Integrating Robotic Intelligence Across Multiple Platforms

A common question I'm frequently asked: "Which AI agent framework do you use for building AI apps?" Initially, I jumped into LangChain and LlamaIndex, quickly got intrigued by CrewAI, and lately, I'm diving deeper into Agno—each great in their own way. With OpenAI and Google also releasing their own AI agent frameworks, the landscape keeps changing fast. But here's what's interesting—I recognized early on that no matter which framework I selected, an AI agent is only as powerful as the tools it leverages. And keeping tools modular, reusable, and cross-project compatible turned out to be a real engineering headache. Every new AI project felt repetitive—building similar tools again and again was neither efficient nor scalable. Then I came across MCP: the Model Context Protocol, an open-source protocol framework developed by Anthropic. Think of MCP like HTTP—but instead of websites, it makes AI agents and tools universally connectable. Although I've experimented with MCP before, yesterday it got enabled on my go-to automation platform: n8n. I quickly spun up an MCP server on n8n, populated it with custom-built tools, existing utilities, and even embedded n8n workflows themselves as reusable AI tools. Now I have a single, cohesive "toolbox" server that I seamlessly integrate across multiple AI projects—be it Cursor, Claude desktop, or my own custom agents built on LangChain or Agno. If you're building AI-driven products or workflows, I'd highly recommend exploring MCP for tool interoperability. MCP feels like true engineering efficiency—it lets me stop reinventing wheels and finally spend time on real innovations. #GenAI #AIAgents #MCP #n8n

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

The age of static APIs is ending, and with it, the traditional model of software as a service. In its place emerges a radical new paradigm: SaaS platforms that think, learn, and adapt—exposing not hundreds of rigid endpoints but a single, intelligent interface that understands natural language and orchestrates complexity behind the scenes. Tool selection with the Model Context Protocol (MCP) represents a critical engineering challenge in making large-scale software systems accessible to AI agents. As SaaS platforms expose hundreds or thousands of functions and AI agents gain access to multiple services, the traditional approach of presenting all available tools to an LLM becomes untenable due to prompt bloat and decision complexity. The prompt bloat phenomenon emerges as a fundamental constraint when scaling tool-augmented LLMs. LLM performance degrades significantly when presented with more than approximately 100 tools simultaneously, with models exhibiting confusion in tool selection and even hallucinating non-existent APIs. This degradation comes from both token consumption overwhelming the context window and the cognitive complexity of distinguishing between numerous similar tools. The RAG-MCP framework represents a paradigm shift from static tool presentation to dynamic tool discovery. By maintaining a vector index of tool metadata and retrieving only the most relevant tools for each query, the system reduces prompt tokens by over 50% while tripling selection accuracy from 13.62% to 43.13%. This approach fundamentally changes how LLMs interact with external tools, moving from exhaustive enumeration to intelligent selection. Ontology-driven tool management introduces explicit modeling of tool relationships, dependencies, and workflows. The knowledge graph approach stores not just tool descriptions but also understands which tools provide inputs for others, common execution sequences, and proven workflow patterns. This structured representation enables the system to reason about multi-step operations and handle complex nested tool calling scenarios that would otherwise fail. Viewing MCP server as intelligent orchestrator pattern represents a system design where complexity is abstracted behind a simple interface. Rather than exposing hundreds of individual tools, the MCP server presents a single natural language interface while internally managing tool selection, workflow planning, and execution. This creates a powerful facade that makes enterprise software systems accessible to AI agents without overwhelming them. Hierarchical orchestration patterns emerge also when systems scale to multiple MCP servers, creating recursive orchestration needs. External agents must also select between different MCP servers (each potentially managing hundreds of tools), leading to the same tool selection challenges at a higher level.

AI adoption often begins with enthusiasm and experimentation, but without coordination it can quickly become fragmented. Separate tools, models, and data pipelines make it difficult for enterprises to scale efficiently or maintain consistent governance. Recognizing these challenges, Intel Corporation IT set out to build a unified foundation that could scale securely while delivering measurable business value.     To meet that need, we developed One AI (1AI), a consolidated agentic AI platform that brings multiple chatbot interfaces and language models together within a single, modular framework. Built on Intel architecture and open-source tools, One AI enables business units to deploy specialized agents for specific use cases under consistent, centralized oversight. The unified interface improves the user experience and simplifies maintenance, helping reduce the complexity that often accompanies distributed AI projects.      Working with Intel’s Sales and Marketing Group, we were able to identify high-value use cases and implemented a centralized framework that eliminated redundancy and improved response times and overall quality. This collaboration demonstrated how a well-governed, agentic AI platform can accelerate productivity and align technical innovation with business outcomes.     The success of One AI marks a step toward a mature, scalable approach to agentic AI. One that embeds automation within enterprise processes while preserving trust across every layer of the system. As we expand One AI across additional business environments, this model will continue to shape how organizations use AI to improve efficiency and decision quality at scale. 

