How to Understand Modern AI Agent Architecture

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?

If you’re getting started with AI agents, this is for you 👇 I’ve seen so many builders jump straight into wiring up LangChain or CrewAI without ever understanding what actually makes an LLM act like an agent, and not just a glorified autocomplete engine. I put together a 10-phase roadmap to help you go from foundational concepts → all the way to building, deploying, and scaling multi-agent systems in production. Phase 1: Understand what “agentic AI” actually means → What makes an agent different from a chatbot → Why long-context alone isn’t enough → How tools, memory, and environment drive reasoning Phase 2: Learn the core components → LLM = brain → Memory = context (short + long term) → Tools = actuators → Environment = where the agent runs Phase 3: Prompting for agents → System vs user prompts → Role-based task prompting → Prompt chaining with state tracking → Format constraints and expected outputs Phase 4: Build your first basic agent → Start with a single-task agent → Use UI (Claude or GPT) before code → Iterate prompt → observe behavior → refine Phase 5: Add memory → Use buffers for short-term recall → Integrate vector DBs for long-term → Enable retrieval via user queries → Keep session memory dynamically updated Phase 6: Add tools and external APIs → Function calling = where things get real → Connect search, calendar, custom APIs → Handle agent I/O with guardrails → Test tool behaviors in isolation Phase 7: Build full single-agent workflows → Prompt → Memory → Tool → Response → Add error handling + fallbacks → Use LangGraph or n8n for orchestration → Log actions for replay/debugging Phase 8: Multi-agent coordination → Assign roles (planner, executor, critic) → Share context and working memory → Use A2A/TAP for agent-to-agent messaging → Test decision workflows in teams Phase 9: Deploy and monitor → Host on Replit, Vercel, Render → Monitor tokens, latency, error rates → Add API rate limits + safety rules → Setup logging, alerts, dashboards Phase 10: Join the builder ecosystem → Use Model Context Protocol (MCP) → Contribute to LangChain, CrewAI, AutoGen → Test on open evals (EvalProtocol, SWE-bench, etc.) → Share workflows, follow updates, build in public This is the same path I recommend to anyone transitioning from prompting → to building production-grade agents. Save it. Share it. And let me know what phase you’re in, or where you’re stuck. 〰️〰️〰️ Follow me (Aishwarya Srinivasan) for more AI insight and subscribe to my Substack to find more in-depth blogs and weekly updates in AI: https://lnkd.in/dpBNr6Jg

I came across a new framework that brings clarity to the messy world of AI agents with a 6-level autonomy hierarchy. While most definitions of AI agents are binary (it either is or isn't), a new framework from Vellum introduces a spectrum of agency that makes far more sense for the current AI landscape. The six levels of agentic behavior provide a clear path from basic to advanced: 𝐋𝐞𝐯𝐞𝐥 0 - 𝐑𝐮𝐥𝐞-𝐁𝐚𝐬𝐞𝐝 𝐖𝐨𝐫𝐤𝐟𝐥𝐨𝐰 (𝐅𝐨𝐥𝐥𝐨𝐰𝐞𝐫) No intelligence—just if-this-then-that logic with no decision-making or adaptation. Examples include Zapier workflows, pipeline schedulers, and scripted bots—useful but rigid systems that break when conditions change. 𝐋𝐞𝐯𝐞𝐥 1 - 𝐁𝐚𝐬𝐢𝐜 𝐑𝐞𝐬𝐩𝐨𝐧𝐝𝐞𝐫 (𝐄𝐱𝐞𝐜𝐮𝐭𝐨𝐫) Shows minimal autonomy—processing inputs, retrieving data, and generating responses based on patterns. The key limitation: no control loop, memory, or iterative reasoning. It's purely reactive, like basic implementations of ChatGPT or Claude. 𝐋𝐞𝐯𝐞𝐥 2 - 𝐔𝐬𝐞 𝐨𝐟 𝐓𝐨𝐨𝐥𝐬 (𝐀𝐜𝐭𝐨𝐫) Not just responding but executing—capable of deciding to call external tools, fetch data, and incorporate results. This is where most current AI applications live, including ChatGPT with plugins or Claude with Function Calling. Still fundamentally reactive without self-correction. 𝐋𝐞𝐯𝐞𝐥 3 - 𝐎𝐛𝐬𝐞𝐫𝐯𝐞, 𝐏𝐥𝐚𝐧, 𝐀𝐜𝐭 (𝐎𝐩𝐞𝐫𝐚𝐭𝐨𝐫) Managing execution by mapping steps, evaluating outputs, and adjusting before moving forward. These systems detect state changes, plan multi-step workflows, and run internal evaluations. Examples like AutoGPT or LangChain agents attempt this, though they still shut down after task completion. 𝐋𝐞𝐯𝐞𝐥 4 - 𝐅𝐮𝐥𝐥𝐲 𝐀𝐮𝐭𝐨𝐧𝐨𝐦𝐨𝐮𝐬 (𝐄𝐱𝐩𝐥𝐨𝐫𝐞𝐫) Behaving like stateful systems that maintain state, trigger actions autonomously, and refine execution in real-time. These agents "watch" multiple streams and execute without constant human intervention. Cognition Labs' Devin and Anthropic's Claude Code aspire to this level, but we're still in the early days, with reliable persistence being the key challenge. 𝐋𝐞𝐯𝐞𝐥 5 - 𝐅𝐮𝐥𝐥𝐲 𝐂𝐫𝐞𝐚𝐭𝐢𝐯𝐞 (𝐈𝐧𝐯𝐞𝐧𝐭𝐨𝐫) Creating its own logic, building tools on the fly, and dynamically composing functions to solve novel problems. We're nowhere near this yet—even the most powerful models (o1, o3, Deepseek R1) still overfit and follow hardcoded heuristics rather than demonstrating true creativity. The framework shows where we are now: production-grade solutions up to Level 2, with most innovation happening at Levels 2-3. This taxonomy helps builders understand what kind of agent they're creating and what capabilities correspond to each level. Full report https://lnkd.in/gZrGb4h7

