, deployed, and operated. And the people who understand how agents actually work will lead the next wave of tech innovation. Most professionals still see Agentic AI as “better prompting.” In reality, it’s a full ecosystem - reasoning engines, memory systems, tool execution, multi-agent workflows, safety layers, and operational tooling. Here’s a simple breakdown of what you need to learn to stay ahead: 🔹 Agentic AI Basics Understand what agents are, how they differ from standard LLMs, and why autonomy, reasoning, and tool-use separate them from traditional automation. 🔹 Core Agent Components Agents rely on four pillars: • Intent understanding • Reasoning & planning • Memory systems • Tool use & API execution These functions decide how an agent interprets tasks and takes action. 🔹 Agent Frameworks & Tools Platforms like OpenAI Agents, LangGraph, CrewAI, AutoGen, LlamaIndex, and HuggingFace Agents help you build real production-ready agents. 🔹 Key Agentic Capabilities Planning, multi-step reasoning, scheduling, RAG, and multi-modal retrieval - the abilities that turn agents into problem-solvers instead of text generators. 🔹 Execution & Multi-Agent Collaboration How agents delegate tasks, communicate, call APIs, run workflows, and coordinate with other agents to complete complex goals. 🔹 Safety & Governance Guardrails, output validation, ethical constraints, security layers, and data-privacy systems - essential for trustworthy AI. 🔹 AgentOps (Agentic DevOps) Versioning, CI/CD for AI pipelines, monitoring, observability, model registries, dataset tracking, infra-as-code - everything needed to operate agents reliably in production. Agentic AI isn’t optional anymore. If you want to stay relevant, you need to understand how agents think, act, plan, and collaborate. Which part are you planning to learn first - reasoning, memory, or tool execution?

How Agentic AI Actually Works Everyone is talking about AI Agents — but very few explain what’s really happening under the hood. Agentic AI is not just a smarter chatbot. It’s a decision-making system that can reason, remember, and act across tools and environments. Here’s the simplified architecture behind modern Agentic AI systems 🔹 1. User & Frontend Layer ▪️Users interact through applications — copilots, enterprise dashboards, or conversational interfaces. ▪️This layer translates human intent into structured tasks for the agent. 🔹 2. Agent Runtime (The Brain) The agent orchestrates everything: ▪️Plans tasks ▪️Breaks goals into steps ▪️Chooses tools ▪️Calls AI models for reasoning ▪️Executes workflows This is where frameworks like LangGraph, AutoGen, CrewAI, or custom orchestration engines operate. 🔹 3. AI Model (Reasoning Engine) LLMs provide: ▪️ reasoning ▪️language understanding ▪️decision support ▪️But importantly — the model alone is NOT the agent. The agent is the system coordinating intelligence. 🔹 4. Memory System Agents become powerful when they remember: ▪️Short-term memory → current conversation context ▪️Long-term memory → user preferences, past outcomes, organizational knowledge ▪️Memory transforms AI from reactive → adaptive. 🔹 5. Tools & Execution Layer Agents create real business value by taking action through: ▪️Databases ▪️APIs ▪️Enterprise services ▪️Files & workflows This is where AI moves from answers to outcomes. 🔹 6. Communication Protocols Modern agents rely on structured protocols (MCP, tool calling, function interfaces) to safely interact with systems. The Big Shift Traditional AI: Generate responses Agentic AI: Achieve objectives We are moving from: 👉 Prompt → Response to 👉 Goal → Plan → Action → Learning This architectural shift is why AI agents are becoming the foundation of next-generation enterprise platforms. The future isn’t just smarter models — it’s autonomous systems built around them. #AgenticAI #AIAgents #GenerativeAI #AIArchitecture #EnterpriseAI #RAG #AITransformation #ProductManagement #ArtificialIntelligence Image Credit : Rahul Agarwal

