Building Autonomous Robotics Systems

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?

If you are building AI agents or learning about them, then you should keep these best practices in mind 👇 Building agentic systems isn’t just about chaining prompts anymore, it’s about designing robust, interpretable, and production-grade systems that interact with tools, humans, and other agents in complex environments. Here are 10 essential design principles you need to know: ➡️ Modular Architectures Separate planning, reasoning, perception, and actuation. This makes your agents more interpretable and easier to debug. Think planner-executor separation in LangGraph or CogAgent-style designs. ➡️ Tool-Use APIs via MCP or Open Function Calling Adopt the Model Context Protocol (MCP) or OpenAI’s Function Calling to interface safely with external tools. These standard interfaces provide strong typing, parameter validation, and consistent execution behavior. ➡️ Long-Term & Working Memory Memory is non-optional for non-trivial agents. Use hybrid memory stacks, vector search tools like MemGPT or Marqo for retrieval, combined with structured memory systems like LlamaIndex agents for factual consistency. ➡️ Reflection & Self-Critique Loops Implement agent self-evaluation using ReAct, Reflexion, or emerging techniques like Voyager-style curriculum refinement. Reflection improves reasoning and helps correct hallucinated chains of thought. ➡️ Planning with Hierarchies Use hierarchical planning: a high-level planner for task decomposition and a low-level executor to interact with tools. This improves reusability and modularity, especially in multi-step or multi-modal workflows. ➡️ Multi-Agent Collaboration Use protocols like AutoGen, A2A, or ChatDev to support agent-to-agent negotiation, subtask allocation, and cooperative planning. This is foundational for open-ended workflows and enterprise-scale orchestration. ➡️ Simulation + Eval Harnesses Always test in simulation. Use benchmarks like ToolBench, SWE-agent, or AgentBoard to validate agent performance before production. This minimizes surprises and surfaces regressions early. ➡️ Safety & Alignment Layers Don’t ship agents without guardrails. Use tools like Llama Guard v4, Prompt Shield, and role-based access controls. Add structured rate-limiting to prevent overuse or sensitive tool invocation. ➡️ Cost-Aware Agent Execution Implement token budgeting, step count tracking, and execution metrics. Especially in multi-agent settings, costs can grow exponentially if unbounded. ➡️ Human-in-the-Loop Orchestration Always have an escalation path. Add override triggers, fallback LLMs, or route to human-in-the-loop for edge cases and critical decision points. This protects quality and trust. PS: If you are interested to learn more about AI Agents and MCP, join the hands-on workshop, I am hosting on 31st May: https://lnkd.in/dWyiN89z If you found this insightful, share this with your network ♻️ Follow me (Aishwarya Srinivasan) for more AI insights and educational content.

