How Agents Acquire Knowledge in AI

Treating AI like a chatbot, AKA you ask a question → it gives an answer is only scraching the surface. Underneath, modern AI agents are running continuous feedback loops - constantly perceiving, reasoning, acting, and learning to get smarter with every cycle. Here’s a simple way to visualize what’s really happening 👇 1. Perception Loop – The agent collects data from its environment, filters noise, and builds real-time situational awareness. 2. Reasoning Loop – It processes context, forms logical hypotheses, and decides what needs to be done. 3. Action Loop – It executes those plans using tools, APIs, or other agents, then validates outcomes. 4. Reflection Loop – After every action, it reviews what worked (and what didn’t) to improve future reasoning. 5. Learning Loop – This is where it gets powerful, the model retrains itself based on new knowledge, feedback, and data patterns. 6. Feedback Loop – It uses human and system feedback to refine outputs and improve alignment with goals. 7. Memory Loop – Stores and retrieves both short-term and long-term context to maintain continuity. 8. Collaboration Loop – Multiple agents coordinate, negotiate, and execute tasks together, almost like a digital team. These loops are what make AI agents more human-like while reasoning and self-improveming. Leveraging these loops moves AI systems from “prompt and reply” to “observe, reason, act, reflect, and learn.” #AIAgents

Agentic AI marks a new era where machines do not just respond, they reason, act, and evolve like autonomous problem-solvers. These systems go beyond static prompts and outputs, continuously learning from context, feedback, and their own decisions. Here is a clear breakdown of how Agentic AI actually works - step by step 👇 1. Goal Definition Every AI agent starts with a clear objective, whether it is summarizing data, automating a workflow, or generating insights. This goal defines the scope, constraints, and direction for all subsequent actions. 2. Context Gathering The agent collects relevant data or context from APIs, databases, or user input to understand the environment. This ensures decisions are grounded in real-world context rather than static information. 3. Perception & Understanding Through natural language processing, vision models, and structured data comprehension, the agent interprets its surroundings and builds a situational understanding before acting. 4. Memory Management The agent maintains both short-term (context window) and long-term (vector database) memory to ensure continuity and recall. This allows it to connect past insights with current actions effectively. 5. Reasoning & Planning Once the goal and data are clear, the agent breaks the task into smaller subtasks. It uses reasoning frameworks like chain-of-thought or planners to organize steps and make logical progress. 6. Decision Making & Adaptation At each step, the agent evaluates outcomes, adjusts strategies dynamically, and selects the next best action based on feedback, just like an intelligent human operator would. 7. Tool Selection & Execution The agent executes its plan by interacting with tools such as APIs, browsers, or software apps to perform real-world tasks. This bridges reasoning with tangible action. 8. Collaboration Between Agents In complex environments, multiple agents collaborate - sharing data, delegating subtasks, and working in parallel to solve multi-domain challenges efficiently. 9. Self-Evaluation & Reflection After execution, the agent reviews its performance, identifies errors or inefficiencies, and refines its reasoning pipeline - a key step toward becoming self-correcting. 10. Continuous Learning & Optimization Over time, the agent updates its models, memory, and strategies using new data and feedback, becoming smarter, faster, and more autonomous with each cycle. Agentic AI is the future of automation, where systems do not just follow instructions, they learn, plan, and adapt. Master this workflow, and you’ll understand how true AI autonomy is built.

