How to Understand Agentic Systems

We’re witnessing a shift from static models to 𝗔𝗜 𝗮𝗴𝗲𝗻𝘁𝘀 𝘁𝗵𝗮𝘁 𝗰𝗮𝗻 𝘁𝗵𝗶𝗻𝗸, 𝗿𝗲𝗮𝘀𝗼𝗻, 𝗮𝗻𝗱 𝗮𝗰𝘁—not just respond. But with so many disciplines converging—LLMs, orchestration, memory, planning—how do you 𝗯𝘂𝗶𝗹𝗱 𝗮 𝗺𝗲𝗻𝘁𝗮𝗹 𝗺𝗼𝗱𝗲𝗹 to master it all? Here’s a 𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲𝗱 𝗿𝗼𝗮𝗱𝗺𝗮𝗽 to navigate the Agentic AI landscape, designed for developers and builders who want to go beyond surface-level hype: ↳ 𝟭. 𝗥𝗲𝘁𝗵𝗶𝗻𝗸 𝗜𝗻𝘁𝗲𝗹𝗹𝗶𝗴𝗲𝗻𝗰𝗲: Move from model outputs to goal-driven autonomy. Understand where Agentic AI fits in the automation stack. ↳ 𝟮. 𝗚𝗿𝗼𝘂𝗻𝗱 𝗬𝗼𝘂𝗿𝘀𝗲𝗹𝗳 𝗶𝗻 𝗔𝗜/𝗠𝗟 𝗙𝘂𝗻𝗱𝗮𝗺𝗲𝗻𝘁𝗮𝗹𝘀: Before agents, there’s learning—deep learning, reinforcement learning, and the theories powering adaptive behavior. ↳ 𝟯. 𝗘𝘅𝗽𝗹𝗼𝗿𝗲 𝘁𝗵𝗲 𝗔𝗴𝗲𝗻𝘁 𝗧𝗲𝗰𝗵 𝗦𝘁𝗮𝗰𝗸: Dive into 𝗟𝗮𝗻𝗴𝗖𝗵𝗮𝗶𝗻, 𝗔𝘂𝘁𝗼𝗚𝗲𝗻, and 𝗖𝗿𝗲𝘄𝗔𝗜—frameworks enabling coordination, planning, and tool use. ↳ 𝟰. 𝗚𝗼 𝗗𝗲𝗲𝗽 𝘄𝗶𝘁𝗵 𝗟𝗟𝗠 𝗜𝗻𝘁𝗲𝗿𝗻𝗮𝗹𝘀: Learn how tokenization, embeddings, and memory management drive better reasoning. ↳𝟱. 𝗦𝘁𝘂𝗱𝘆 𝗠𝘂𝗹𝘁𝗶-𝗔𝗴𝗲𝗻𝘁 𝗖𝗼𝗹𝗹𝗮𝗯𝗼𝗿𝗮𝘁𝗶𝗼𝗻: Agents aren’t lone wolves—they negotiate, delegate, and synchronize in distributed workflows. ↳𝟲. 𝗔𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁 𝗠𝗲𝗺𝗼𝗿𝘆 + 𝗥𝗲𝘁𝗿𝗶𝗲𝘃𝗮𝗹: Understand how 𝗥𝗔𝗚, vector stores, and semantic indexing turn short-term chatbots into long-term thinkers. ↳𝟳. 𝗗𝗲𝗰𝗶𝘀𝗶𝗼𝗻-𝗠𝗮𝗸𝗶𝗻𝗴 𝗮𝘀 𝗮 𝗦𝗸𝗶𝗹𝗹: Build agents with layered planning, feedback loops, and reinforcement-based self-improvement. ↳𝟴. 𝗠𝗮𝗸𝗲 𝗣𝗿𝗼𝗺𝗽𝘁𝗶𝗻𝗴 𝗗𝘆𝗻𝗮𝗺𝗶𝗰: From few-shot to chain-of-thought, prompt engineering is the new compiler—learn to wield it with intention. ↳𝟵. 𝗥𝗲𝗶𝗻𝗳𝗼𝗿𝗰𝗲𝗺𝗲𝗻𝘁 + 𝗦𝗲𝗹𝗳-𝗢𝗽𝘁𝗶𝗺𝗶𝘇𝗮𝘁𝗶𝗼𝗻: Agents that improve themselves aren’t science fiction—they're built on adaptive loops and human feedback. ↳𝟭𝟬. 𝗢𝗽𝘁𝗶𝗺𝗶𝘇𝗲 𝗥𝗲𝘁𝗿𝗶𝗲𝘃𝗮𝗹-𝗔𝘂𝗴𝗺𝗲𝗻𝘁𝗲𝗱 𝗚𝗲𝗻𝗲𝗿𝗮𝘁𝗶𝗼𝗻: Master hybrid search and scalable retrieval pipelines for real-time, context-rich AI. ↳𝟭𝟭. 𝗧𝗵𝗶𝗻𝗸 𝗗𝗲𝗽𝗹𝗼𝘆𝗺𝗲𝗻𝘁, 𝗡𝗼𝘁 𝗝𝘂𝘀𝘁 𝗗𝗲𝗺𝗼𝘀: Production-ready agents need low latency, monitoring, and integration into business workflows. 𝟭𝟮. 𝗔𝗽𝗽𝗹𝘆 𝘄𝗶𝘁𝗵 𝗣𝘂𝗿𝗽𝗼𝘀𝗲: From copilots to autonomous research assistants—Agentic AI is already solving real problems in the wild. 𝗔𝗴𝗲𝗻𝘁𝗶𝗰 𝗔𝗜 𝗶𝘀𝗻’𝘁 𝗷𝘂𝘀𝘁 𝗮𝗯𝗼𝘂𝘁 𝘀𝗺𝗮𝗿𝘁𝗲𝗿 𝗼𝘂𝘁𝗽𝘂𝘁𝘀—𝗶𝘁’𝘀 𝗮𝗯𝗼𝘂𝘁 𝗶𝗻𝘁𝗲𝗻𝘁𝗶𝗼𝗻𝗮𝗹, 𝗽𝗲𝗿𝘀𝗶𝘀𝘁𝗲𝗻𝘁 𝗶𝗻𝘁𝗲𝗹𝗹𝗶𝗴𝗲𝗻𝗰𝗲. If you're serious about building the next wave of intelligent systems, this roadmap is your compass. Curious—what part of this roadmap are you diving into right now?