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

I've read 100+ pages on AI agents this week. Here's what most people get wrong: People think agents = chatbots. They're not. Agents are AI systems that independently (keyword) execute multi-step workflows with real autonomy. Here's what actually makes an agent: 1. Independent Decision Making - Must control its own workflow execution - Can recognize task completion and correct mistakes  - Knows when to hand control back to humans 2. Real-World Integration - Has access to external tools and systems - Can read data AND take concrete actions - Dynamically selects right tools for each phase 3. Built-in Safety Rails (optional, but recommended)  - Runs concurrent security checks - Filters sensitive data in real-time - Escalates high-risk actions to humans 4. Incremental Complexity - Start with single-agent architecture - Add capabilities through tools, not agents - Only split into multi-agent system when necessary 5. Clear Handoff Protocols - Defined triggers for human intervention - Graceful transitions between agents - Maintains context through transfers Building agents isn't about creating fancy chatbots. It's about automating complex workflows end-to-end with intelligence and adaptability. — Have you seen a “real” AI agent in the wild?  — Enjoyed this? 2 quick things: - Follow me for more AI automation insights - Share this a with teammate 

Now that you’ve selected your use case, designing AI agents is not about finding the perfect configuration but making deliberate trade-offs based on your product’s goals and constraints. You’ll be optimizing for control, latency, scalability, or safety, and each architectural choice will impact downstream behavior. This framework outlines 15 of the most critical trade-offs in Agentic AI to help you build successfully: 1.🔸Autonomy vs Control Giving agents more autonomy increases flexibility, but reduces human oversight and predictability. 2.🔸Speed vs Accuracy Faster responses often come at the cost of precision and deeper reasoning. 3.🔸Modularity vs Cohesion Modular agents are easier to scale. Cohesive ones reduce communication overhead. 4.🔸Reactivity vs Proactivity Reactive agents wait for input. Proactive ones take initiative, sometimes without clear triggers. 5.🔸Security vs Openness Opening up tool access increases capability, but also the risk of data leaks or misuse. 6.🔸Memory Depth vs Freshness Deep memory helps with long-term context. Fresh memory improves agility and faster decision-making. 7.🔸Multi-Agent vs Solo Agent Multi-agent systems bring specialization but add complexity. Solo agents are easier to manage. 8.🔸Cost vs Performance More capable agents require more tokens, tools, and compute, raising operational costs. 9.🔸Tool Access vs Safety Letting agents access APIs boosts functionality but can lead to unintended outcomes. 10.🔸Human-in-the-Loop vs Full Automation Humans add oversight but slow things down. Full automation scales well but may go off-track. 11.🔸Model-Centric vs Function-Centric Model-based reasoning is flexible but slower. Function calls are faster and more predictable. 12.🔸Evaluation Simplicity vs Real-World Alignment Testing in a sandbox is easier. Real-world tasks are messier, but more meaningful. 13.🔸Static Prompting vs Dynamic Planning Static prompts are stable. Dynamic planning adapts better, but adds complexity. 14.🔸Generality vs Specialization General agents handle a wide range of tasks. Specialized agents perform better at specific goals. 15.🔸Local vs Cloud Execution Cloud offers scalability. Local execution gives more privacy and lower latency. These kinds of decisions shape results of your AI system, for better… or worse. Save this for reference and share with others. #aiagents #artificialintelligence