Many people often ask me how to learn Agentic AI and where to start. My answer keeps evolving — because the field itself is changing every few months. What I shared six months ago helped many people get started. But today, with newer frameworks, deeper integrations, and more real-world use cases, that learning path looks different. So I’ve put together this updated AI Agents Learning Map — a structured view of how I now see this space progressing. Level 1 – Foundations This is where every learner should begin. The goal is to understand how intelligent systems are built and connected. • Large Language Models – Core models that generate and understand natural language. • Embeddings and Vector Databases – Represent meaning and context for better search and reasoning. • Prompt Engineering – Techniques to guide model responses effectively. • APIs and External Data Access – Allow models to connect to external systems and data sources. At this level, focus on understanding how LLMs interact with structured and unstructured data. Level 2 – System Capabilities At this stage, models evolve into systems. You begin combining memory, context, and reasoning to build early agent behaviors. • Context Management – Managing dialogue and maintaining state across interactions. • Memory and Retrieval – Implementing persistent storage for short- and long-term information. • Function Calling and Tool Use – Letting AI take real actions beyond text generation. • Multi-step Reasoning – Enabling sequential decision-making and logical flow. • Agent Frameworks – Using orchestration tools like LangGraph, CrewAI, and Microsoft AutoGen. This level is where isolated models start becoming intelligent systems. Level 3 – Advanced Autonomy Here, agents collaborate, plan, and execute tasks independently. This is where agentic AI truly begins. • Multi-Agent Collaboration – Building systems where agents work together with defined roles. • Agentic Workflows – Structuring processes that allow autonomous execution. • Planning and Decision-Making – Defining goals, evaluating options, and acting without human prompts. • Reinforcement Learning and Fine-tuning – Improving outcomes based on feedback and experience. • Self-Learning AI – Systems that evolve continuously as they operate. At this level, AI transitions from reactive systems to proactive problem-solvers. Why this learning map matters This map is not about tools or frameworks. It’s about progression — how engineers and organizations move from using AI to building intelligence. Mastering each level leads to better design decisions, deeper understanding, and ultimately, the ability to create autonomous, adaptive systems. Where would you place your current AI understanding on this map?

Demystifying AI Agents Memory: The Hidden Architecture Behind Intelligent Systems Just came across this fascinating diagram that perfectly illustrates how memory works in modern AI agents! This visualization breaks down the complex memory architecture that enables AI systems to maintain context and provide coherent responses: Episodic Memory: Stores previous human-assistant interactions, creating continuity in conversations Private Knowledge Base: Contains the foundational information, documentation, and grounding context Short-term (Working) Memory: Manages prompt structure, available tools, additional context, and reasoning history Procedural Memory: Maintains prompt and tool registries for executing specific functions Core: Houses the LLM and orchestrator that coordinate all memory components What's particularly interesting is how the embedding model transforms information into vector representations [0.01, ..., 0.43] that can be indexed and searched using Approximate Nearest Neighbor (ANN) techniques in latent space. This architecture explains why today's AI assistants can maintain context across conversations, recall previous interactions, and integrate new information with existing knowledge - mimicking aspects of human memory systems. As someone working in AI, I find these architectural insights invaluable for understanding both the capabilities and limitations of current systems. The parallels to human cognitive architecture are striking!

For those of you who want to know how AI agents actually take actions, here’s the simplest way to think about it Inputs : The agent starts by pulling information from different places: UI you interact with, your documents, a quick web search, a vector database for memory, or a knowledge graph for structured facts. Reasoning – This is where the magic happens. Instead of guessing, the agent uses different ways of thinking: CoT (Chain of Thought) → step-by-step logical reasoning. ToT (Tree of Thought) → explores multiple reasoning paths in parallel, like testing different scenarios before choosing. GoT (Graph of Thought) → connects ideas in a web, powerful when relationships are complex. ReAct, Reflexion, Plan & Execute → strategies that balance acting, self-correcting, and structured planning. Actions – Once it has a plan, the agent can do things: generate documents, call APIs, update databases, create visuals, or schedule tasks. Feedback Loop – Finally, it learns from your feedback, its own logs, and even LLM self-checks, so next time, it does better. Example many can relate to: Imagine you’re planning a business trip. The agent checks your calendar (UI), your company’s travel policy docs, runs a web search for flights, looks up your preferences from a vector DB, and pulls office locations from a knowledge graph. It reasons: “Cheapest flight lands too late, but Tree of Thought shows another option; Plan & Execute says early morning works best.” It acts: books the ticket, reserves a hotel, updates your team’s calendar. You give feedback: “I prefer aisle seats.” Next time, it remembers. AI agents don’t stop at answers. They pull context, plan actions, execute tasks, and refine themselves — every single time. #AI #AIagents #AgenticAI #FutureOfWork #LLM's #artificialintelligence