If you’re an AI engineer, here are the 15 components of agentic AI you should know. Building truly agentic systems goes far beyond chaining prompts or wiring tools. It requires modular intelligence that can perceive, plan, act, learn, and adapt across dynamic environments - autonomously and reliably. This framework breaks it down into 15 technical components: 🔴 1. Goal Formulation → Agents must define explicit objectives, decompose them into subgoals, prioritize execution, and adapt dynamically as new context arises. 🟣 2. Perception → Real-time sensing across modalities (text, visual, audio, sensors) with uncertainty estimation and context grounding. 🟠 3. Cognition & Reasoning → From world modeling to causal inference, agents need inductive, abductive reasoning, planning, and introspection via structured knowledge (graphs, ontologies). 🔴 4. Action Selection & Execution → This includes policy learning, planning, trial-and-error correction, and UI/tool interfacing to interact with real systems. 🟣 5. Autonomy & Self-Governance → Independence from human-in-the-loop oversight through constraint-aware, initiative-taking decision frameworks. 🟠 6. Learning & Adaptation → Support for continual learning, transfer learning, and meta-learning with feedback-driven self-improvement loops. 🔴 7. Memory & State Management → Episodic memory, working memory buffers, and semantic grounding for contextually-aware actions over time. 🟣 8. Interaction & Communication → Natural language generation and understanding, negotiation, and multi-agent coordination with social signal processing. 🟠 9. Monitoring & Self-Evaluation → Agents should monitor their own performance, detect anomalies, benchmark against goals, and recover autonomously. 🔴 10. Ethical and Safety Control → Safety constraints, transparency, explainability, and alignment to human values - non-negotiable for real-world deployment. 🟣 11. Resource Management → Optimizing compute, memory, and energy with intelligent resource scheduling and infrastructure-aware orchestration. 🟠 12. Persistence & Continuity → Agents must preserve goal state across sessions, maintain behavioral consistency, and recover from disruptions. 🔴 13. Agency Integration Layer → Modular architecture, orchestration of internal components, and hierarchical control systems for scalable design. 🟣 14. Meta-Agent Capabilities → Delegation to sub-agents, participation in agent collectives, and orchestration of agent teams with diverse roles. 🟠 15. Interface & Environment Adaptability → Adaptation across domains and tools with robust APIs and reconfigurable sensing-actuation layers. 〰️〰️〰️ 🔁 Save and share this if you’re designing agents beyond the demo stage. 🔔 Follow me (Aishwarya Srinivasan) for more data & AI insights