The robotics algorithm library every engineer should know! 📚 PythonRobotics is an open-source collection of Python code and textbook for robotics algorithms, created by Atsushi Sakai. It has 27.2k stars on GitHub and 7k forks, so it's a no brainer to bookmark it! 🔖 The project covers everything from localization (EKF, particle filters, histogram filters) to SLAM (FastSLAM, ICP matching), path planning (A*, RRT*, Dijkstra, D*, potential fields, state lattice), path tracking (Stanley, LQR, MPC), arm navigation, aerial navigation, and even bipedal planning. What makes this special? It's designed to be easy to read and understand, with minimal dependencies and practical, widely-used algorithms. Each algorithm comes with visual animations, mathematical explanations, and working code. The documentation is essentially a full textbook on robotics algorithms, available free online at https://lnkd.in/dvuuVy6e. Requirements are simple: Python 3.13+, NumPy, SciPy, Matplotlib, and cvxpy. That's it. This a learning resource with 2,201 commits, contributions from 138 developers, and active maintenance. The animations alone (stored in a separate repo) are worth studying. If you're learning robotics, building autonomous systems, or teaching algorithms, this is the resource. It's MIT licensed, so you can use it freely in research or commercial projects. Here's the link: https://lnkd.in/dNX5JzkX P.S. This is what good open-source looks like: educational, practical, well-documented, and community-driven. Bookmark it. 🔖 ~~ ♻️ Join the weekly robotics newsletter, and never miss any news → ziegler.substack.com

Happy to share our latest paper, "Enabling Novel Mission Operations and Interactions with ROSA: The Robot Operating System Agent". This work was led by Rob R. in collaboration with Marcel Kaufmann, Jonathan Becktor, Sangwoo Moon, Kalind Carpenter, Kai Pak, Amanda Towler, Rohan Thakker and myself. Please find the #OpenSource code, paper, and video demonstration linked below. Operating autonomous robots in the field is often challenging, especially at scale and without the proper support of Subject Matter Experts (SMEs). Traditionally, robotic operations require a team of specialists to monitor diagnostics and troubleshoot specific modules. This dependency can become a bottleneck when an SME is unavailable, making it difficult for operators to not only understand the system's functional state but to leverage its full capability set. The challenge grows when scaling to 1-to-N operator-to-robot interactions, particularly with a heterogeneous robot fleet (e.g., walking, roving, flying robots). To address this, we present the ROSA framework, which can leverage state-of-the-art Vision Language Models (VLMs), both on-device and online, to present the autonomy framework's capabilities to operators in an intuitive and accessible way. By enabling a natural language interface, ROSA helps bridge the gap for operators who are not roboticists, such as geologists or first responders, to effectively interact with robots in real-world missions. In our video, we demonstrate ROSA using the NeBula Autonomy framework developed at NASA Jet Propulsion Laboratory to operate in JPL's #MarsYard. Our paper also showcases ROSA's integration with JPL's EELS (Exobiology Extant Life Surveyor) robot and the NVIDIA Carter robot in the IsaacSim environment (stay tuned for ROSA IssacSim extension updates!). These examples highlight ROSA's ability to facilitate interactions across diverse robotic platforms and autonomy frameworks. Paper: https://lnkd.in/g4PRjF4V Github: https://lnkd.in/gwWXmmjR Video: https://lnkd.in/gxKcum27 #Robotics #Autonomy #AI #ROS #FieldRobotics #RobotOperations #NaturalLanguageProcessing #LLM #VLM