Not all AI agents are the same. Depending on how they’re built and what they’re designed to do, they can behave in very different ways. 𝗧𝗵𝗲 𝗯𝗮𝘀𝗶𝗰𝘀 AI agents are autonomous systems that perceive their environment, make decisions, and act toward specific goals — often without direct human input. At their core, they follow a simple loop: perceive → reason → act → learn (optional). The sophistication of that loop varies greatly. Some agents follow fixed rules — reacting to inputs with predictable, hard-coded responses. Others form a dynamic understanding of their environment, evaluate possible outcomes, and learn from experience. What separates one AI agent from another isn’t just intelligence — it’s the degree of autonomy, adaptability, and context awareness built into their design. 𝗧𝗵𝗲 𝗰𝗿𝗶𝘁𝗲𝗿𝗶𝗮 AI agents differ in how they perceive, decide, and adapt. Key criteria include: 𝟭. Perception: how they sense and interpret their environment. 𝟮. Reasoning: how they process information to make decisions. 𝟯. Learning: whether they improve performance over time. 𝟰. Goal orientation: whether they act reactively or plan ahead. 𝟱. Autonomy: how independently they operate from human control. 𝗧𝗵𝗲 𝘁𝘆𝗽𝗲𝘀 These criteria define five broad categories: 𝟭. Simple Reflex Agents: React instantly to inputs using predefined rules. They have no memory or context. Example: chatbots that reply with preset answers to specific keywords. 𝟮. Model-Based Agents: Track how the world changes, making more informed, context-aware decisions using an internal model. Example: navigation apps that adjust routes based on live traffic. 𝟯. Goal-Based Agents: Act with objectives in mind, evaluating which actions bring them closer to a desired outcome. Example: a delivery drone that plans its route to reach a destination while avoiding obstacles. 𝟰. Utility-Based Agents: Measure trade-offs to optimize for the best possible result. Example: recommendation engines that weigh multiple factors to suggest the most relevant content. 𝟱. Learning Agents: Continuously adapt and improve through feedback, experience, and data. Example: virtual assistants like Siri or Alexa that better understand user preferences over time. It’s like a ladder — each step upward adds more intelligence, independence, and sophistication, turning simple automation into real capability. As AI agents become more widespread, choosing the right kind to deploy will make all the difference. Opinions: my own, Graphic source: ByteByteGo   𝐒𝐮𝐛𝐬𝐜𝐫𝐢𝐛𝐞 𝐭𝐨 𝐦𝐲 𝐧𝐞𝐰𝐬𝐥𝐞𝐭𝐭𝐞𝐫: https://lnkd.in/dkqhnxdg

What if your AI agent could learn from its mistakes — without being retrained? New research reveals self-improving AI is real 👇 Right now, most models are static - if they fail once, they fail the same way again. That’s a huge bottleneck for real-world autonomy — especially as agents face tasks that require reasoning over long contexts. A new approach called Agentic Context Engineering (ACE) changes that. Instead of retraining weights, ACE builds a structured playbook of knowledge through a generator–reflector–curator loop: 🧠 Generator → tries the task (say, navigating an app) 🔍 Reflector → analyzes the failure 📘 Curator → writes a new rule into the AI’s context playbook The model’s “brain” doesn’t change, but its wisdom evolves. That’s self-improvement without gradient updates. It’s not a silver bullet, though. Poorly designed reflector agents can cause error accumulation or overfitting to bad habits. Still, the implication is massive: You don’t have to wait for next-gen models. You can build systems that learn in production — in real time. So… are you experimenting with self-improving agent loops yet? #StephYouShouldKnow #AI #AIagents #LLMs