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 

At first glance, Google’s new Gemma 3 open-source LLM doesn’t look like a big deal. However, one stat makes it a game-changer for agentic AI: It runs and serves inference on a single H100 GPU. Most benchmarks are meaningless, but this one matters. Agentic #AI systems support complex, multi-step workflows. Serving a single user or customer request requires several calls to the LLM that can tie up the model for a few minutes at a time. If you have a 100-person development team using generative AI coding tools (non-agentic) running DeepSeek, each developer consumes 32 H100s with a single request, but requests are served relatively quickly. With load balancing, you don’t need 3,200 H100s to support the team. Agentic systems are a different story. A single request can involve 15 minutes of working with a user to deliver an outcome. An agentic AI system for recruiting takes a job description and follows a multi-step process to schedule phone screenings. 1️⃣ Review the job description and extract the key requirements. 2️⃣ Search internal and external sources for matching resumes. Aggregate multi-source data into matching profiles. 3️⃣ Rank the profiles and resumes for targeted outreach. 4️⃣ Send text messages, DMs, and emails to schedule a phone screen appointment. 5️⃣ Monitor those channels for responses and handle the back and forth to schedule the appointment. I am oversimplifying the workflow, but you get the point. Agentic systems require multiple instances of the #LLM to serve a single request, multiple requests per LLM, and LLMs that are dedicated to continuous monitoring tasks. A DeepSeek-based agentic system costs 32 times the one built with Gemma. Bringing the costs down means more use cases become viable. Gemma 3 is a huge step forward for agentic AI and supporting more complex workflows.

Agentic AI is evolving and we are seeing four emerging patterns. Agentic AI systems don’t just answer questions, but actively do, decide, and drive business outcomes. If you’re mapping your organization’s AI journey, understanding the levels of agentic capability is crucial for unlocking both monetization and margin potential. Most of our customers are using Level 1, some are using Level 2 and Level 3. Level 4 has multiple challenges with sandboxing, security and governance. Mainly startups that are innovating in this space. Enterprises mostly are sitting this one out, for now. The Four Levels of Agentic AI: From Queries to Autonomy 1. Query Agents: The Generative Foundation These are your classic AI assistants with a plus: users ask questions, get answers. They support employees by surfacing information fast but don’t act on it. Think: knowledge retrieval, basic chatbots, or AI-powered search. 2. Task Agents: Getting Things Done Agents now complete discrete tasks—like scheduling meetings, drafting emails, or pulling reports. They access corporate knowledge and integrate with existing workflows, but still need human oversight. The payoff? Significant time savings and reduced manual effort, though boundaries and data quality remain key. 3. Workflow Agents: Orchestrating Complexity Here, agents handle multi-step workflows, integrating deeply into tech stacks and collaborating with other agents or systems. They plan, sequence, and adapt actions dynamically—think troubleshooting IT issues, automating onboarding, or managing campaigns. These agents leverage proprietary data and can iterate based on results, reducing manual intervention and boosting efficiency. 4. Autonomous Agents: The Future, Now The pinnacle: agents that understand entire business processes, access multiple systems, and operate with minimal human oversight. They don’t just follow instructions—they set goals, adapt to new scenarios, and optimize for outcomes in real time. Why This Matters As you move up the agentic ladder, both the value and margin potential increase dramatically. Query agents save time; autonomous agents can reinvent entire workflows, drive innovation, and open new business models. According to Gartner, Agentic AI will make 15% of all organizational decisions autonomously by 2028. Key Takeaways for Leaders a. Start with the basics: Ensure your data is organized and accessible to enable higher levels of agentic automation. b. Define governance and boundaries: Set clear rules for agent autonomy to balance efficiency with oversight. c. Invest in integration: The real value comes when agents orchestrate across systems, not just within silos. d. Prepare for autonomy: As agents become more capable, they’ll need less human intervention—freeing your teams for higher-value work. Agentic AI isn’t just a technology trend—it’s the new foundation for digital business. What are your thoughts about evolution of Agentic AI?