𝗠𝗼𝘀𝘁 𝗽𝗲𝗼𝗽𝗹𝗲 𝘁𝗵𝗶𝗻𝗸 𝗯𝘂𝗶𝗹𝗱𝗶𝗻𝗴 𝗔𝗜 𝘀𝘆𝘀𝘁𝗲𝗺𝘀 𝘀𝘁𝗮𝗿𝘁𝘀 𝘄𝗶𝘁𝗵 𝗰𝗵𝗼𝗼𝘀𝗶𝗻𝗴 𝗮 𝗺𝗼𝗱𝗲𝗹. It doesn’t. The real challenge is building the system around the model. That’s where most teams lose time: wrong assumptions, weak workflows, no memory, poor tool usage, and zero monitoring. If you’re building AI systems, here’s a simple roadmap that actually helps: 1️⃣ 𝗟𝗲𝗮𝗿𝗻 𝘁𝗵𝗲 𝗯𝗮𝘀𝗶𝗰𝘀 Understand the difference between: • Chatbots     • Agents • Workflows   • Automation vs autonomy 2️⃣ 𝗨𝗻𝗱𝗲𝗿𝘀𝘁𝗮𝗻𝗱 𝘁𝗵𝗲 𝗰𝗼𝗿𝗲 𝗰𝗼𝗺𝗽𝗼𝗻𝗲𝗻𝘁𝘀 Every useful agent needs: • An LLM     • Prompts • Tools        • Memory • An environment to operate in 3️⃣ 𝗟𝗲𝗮𝗿𝗻 𝗽𝗿𝗼𝗺𝗽𝘁𝗶𝗻𝗴 𝗽𝗿𝗼𝗽𝗲𝗿𝗹𝘆 Not prompt hacks. Clear system instructions, rules, examples, and expected outputs. 4️⃣ 𝗕𝘂𝗶𝗹𝗱 𝗼𝗻𝗲 𝘀𝗶𝗺𝗽𝗹𝗲 𝗮𝗴𝗲𝗻𝘁 𝗳𝗶𝗿𝘀𝘁 Start with one use case. Keep it narrow. Make it reliable before making it bigger. 5️⃣ 𝗔𝗱𝗱 𝗺𝗲𝗺𝗼𝗿𝘆 Short-term memory for context. Long-term memory for continuity. This is where agents start becoming useful. 6️⃣ 𝗖𝗼𝗻𝗻𝗲𝗰𝘁 𝘁𝗼𝗼𝗹𝘀 𝗮𝗻𝗱 𝗔𝗣𝗜𝘀 This is where AI stops just answering… and starts doing. 7️⃣ 𝗕𝘂𝗶𝗹𝗱 𝘁𝗵𝗲 𝗳𝘂𝗹𝗹 𝘄𝗼𝗿𝗸𝗳𝗹𝗼𝘄 Prompt → reasoning → tool use → output → fallback → logging That flow matters more than most people think. 8️⃣ 𝗘𝘅𝗽𝗹𝗼𝗿𝗲 𝗺𝘂𝗹𝘁𝗶-𝗮𝗴𝗲𝗻𝘁 𝘀𝘆𝘀𝘁𝗲𝗺𝘀 Planner, executor, reviewer. Different roles. Shared context. Better coordination. 9️⃣ 𝗗𝗲𝗽𝗹𝗼𝘆 𝗮𝗻𝗱 𝗺𝗼𝗻𝗶𝘁𝗼𝗿 If you’re not tracking: • Latency    • Token usage • Errors       • Safety • Uptime Then it’s not production-ready. 🔟 𝗦𝘁𝗮𝘆 𝗰𝗹𝗼𝘀𝗲 𝘁𝗼 𝘁𝗵𝗲 𝗲𝗰𝗼𝘀𝘆𝘀𝘁𝗲𝗺 The space is moving fast. Build, test, benchmark, and keep learning. Here’s the thing: AI systems are not just prompts and models. They are architecture. That includes: • Memory          • Retrieval • Orchestration    • Tools • Evaluation        • Monitoring At 𝗦𝗲𝗿𝘃𝗶𝗰𝗲𝘀 𝗚𝗿𝗼𝘂𝗻𝗱, this is exactly how we approach AI systems, not as isolated demos, but as production-ready workflows built to solve real business problems. Because the difference between an AI demo and an AI product is usually system design. Which phase do you think teams underestimate the most? Come hang out on 𝗔𝗴𝗲𝗻𝘁𝗶𝗰 𝗢𝗽𝘀 𝗛𝘂𝗯 𝗗𝗶𝘀𝗰𝗼𝗿𝗱: https://lnkd.in/dF6nNhK4 𝗦𝘂𝗯𝘀𝗰𝗿𝗶𝗯𝗲 𝗳𝗼𝗿 𝗳𝗿𝗲𝗲: https://lnkd.in/dzQpf5uQ #AI #AgenticAI #AIEngineering #Automation #LLM #ServicesGround

Most LLM apps today are still stuck in a straight line: prompt → response. But real-world autonomy doesn’t work that way. When you're building AI agents to actually get things done - not just chat - you need more than text generation. You need reflection. You need planning. You need tools. You need teamwork. That’s where Agentic Design Patterns come in. At Jeeva, we’ve seen these four pillars (what we call the RTPM framework) separate toy projects from production-grade autonomy:  🔁 Reflection — Can the agent check its own work? Self-correct? Replan? 🛠 Tool Use — Can it call APIs, run code, query a DB not just “suggest” it? 📊 Planning — Can it break down long tasks and adapt when the path shifts? 🤖 Multi-Agent Collaboration — Can it hand off, validate, and sync across teammates? These aren’t fancy wrappers. They’re the bones of real AI systems — where output isn't just words, it's outcomes. If you're still chaining prompts, you're missing the shift. RTPM isn't theory - it's how agents learn, scale, and self-improve in production. Want to see how we structure real agentic systems at Jeeva? Follow Gaurav Bhattacharya for more breakdowns or DM if you’re building in this space.