Continual learning is one of the hardest features to build for agents. It’s also one that’s critical to get right. Nowhere is this more true than in production. Every company has deep institutional knowledge about how their production environment functions. This knowledge is fragmented across organization and team-specific documentation, as well as within the minds of tenured engineers. Much of it has never been written down. This is the single biggest barrier to AI agents ramping up and becoming useful in production domains, and it's a largely unrealized opportunity. Here's the deeper framework behind how we think about it. There are three distinct types of knowledge an AI agent needs to build: - Organizational Vocabulary: Every company has its own naming conventions, internal shorthand style, and acronyms. This type of knowledge is the most-straightforward to teach AI agents. - Tool-Specific Knowledge: Information on how to navigate specific systems, what data lives where, and how this data connects across different services. This type of knowledge is time-consuming to map out manually and it varies across companies. - Decision-Trace Knowledge: The most difficult type of knowledge to capture. This is the intuition that separates system experts from others. Given a particular situation, knowing which thread to pull next and why, based on pattern recognition abilities built up over years of progressive understanding. This knowledge generally lives in people's heads, is very rarely documented, and it is almost always context-dependent. Across all three, one principle holds: Resolve learns on its own, both explicitly and implicitly. However, Resolve always verifies these learnings with the humans who know the system best. Everything it learns is viewable, editable, and version-controlled, organized as a hierarchy that models the teams, resources, humans, etc. Resolve AI is already becoming the single and up-to-date source of truth for all organizational contexts for many production environments. Read more in my Introducing AI for Prod blog - link in comments.

Ever wondered about the inner workings of Agentic AI? Here's a simple breakdown of how it operates behind the scenes: 1. Input Sources: Agentic AI starts by gathering data from various sources, including knowledge bases, user queries, APIs, logs, and web data. 2. AI Processing: Once the data is collected, the AI processes it through multiple layers. It analyzes the query, reasons through context, retrieves memory, plans the task, selects tools, and manages the situation. 3. Action Layer: Utilizing its contextual understanding, the agent takes real actions, such as making decisions, executing tasks, handling errors, collaborating with other agents, and scheduling future actions. 4. Output: The culmination of these processes results in an accurate, relevant, and context-aware response that exudes intelligence and autonomy, distinguishing Agentic AI from traditional AI chatbots or rule-based systems. Understanding these mechanisms sheds light on the unique capabilities of Agentic AI. Save this post to delve deeper into the world of AI agents.

Most AI agents today have the same frustrating flaw: They don’t learn. You correct them… and they repeat the exact same mistake at the next task. You show them the right workflow… and it disappears the moment the session ends. Last week, I came across an open-source project that actually fixes this. And honestly, it changes how we think about agent architecture. It’s called Acontext and it gives AI agents the one thing they’ve always lacked: 👉 The ability to learn from real tasks and turn them into reusable skills. What impressed me the most Acontext doesn’t just store messages. It builds a full learning loop around every single task your agent performs. In plain English, here’s what it does: 1️⃣ Store Captures persistent context, session history, and artifacts like a memory layer that never resets. 2️⃣ Observe Watches how the agent solved a task, including tool calls, user feedback, and intermediate steps. 3️⃣ Learn Extracts those steps → identifies patterns → turns them into SOP-style skill blocks. These skill blocks then live inside a Notion-like workspace, ready to be reused whenever a similar task appears. Your agent doesn’t just respond… It remembers and improves. The architecture is genuinely smart: User ↕ Your Agent ↕ Session (stores all messages & artifacts) ↓ Task Extraction ↓ Task Completion ↓ Skill Learning ↓ Skill Blocks (saved) ↓ Search → Reuse → Improve This is the closest I’ve seen to a practical “self-learning” agent system. Multi-modal support is already built in: ✓ Text ✓ Images ✓ Files ✓ Tool calls ✓ OpenAI format ✓ Anthropic format Basically… if your agent can see it, Acontext can learn from it. Completely open-source. Apache 2.0. Free. While some companies pay $200/seat for static enterprise chatbots, you can now build self-improving agents without spending a rupee. And yes Python & TypeScript SDKs are already available. GitHub → https://lnkd.in/gS5rJbit If you’re building AI agents, this is one of the most important repos to watch right now.