Strategies to Increase Robot Intent Understanding

I've built 67+ AI agents in n8n. At first, I thought adding nodes and optimizing connections was what mattered. But I never really trusted them. Every output felt like a gamble. The bottleneck wasn't my architecture. It was my instructions. Avoid my mistakes and: 1. Separate static facts from inputs. Mixing them makes the agent guess context it should already know. → Example: Static = “Store opens at 9 AM.” Dynamic = “Order ID: 48281.” 2. Make the agent call out missing info. Guessing is the #1 source of silent failures. → Example: MISSING_FIELD: customer_email. 3. Force it to plan before acting. Step-planning stabilizes reasoning and reduces randomness. → Example: Plan internally. Output only the final result. 4. Give a fallback for impossible tasks. Without a fallback, the agent hallucinates a solution. → Example: ERROR_REASON: date_format_invalid. 5. Define “If X → Do Y” rules. Deterministic branching kills unpredictability. → Example: If date can’t be parsed → ask for a new one. 6. Allow creativity only where needed. Uncontrolled creativity = guaranteed hallucinations. → Example: Creative only in “Rewrite.” Everything else literal. 7. Limit the agent’s memory. Too much history makes the agent drift off-task. → Example: Use only the last 2 messages to determine intent. 8. Make it restate the task first. Repetition confirms the agent understood the request correctly. → Example: Task summary: extract the invoice number. 9. Validate inputs before generating outputs. Output built on bad inputs = guaranteed bad outputs. → Example: Invalid date: expected YYYY-MM-DD. 10. Require a termination signal. Your workflow needs a clear signal that the task is complete. → Example: End with “TERMINATE.” 11. Test your instructions with ugly inputs. If it only works on “happy path,” it’s not reliable - it’s lucky. → Example: Missing fields, malformed dates, weird formats. 12. Run a 10–20 sample eval before shipping. You can’t improve what you don’t measure. Vibes ≠ validation. → Example: Score each output: accuracy, format, tone, stability. 13. Iterate based on failures, not feelings. One word in your instructions can double your success rate. → Example: 2 outputs broke the format → tighten output rules. This is how you get from 30% to 80% success rate. Better instructions beat complex architecture. What's been your biggest challenge getting agents to behave consistently?

Understanding user intent is foundational to improving any AI-driven product experience. In this tech blog, Udemy’s engineering team shares how they evolved their intent-understanding system by incorporating LLMs, ultimately improving the user experience of the Udemy AI Assistant. - For the Assistant to work well, the very first step is figuring out what a learner actually means so that the system can take the right action. Early versions relied on a lightweight sentence-embedding model: user messages were mapped to a vector space and matched against example utterances to identify the closest intent. This approach worked reasonably well at the start, but as the Assistant grew to support more features and nuanced intents, it began to struggle, leading to more misclassifications and weaker responses. - To improve accuracy, the team explored larger embedding models and eventually tested using LLMs directly for intent classification. While this LLM-only approach significantly improved understanding by leveraging full conversational context, it also came with higher latency and cost. The key was a hybrid strategy: use embeddings when confidence is high, and fall back to a smaller LLM only when intent is ambiguous. This delivered a strong balance between accuracy and efficiency in production. What stands out is how real-world constraints shaped the final design. In production systems, there are always trade-offs between quality, speed, and cost—and the “best” architecture is rarely the most complex one. Udemy’s approach is a useful reminder that combining lightweight methods with LLMs in the right places can meaningfully improve user experience without over-engineering the solution. #DataScience #MachineLearning #LLM #ProductAI #AppliedML #MLSystems #IntentUnderstanding #SnacksWeeklyonDataScience – – –  Check out the "Snacks Weekly on Data Science" podcast and subscribe, where I explain in more detail the concepts discussed in this and future posts:    -- Spotify: https://lnkd.in/gKgaMvbh   -- Apple Podcast: https://lnkd.in/gFYvfB8V    -- Youtube: https://lnkd.in/gcwPeBmR https://lnkd.in/ga5JJuzN