𝐀𝐈 𝐚𝐠𝐞𝐧𝐭𝐬 𝐝𝐨𝐧’𝐭 𝐧𝐞𝐞𝐝 𝐦𝐨𝐫𝐞 𝐝𝐚𝐭𝐚 𝐭𝐡𝐞𝐲 𝐧𝐞𝐞𝐝 𝐛𝐞𝐭𝐭𝐞𝐫 𝐦𝐞𝐦𝐨𝐫𝐲 𝐚𝐫𝐜𝐡𝐢𝐭𝐞𝐜𝐭𝐮𝐫𝐞. Most agents fail not from ignorance, but from memory blindness. Design memory first, and agents become informed, consistent, and trustworthy. Five memories turn static models into adaptive, accountable digital coworkers. ↳ 𝐖𝐨𝐫𝐤𝐢𝐧𝐠 𝐦𝐞𝐦𝐨𝐫𝐲 holds current goals, constraints, and dialogue turns in play. ↳ 𝐒𝐞𝐦𝐚𝐧𝐭𝐢𝐜 𝐦𝐞𝐦𝐨𝐫𝐲 stores facts, schemas, and domain knowledge beyond single tasks. ↳ 𝐏𝐫𝐨𝐜𝐞𝐝𝐮𝐫𝐚𝐥 𝐦𝐞𝐦𝐨𝐫𝐲 captures tools, steps, and policies for repeatable execution. ↳ 𝐄𝐩𝐢𝐬𝐨𝐝𝐢𝐜 𝐦𝐞𝐦𝐨𝐫𝐲 logs situations, outcomes, and lessons from past work. ↳ 𝐏𝐫𝐞𝐟𝐞𝐫𝐞𝐧𝐜𝐞 𝐦𝐞𝐦𝐨𝐫𝐲 tracks users, roles, thresholds, and exceptions that personalize actions. Insight: Separation prevents overwrites and hallucinations when contexts suddenly shift. Insight: Retrieval gates control which memories are relevant, reducing noise. Insight: Freshness scores prioritize recent episodes without erasing durable knowledge. Insight: Audit trails from episodic memory create governance and regulatory defensibility. A Manufacturing support agent forgot entitlements and unnecessarily escalated routine tickets. Adding procedural, episodic, and preference memories with retrieval gates. Resolution accuracy rose, first contact resolutions jumped, and escalations dropped dramatically. Leaders finally trusted agents because decisions referenced verifiable, auditable memories. If you deploy agents, design memory before prompts, models, or dashboards. ♻️ Repost to your LinkedIn empower your network & follow Timothy Goebel for expert insights #AIAgents #Manufacturing #Construction #Healthcare #SmallBusiness

Agents will unlock the next wave of productivity gains for the enterprise...but they also have their own unique set of operational challenges Let's check the lifecycle for AI Agentic development 𝗗𝗲𝘀𝗶𝗴𝗻: 1. Define agent use case, detailed workflow and KPIs to align with business goal. 2. Identify data sources (tools) available to validate feasibility of project. 3. Select/fine-tune appropriate model to suit the agentic workflow. 4. Define appropriate architecture & patterns (framework & libraries) to enable reasoning, planning, self-improvement, tool usage. 5. Design underlying infrastructure to optimize cost-effectiveness. 𝗕𝘂𝗶𝗹𝗱 & 𝗗𝗲𝗽𝗹𝗼𝘆: 1. Integrate agentic workflow with LLM inference provider. 2. Integrate service with data sources (tools) across environments. 3. Simulate and debug service behavior. Guardrail actions and outputs. 𝗖𝗼𝗻𝘀𝘂𝗺𝗲 & 𝗠𝗼𝗻𝗶𝘁𝗼𝗿: 1. Deploy agentic workflow as API endpoint. Ensure access control and security. 2. Integrate agentic workflow with application services (UI, etc.). 3. Monitor agentic workflow KPIs & logs to ensure optimized results, provide transparency & explainability. AI agents need supporting enterprise capabilities to overcome adoption barriers and be deployed at scale.