From Foundation to Reflex: The New Standard for Next-Gen, Scalable, and Assured Autonomy For more than a decade, autonomous vehicle development has been dominated by two opposing design philosophies: modular pipelines and end-to-end learning. Modular architectures, which break autonomy into separate perception, planning, and control blocks, offer explainability and task-specific optimization. However, they often suffer from integration complexity, brittle hand-offs between modules, and challenges in generalizing across domains. In contrast, pure end-to-end models simplify the system, directly mapping sensor inputs to driving commands. But such systems lack semantic understanding and are often unable to explain or adapt their behavior in unstructured or novel scenarios. A new architectural paradigm is emerging that aims to combine the strengths of both approaches. This involves integrating global scene understanding models with reflexive control layers. At its core, the architecture leverages semantic understanding of the environment, extracted via large-scale pre-trained models capable of processing multimodal inputs such as images, LiDAR, and radar. These models generalize across environments and provide high-level interpretations like lane topology, object classification, and actor behaviors. Crucially, this semantic representation becomes the structured input for control layers responsible for direct action generation. In essence, autonomous systems begin to drive based on what they understand, not just what they sense. Equally critical is the control side of the architecture. Reflexive control networks designed for low-latency, real-time actuation process environmental context and generate precise motion commands. These networks are optimized for direct control objectives such as collision avoidance, energy-efficient routing, and path stability. By decoupling scene interpretation from control logic, this approach allows perception and control components to evolve independently, while their fusion ensures coordinated, real-time decision-making. This structure also supports adaptive behaviors in complex, partially structured environments like logistics yards, ports, and industrial campuses, where fixed HD maps or conventional rule-based planners are inadequate. This architectural shift from “foundation to reflex” is no longer theoretical. It represents a practical, scalable strategy for deploying autonomous systems in real-world environments where both generalized intelligence and specialized control are required. In the years ahead, the autonomous systems that lead will not be those that think the best, nor those that react the fastest, but those that can do both—thinking like a human, reacting like a machine. #AutonomousVehicles #FoundationModels #ScalableAutonomy #ReflexiveControl #EndToEndAI #NextGenAutonomy #RealTimeAI #SemanticPerception #AIInnovation #IntelligentMobility