Stop trying to build one massive AI agent. You're setting yourself up for hallucinations and latency spikes. Here are 5 architectural patterns that separate fragile demos from robust, production systems. ⬇️ I see too many teams struggle because they treat agent development like advanced prompt engineering. It's not just about prompts—it's about architecture. The 'just chat with it' phase is over. Building production-grade agents requires real engineering. 1. Decomposing Workflows Break down complex tasks into smaller, specialized agents. Have a 'supervisor' agent route requests to the right specialist—one for understanding user intent, another for retrieving data, a third for complex reasoning. This approach simplifies maintenance and makes scaling much easier. 2. Future-Proofing Your Architecture The complex logic you build today could become a single API call tomorrow as models improve. The field is moving incredibly fast. Design your system in a modular way, so you can easily swap out custom components when a better, native solution becomes available. 3. Embedding Multimodality Text-only is no longer enough. The best agent systems are built with multimodality from day one. They can process user images, understand visual context, and even generate visual outputs. Don't treat it as an add-on; it's fundamental for a complete and accurate solution. 4. Leveraging Open Protocols Stop wasting engineering cycles on custom API wrappers. Adopt open standards for both agent-to-agent (A2A) and agent-to-tool communication (MCP). This allows your decomposed agents (see point #1) to collaborate seamlessly and lets them dynamically discover and use tools with a standardized format. You're building a scalable ecosystem, not a maintenance nightmare of fragile, custom integrations. 5. Separating Reasoning & Execution Never let an LLM perform calculations or write directly to a database. That's a critical mistake. Use the LLM for what it's good at: reasoning and understanding intent. Then, force its output into a strict format (like a Pydantic model), validate it, and pass it to reliable, deterministic code for the actual execution. Let the LLM think, let your code do. Building reliable agents is a serious engineering challenge. Respect the fundamentals. What's the biggest architectural lesson you've learned building AI agents? ♻️ Repost this if you find it useful. 🔔 Follow me for more on production AI. #AgenticAI #MLOps #EnterpriseArchitecture #AIStrategy

GazeVLA: Learning Human Intention for Robotic Manipulation Arxiv: https://lnkd.in/dk6hphAw Project: https://gazevla.github.io/ 🔁 At a Glance 💡 Goal: Enable robots to transfer human intentions learned from gaze to improve manipulation. ⚙️ Approach: - Pretrain on large-scale egocentric human videos to capture intention. - Use a Chain-of-Thought reasoning paradigm for intention-first action prediction. - Finetune with small robot data for adaptation. - Explicitly model gaze as intention to bridge embodiment gap. 📈 Impact (Key Results) 🧪 Human intention prediction: 4.8% error on image diagonal. - Accurate hand and gaze reconstruction. 🔄 Better generalization across long-horizon and fine-grained tasks. - Outperforms baselines in simulation and real-robot tests. 🤖 Effective transfer from human to robot without intention labels in robot data. 🔬 Experiments 🧪 Benchmarks: AV-ALOHA, real-robot tasks. 🎯 Tasks: pick-and-place, tool use, long-horizon operations. 🦾 Setup: 8 NVIDIA A800 GPUs, two robotic platforms. 📐 Inputs: egocentric images, language, gaze, hand states. 🛠 How to Implement 1️⃣ Collect large-scale egocentric dataset with gaze. 2️⃣ Pretrain Vision-Language-Intention model. 3️⃣ Finetune on small robot dataset, combining human and robot data. 4️⃣ Use intention-action reasoning chain during inference. 5️⃣ Deploy on real robots for manipulation tasks. 📦 Deployment Benefits ✅ Improved generalization to unseen scenes. ✅ Better handling of long-horizon and fine manipulation. ✅ Cross-embodiment human-robot transfer. ✅ Reduced need for robot demonstration data. 📣 Takeaway Understanding human intention through gaze offers a powerful bridge for robotic learning. Explicit intention modeling enhances zero-shot generalization and task robustness. This work brings us closer to flexible, human-like embodied intelligence. Follow me to know more about AI, ML and Robotics!

One of our goals in 2026 is to make Modern Robot Learning accessible to all. "Teaching a Robot to Understand What You Mean, Not Just What You Say" I have been working with a SmolVLA (Vision-Language-Action) model on an SO-101 robot arm, trained to pick up a box and place it in colored bowls — red, green, and blue. Here's the catch: the model only responds correctly to the exact task descriptions it was trained on. Say "Pick up the box and place it in the blue bowl" and it works perfectly. But say "Put it in the bowl that has the color of the sky" — and it fails. The VLM backbone (SmolVLM2-500M) doesn't generalize well to paraphrased instructions at this scale. The fix? A lightweight intent classification layer. I added a sentence-transformer model (all-MiniLM-L6-v2, ~30MB) that sits between the user and the robot policy. It maps any free-form instruction to the closest canonical training task using cosine similarity: (1) "Place it in the bowl colored like the ocean" → blue bowl  (2) "Move the box to the grass-colored bowl" → green bowl  (3) "Put it in the sky-colored one" → blue bowl The classifier runs in <1ms — zero impact on the 30Hz control loop. The full pipeline: 1. Voice command captured via Whisper STT  2. Intent classified → canonical task string  3. SmolVLA policy executes with the exact training instruction  4. Seamless multi-episode inference — no reloading between tasks What I find interesting is how a tiny embedding model (~30MB) can bridge the gap that a 500M-parameter VLM struggles with. Sometimes the best solution isn't a bigger model — it's the right model at the right layer. Video below shows back-to-back episodes with different voice commands, all running on a Mac Mini with MPS acceleration.