The rise of agentic reasoning promises to transform how we work by creating AI systems that can autonomously pursue goals and complete complex tasks. Glean is at the forefront of this shift, pioneering a new agentic reasoning architecture that expands AI’s potential to get work done: resolve support tickets, help engineers debug code, and adapt tone of voice for corporate communications. With our new architecture, agents break down complex work into multi-step plans. Steps are executed by AI agents that are trained on their tasks and equipped with the tools to achieve their goals. Early research shows a 24% increase in relevance with our new agentic reasoning architecture. Here’s a preview of the new architecture: 🔷 Search: Evaluate the query and, using heuristics, determine whether it can be answered using search or agentic reasoning. 🔷 Reflect: Reflect on the initial search results, gauge confidence in the result, and decide whether to return a result or keep going down the agentic reasoning path. Search → fast and accurate answers Agentic reasoning → complex multi-step queries 🔷 Plan: Formulate the strategy, deeply understanding the goal and breaking down the steps to achieve it. Figure out the specialized sub-agents and tools to achieve each step of the work. 🔷 Execute: Sub-agents reason about the tools to use-search, data analysis, email, calendar, employee search, expert search, etc.- and how to stitch them together to achieve individual goals. 🔷 Respond: Respond in natural language via chat or by taking an action like creating a Jira ticket. Reimagining Work AI is an ongoing journey that builds on our foundational technologies. We began with search and advanced to RAG; now we’re progressing from RAG to agentic reasoning. We remain committed to pushing the boundaries of what AI can achieve in the workplace. This is the AI journey we envision for all our customers, where continuous innovation and practical application go hand in hand to transform the future of work. https://bit.ly/3ZdIWvg

"Are AI Agents Just Chatbots? Think Again." Most people think AI agents are just glorified chatbots. But what if I told you they’re redefining the future of digital workforces? 🚀 Let’s address the common misconception: AI agents are often described as simple chatbots with API-calling capabilities. While that might sound impressive, it’s far from what true AI-driven systems can do. Here’s the reality: There’s a fundamental difference between what most call an "AI agent" and what a true autonomous AI system is designed to achieve. Let me break it down for you: 📌 Chatbot Architecture: The Basics A traditional chatbot follows a linear process: 1️⃣ You input a request (e.g., “Find the nearest coffee shop”). 2️⃣ It maps the request to a single, pre-defined action like calling a map API. 3️⃣ The output? Something like, “The nearest coffee shop is 0.5 miles away.” While helpful, it’s limited to single-task responses without adaptability or learning. 📌 AI Agent Architecture: A Game-Changer A true AI agent doesn’t just respond—it thinks, plans, and adapts. For example, ask it to plan a 3-day Paris trip under $1000, and here’s what happens: 1️⃣ It breaks your request into actionable components (flights, hotels, activities, and budget). 2️⃣ It identifies the best tools and APIs, such as flight search engines and hotel booking platforms. 3️⃣ It leverages memory to align recommendations with your past preferences (like favorite cuisines or travel styles). 4️⃣ It iterates on the plan to ensure it fits within your budget and constraints. 5️⃣ The final result? A personalized, optimized travel plan ready for execution. 📌 What Makes the Difference? These AI agents perform contextual, multi-step tasks that go beyond simple Q&A. Take Microsoft’s Ignite demo, for instance. In it, an AI agent autonomously planned a trip, created a detailed document, and adjusted workflows dynamically. It’s not just about answering queries—it’s about delivering tailored, actionable solutions. As we move toward systems combining intelligence, adaptability, and decision-making, the potential of AI agents is becoming transformative across industries. 💡 What are the common misconceptions about AI agents you’ve come across? Let’s discuss in the comments below. ♻️ Share 👍 React 💭 Comment