💡 𝐇𝐞𝐫𝐞'𝐬 𝐰𝐡𝐚𝐭 𝐧𝐨𝐛𝐨𝐝𝐲 𝐭𝐞𝐥𝐥𝐬 𝐲𝐨𝐮 𝐚𝐛𝐨𝐮𝐭 𝐛𝐮𝐢𝐥𝐝𝐢𝐧𝐠 𝐦𝐮𝐥𝐭𝐢-𝐚𝐠𝐞𝐧𝐭 𝐚𝐫𝐜𝐡𝐢𝐭𝐞𝐜𝐭𝐮𝐫𝐞𝐬. So everyone’s obsessed with AI agents right now. But building one agent is easy. Building a colony of them — that coordinate, reason, and adapt — is a completely different game. Here’s what nobody tells you 👇 1. 𝐈𝐭’𝐬 𝐧𝐨𝐭 𝐚𝐛𝐨𝐮𝐭 𝐢𝐧𝐭𝐞𝐥𝐥𝐢𝐠𝐞𝐧𝐜𝐞. 𝐈𝐭’𝐬 𝐚𝐛𝐨𝐮𝐭 𝐜𝐨𝐨𝐫𝐝𝐢𝐧𝐚𝐭𝐢𝐨𝐧. The bottleneck isn’t reasoning — it’s orchestration. How agents talk to each other, share context, and handle conflicting goals. You’re not optimizing a model anymore — you’re architecting communication. 2. 𝐌𝐞𝐦𝐨𝐫𝐲 𝐤𝐢𝐥𝐥𝐬 𝐬𝐜𝐚𝐥𝐢𝐧𝐠 𝐟𝐚𝐬𝐭𝐞𝐫 𝐭𝐡𝐚𝐧 𝐥𝐚𝐭𝐞𝐧𝐜𝐲 𝐞𝐯𝐞𝐫 𝐰𝐢𝐥𝐥. In single-agent setups, context is the limit. In multi-agent setups, it’s memory synchronization. Each agent builds its own world view — but maintaining a consistent global state becomes impossible. It’s CAP theorem all over again — for cognition, not data. 3. 𝐄𝐦𝐞𝐫𝐠𝐞𝐧𝐭 𝐛𝐞𝐡𝐚𝐯𝐢𝐨𝐫 𝐬𝐨𝐮𝐧𝐝𝐬 𝐜𝐨𝐨𝐥 — 𝐮𝐧𝐭𝐢𝐥 𝐢𝐭 𝐛𝐫𝐞𝐚𝐤𝐬 𝐩𝐫𝐨𝐝𝐮𝐜𝐭𝐢𝐨𝐧. The moment agents can spawn tasks or hit APIs, chaos begins. You’ll need supervisor agents, rate limits, and sandboxing — basically, Kubernetes for AI cognition. 4. 𝐓𝐡𝐞𝐫𝐞’𝐬 𝐧𝐨 𝐬𝐮𝐜𝐡 𝐭𝐡𝐢𝐧𝐠 𝐚𝐬 𝐬𝐭𝐚𝐭𝐞𝐥𝐞𝐬𝐬 𝐢𝐧𝐭𝐞𝐥𝐥𝐢𝐠𝐞𝐧𝐜𝐞. Agents evolve — they remember, forget, and relearn. Without architectural memory — persistent stores and trust graphs — orchestration breaks down. It’s not prompt engineering anymore. It’s context engineering. 5. 𝐓𝐡𝐞 𝐛𝐢𝐠𝐠𝐞𝐬𝐭 𝐜𝐡𝐚𝐥𝐥𝐞𝐧𝐠𝐞 𝐢𝐬𝐧’𝐭 𝐭𝐞𝐜𝐡𝐧𝐢𝐜𝐚𝐥 — 𝐢𝐭’𝐬 𝐚𝐫𝐜𝐡𝐢𝐭𝐞𝐜𝐭𝐮𝐫𝐚𝐥. Fifty autonomous components talking asynchronously means debugging turns into archaeology. You stop managing threads — you start managing behaviors. We’re entering a world where AI systems behave less like codebases and more like distributed organisms — complex, adaptive, and unpredictable. The challenge isn’t building them. It’s understanding them once they start running. That’s the kind of architecture we break down inside my Advanced System Design Cohort — A 3-month intensive for senior engineers who want to master adaptive, large-scale system design. If you think you’re a fit, there’s a link in the comments. Fill it out — we’ll review your profile and get in touch. We’re selective. Because this is where the next generation of technical leaders are being built.