Here's the difference between AI agents and agentic AI 1. Most AI tools today do one task well. That’s an AI Agent. 2. The future is systems that think, plan, and collaborate. That’s Agentic AI. 3. AI Agents are doers. Agentic AI are decision-makers. 4. AI Agents follow rules. Agentic AI writes new ones on the fly. 5. One works solo. The other is a team of AIs working together. 6. AI Agents help with email replies. Agentic AI can run your research lab. 7. Agentic AI uses memory, planning, and collaboration to handle chaos. 8. Imagine 10 AIs, each with a role, working as a team. That’s Agentic AI. 9. AI Agents are task-focused. Agentic AI is outcome-focused. 10. AI Agents get stuck in loops. Agentic AI adapts and moves on. 11. AI Agents break when context shifts. Agentic AI re-plans. 12. You can’t scale AI without memory. Agentic AI solves that. 13. The key is orchestration, getting agents to cooperate without chaos. 14. Think supply chains, hospitals, game engines, and Agentic AI fit here. 15. Chatbots aren’t intelligent systems. That’s the trap. 16. Real intelligence comes from coordination, not just generation. 17. Long-term reliability needs memory, reflex, and collaboration. 18. Agentic AI uses feedback loops: act → observe → adjust. 19. AI Agents hallucinate when out of context. Agentic AI learns from mistakes. 20. Managing Agentic AI needs governance, not just prompts. 21. The biggest challenge is coordination without confusion. 22. When your AI team argues with itself, things break. 23. Agentic AI needs transparency, traceability, and strong rules. 24. Tool use isn’t enough. The system must choose the right tool. 25. RAG, ReAct, memory layers, these are real building blocks, not buzzwords. 26. Most startups today are still building AI Agents. 27. The next wave is tools that help multiple agents work together. 28. This paper gives a roadmap for the next 5 years of AI. 29. If you’re building AI products, this is the playbook to study. 30. Don’t just build a smart agent. Build a smart system Share this with anyone unclear on AI agents vs. agentic intelligence.

Day 1: What is Agentic AI? (And How is it Different from Generative AI?) Most people know about Generative AI — tools like ChatGPT, Claude or DALL·E that help create content: words, images, code, presentations. But Agentic AI is something different. Agentic AI doesn’t just create — it acts. It’s designed to: • Understand a goal • Break it down into tasks • Make decisions • Execute actions • Learn and adjust as it works The easiest way to think about it? → Generative AI gives you suggestions. → Agentic AI gets things done. ⸻ Why does this matter for business? Agentic AI shifts AI from being a helper → to being a doer. Example: Let’s say you’re launching a new product. Generative AI: → “Write me a social media post announcing our new product.” Agentic AI: → “Create a social media post, schedule it across all platforms, update the product page on the website, generate internal FAQs for the sales team, draft outreach emails to key clients, monitor engagement, and flag any customer questions that need a human response.” ⸻ → Generative AI gives you content. → Agentic AI runs the playbook. Tomorrow (Day 2), I’ll share examples of where Agentic AI is already showing up in real businesses — with focus on how teams operate.