Ever wondered what actually happens inside an AI agent before it gives you an answer? 🤔 Agentic AI isn’t magic. It’s a system — one that perceives, reasons, plans, and acts. Here’s a clear mental model to understand how it really works ⤵️ 🔹 1. Input Layer: Where intelligence begins An AI agent doesn’t rely on a single prompt. It pulls signals from: User queries Knowledge bases APIs & tools Logs, memory, and web data 👉 Think of this as the agent’s sensory system. 🔹 2. Reasoning & Planning Layer: The “brain” This is where Agentic AI separates itself from chatbots. The agent: Understands intent & context Retrieves long-term / short-term memory Breaks tasks into steps Chooses the right tools Adapts when things go wrong 👉 This is decision-making, not just text generation. 🔹 3. Action Layer: Doing real work Based on its plan, the agent can: Execute tasks Call APIs Collaborate with other agents Handle failures Schedule future actions 👉 The AI doesn’t just answer — it acts. 🔹 4. Output Layer: The final result All that orchestration leads to: Context-aware responses Accurate decisions Autonomous behavior that feels “intelligent” This is why Agentic AI ≠ traditional rule-based systems or chatbots. 📚 Want to learn this deeper? Start here: ⏺️ LangGraph (by LangChain) – agent workflows & state machines ⏺️ AutoGen (Microsoft) – multi-agent collaboration ⏺️ CrewAI – role-based agent systems ⏺️ OpenAI Function Calling & Assistants API ⏺️ Anthropic’s Agent Design Patterns ⏺️ Papers on ReAct, Toolformer & Reflexion Agentic AI is not the future. It’s already in production — quietly running systems. 📌 Save this if you’re building or debugging AI agents CC:Prem Natrajan

Most conversations around Agentic AI focus on what agents can do — plan, reason, call tools, and act autonomously. That framing, while exciting, misses the harder question enterprises must answer before any real deployment: what should an agent truly understand about the organization it operates in? As AI systems move from generating outputs to taking actions, the center of gravity shifts from model capability to grounding. Autonomy without grounding creates volatility, not intelligence. In enterprises, where decisions are shaped by history, constraints, regulation, and domain nuance, agents cannot rely on logic alone. They need context, memory, and judgment formed over time. Two distinct implementation paths are emerging. One treats Agentic AI as an extension of software engineering, built through predefined workflows and explicit decision logic. The other treats Agentic AI as a data-driven system, grounded in enterprise knowledge and industry context, where behavior emerges from evidence rather than instruction. This article examines these two approaches — Forward Engineering and the Data Foundry model — and explains why long-term, enterprise-scale Agentic AI depends less on how agents are designed, and more on how intelligence is grounded, governed, and allowed to evolve inside the organization.

Agentic AI Will Evolve the Way Humans Work, Not Just How Machines Think! Human intelligence is not powerful because of individual reasoning alone. It scales because humans coordinate, negotiate tasks, divide responsibility, resolve conflict, and make decisions collectively under constraints. Agentic AI follows the same trajectory. Early systems tried to mimic human reasoning inside a single model. That approach plateaus quickly. The real leap comes when agents are designed to work together the way humans do. Each with autonomy, context, and accountability, yet coordinated toward shared outcomes. Human collaboration is decentralized. Decisions emerge from partial information, role specialization, trust boundaries, and continuous feedback. The next generation of agentic systems mirrors this structure: multiple autonomous agents, each with bounded authority, communicating through agent-to-agent protocols, negotiating tasks, sharing state, and adapting based on outcomes. This is not imitation. It is architectural alignment. To behave like high-performing human teams, agentic systems require capabilities that already exist in early form: • Persistent shared memory that functions like institutional knowledge • Intent negotiation and task delegation rather than command execution • Distributed planning and execution loops with local decision rights • Reputation, identity, and policy-bound autonomy to establish trust • Guardrails embedded at interaction and execution time, not post hoc review When these elements come together, agents stop behaving like tools and start behaving like teams. Work is no longer scripted; it is emergent. Automation is no longer brittle; it is adaptive. The outcome is not AI replacing humans, but systems that scale human patterns of collaboration, without fatigue, handoffs, or latency. Humans set direction and constraints; agent collectives execute, learn, and coordinate continuously. This is how software stops mimicking human thought, and starts operating like human organizations, at machine speed.

Agentic systems don’t just learn user preferences — they give you a flexible way to blend those preferences with business needs. Thompson Sampling makes this easy. Each user-action pair is represented by a distribution (usually beta), and the system samples from those to decide what to show. If there's an option you want to prioritize — a product line that needs visibility, a message tied to a quota — you just nudge the distribution. You don’t have to override the system or hard-code a rule. Just tilt the playing field. Want more volume? Inflate the alpha. Need to ease off? Let the distribution settle back to baseline. This can be manual — a short-term push for a campaign. Or dynamic — tie it to a running shortfall and let the system auto-adjust. Like a good sales manager, you can put your thumb on the scale when needed, without breaking the underlying judgment. That’s the advantage of agentic learners: they’re not locked into a fixed notion of optimality. They can balance long-term personalization with short-term goals — without having to pause learning, flip switches, or burn everything down for a quarterly number. You don't have to choose between respecting the user and running the business. You get both.