Most autonomous robots today use a traditional "sense, think, act" architecture. That is, separate code (often implemented by separate teams) are responsible for perceiving what is in the environment, deciding on an appropriate course of action, and carrying out that action. What if we could simplify this, and instead have a single AI model sense, think, and act all at once? That is the domain of Robot Learning and Embodied AI. This week, researchers at UC Berkeley announced SERL, a new open source library for "Sample-Efficient Robotic Reinforcement Learning". Instead of supporting many different reinforcement learning algorithms, they selected sensible defaults, optimizing for being able to train their model with as few attempts as possible (that's the "sample-efficient" part). When they put this new library to the test, they were able to learn tasks much faster and more accurately than anyone has previously achieved. For example, it learned the PCB insertion task in this video to 100% accuracy with just 20 demonstrations and 20 minutes of learning! Now, if only I could get their dataset in mcap format I could visualize this nicely in Foxglove 😄 https://lnkd.in/gwQQ5JVq

𝗧𝗵𝗲 𝗔𝗴𝗲𝗻𝘁𝗶𝗰 𝗔𝗜 𝗥𝗼𝗮𝗱𝗺𝗮𝗽: 𝗙𝗿𝗼𝗺 𝗖𝗼𝗱𝗲 𝘁𝗼 𝗔𝘂𝘁𝗼𝗻𝗼𝗺𝗼𝘂𝘀 𝗜𝗻𝘁𝗲𝗹𝗹𝗶𝗴𝗲𝗻𝗰𝗲 Building AI agents that think, reason, and act autonomously requires more than prompt engineering. It demands a structured approach that combines foundational skills, advanced frameworks, and production ready infrastructure. 𝗘𝘀𝘀𝗲𝗻𝘁𝗶𝗮𝗹 𝗦𝗸𝗶𝗹𝗹𝘀 𝗙𝗼𝘂𝗻𝗱𝗮𝘁𝗶𝗼𝗻 Start with Python as your core language, master FastAPI for building robust APIs, understand Docker for consistent deployments, and learn Git for version control. These tools form the bedrock of any serious AI development workflow. 𝗟𝗟𝗠 𝗮𝗻𝗱 𝗠𝗟 𝗙𝗿𝗮𝗺𝗲𝘄𝗼𝗿𝗸𝘀 Get comfortable with leading models like Nova, GPT-5, Claude, and LLaMA. Use Hugging Face Transformers for model access, LangChain for orchestration, and LlamaIndex for retrieval. Apply PEFT and LoRA for efficient fine tuning, and run models locally with Ollama and vLLM. 𝗗𝗲𝘀𝗶𝗴𝗻𝗶𝗻𝗴 𝗔𝗴𝗲𝗻𝘁 𝗦𝘆𝘀𝘁𝗲𝗺𝘀 Study ReAct, CoT, AutoGPT, and function calling patterns. Explore multi agent systems with MCP and A2A frameworks for workflow coordination. Understanding these patterns separates basic chatbots from intelligent autonomous systems. 𝗞𝗲𝘆 𝗜𝗻𝗳𝗿𝗮𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲 𝗧𝗼𝗼𝗹𝘀 Deploy vector databases like OpenSearch, ChromaDB, Pinecone, and Weaviate for semantic search. Manage memory with Redis, Zep, and SQLite. Monitor performance using LangSmith, Prompty, and TruLens. Handle async operations with Celery, RabbitMQ, and Kafka. 𝗔𝗱𝘃𝗮𝗻𝗰𝗲𝗱 𝗖𝗼𝗻𝗰𝗲𝗽𝘁𝘀 Protect against prompt injections with GuardrailsAI and Rebuff. Enable multi agent collaboration via CrewAI or AutoGen. Implement reinforcement learning from human feedback to continuously improve agent performance. 𝗣𝗿𝗼𝗱𝘂𝗰𝘁𝗶𝗼𝗻 𝗗𝗲𝗽𝗹𝗼𝘆𝗺𝗲𝗻𝘁 Host on AWS, Azure, GCP, or Render. Containerize with Docker and FastAPI. Automate with GitHub Actions and Hugging Face Spaces. Track everything with Loguru and Prometheus. Follow Umair Ahmad for more insights. #AgenticAI #MultiAgentSystems #LLM #MachineLearning #Python #AI #AutoGPT #LangChain