🔥 We've been battling the same integration challenges for decades. Every platform speaks its own language, requiring custom APIs for every connection, with protocols that constantly shift with each update. There may finally be a path forward. Enter MCP (Model Context Protocol). Think of it this way: 🌐 HTTP became the universal standard that lets humans interact with websites consistently, regardless if you were doing bank transactions or equipment configuration. 🤖 MCP is emerging as the standard that allows AI systems to interface with virtually any platform or tool in a similar consistent way. Major players are already moving - Microsoft is integrating MCP into Copilot Studio, and OpenAI officially adopted it across their platform in March. For any industry dealing with complex system integrations, this represents a fundamental shift. Instead of building custom bridges between every system where every skill or capability needs to be explicitly planned for, we're moving toward a world where AI can seamlessly connect and orchestrate across platforms using this common protocol, with self awareness of capabilities. At CTI, our team is 🚀 hands-on exploring MCP's potential and building the expertise to deploy it strategically. We're not just watching from the sidelines—we're actively integrating it into projects and leveraging this technology, even as it still evolves into a mature standard. The implications extend far beyond any single industry. This could reshape how we think about system architecture, reduce integration costs, and unlock capabilities we haven't even imagined yet. I'm 💡curious to hear who else is exploring MCP and what potential you're finding. I'm confident that this is a pivotal moment worth paying attention to. #MCP #Integration #AI #Innovation #TechLeadership #AVTweeps #MicrosoftCopilot

Software systems work best when they’re connected to each other. For years, incumbents use deep integrations as a competitive moat. But AI upends this dynamic. A few of our portfolio companies are starting to develop integrations with AI in a matter of hours, completely upending the two or three quarter timeframes of classic enterprise integration development. This enables two important impacts to the sales cycle. First, the integration a customer desires can be built during the sales cycle, demonstrating the startup’s technical agility. And second, within a few quarters, a startup can have a vast number of integrations demonstrating breadth, establishing credibility & nullifying sales objections. Developers working with coding agents drafting a PRD, developing integration tests, and then asking the AI to iterate until it succeeds. In the medium term, companies that are able to use AI to write integrations are better equipped for agent-to-agent communications. If one AI can develop an integration into another AI, well, then those two systems can talk together seamlessly irrespective of changes within API specification, network communication challenges, state management issues, historical problems that have plagued integration. This sets the startups up well for the next generation of protocols, including model context protocol and agent2agent. AI generation of those interfaces further separates startups from incumbents. With fast integrations, startups can shorten sales cycles and be among the first to build and deploy a complete AI architecture.

We wanted agents to run real workflows, not just suggest them. That means giving them a way to work with tools like Intercom, HubSpot, GitLab, and Notion, without hardcoded logic or building flows from scratch. So instead of building everything from scratch, we built a shared integration layer. Agents describe what needs to happen. MCP receives that request, understands the structure and context, and determines which tool to invoke. Then Membrane takes over, mapping the action to the right connector, handling authentication, retries, and executing the operation safely across apps like GitHub, HubSpot, or Notion. No per-customer logic. No “if app = X” branches. One Integration App layer that can support hundreds of apps, and multiple agent architectures. This is how we run real actions across tools, use cases, and AI-driven products. Here’s the flow: 👉 https://lnkd.in/e--yck3n

The real challenge in AI today isn’t just building an agent—it’s scaling it reliably in production. An AI agent that works in a demo often breaks when handling large, real-world workloads. Why? Because scaling requires a layered architecture with multiple interdependent components. Here’s a breakdown of the 8 essential building blocks for scalable AI agents: 𝟭. 𝗔𝗴𝗲𝗻𝘁𝗶𝗰 𝗙𝗿𝗮𝗺𝗲𝘄𝗼𝗿𝗸𝘀 Frameworks like LangGraph (scalable task graphs), CrewAI (role-based agents), and Autogen (multi-agent workflows) provide the backbone for orchestrating complex tasks. ADK and LlamaIndex help stitch together knowledge and actions. 𝟮. 𝗧𝗼𝗼𝗹 𝗜𝗻𝘁𝗲𝗴𝗿𝗮𝘁𝗶𝗼𝗻 Agents don’t operate in isolation. They must plug into the real world:  • Third-party APIs for search, code, databases.  • OpenAI Functions & Tool Calling for structured execution.  • MCP (Model Context Protocol) for chaining tools consistently. 𝟯. 𝗠𝗲𝗺𝗼𝗿𝘆 𝗦𝘆𝘀𝘁𝗲𝗺𝘀 Memory is what turns a chatbot into an evolving agent.  • Short-term memory: Zep, MemGPT.  • Long-term memory: Vector DBs (Pinecone, Weaviate), Letta.  • Hybrid memory: Combined recall + contextual reasoning.  • This ensures agents “remember” past interactions while scaling across sessions. 𝟰. 𝗥𝗲𝗮𝘀𝗼𝗻𝗶𝗻𝗴 𝗙𝗿𝗮𝗺𝗲𝘄𝗼𝗿𝗸𝘀 Raw LLM outputs aren’t enough. Reasoning structures enable planning and self-correction:  • ReAct (reason + act)  • Reflexion (self-feedback)  • Plan-and-Solve / Tree of Thought These frameworks help agents adapt to dynamic tasks instead of producing static responses. 𝟱. 𝗞𝗻𝗼𝘄𝗹𝗲𝗱𝗴𝗲 𝗕𝗮𝘀𝗲 Scalable agents need a grounding knowledge system:  • Vector DBs: Pinecone, Weaviate.  • Knowledge Graphs: Neo4j.  • Hybrid search models that blend semantic retrieval with structured reasoning. 𝟲. 𝗘𝘅𝗲𝗰𝘂𝘁𝗶𝗼𝗻 𝗘𝗻𝗴𝗶𝗻𝗲 This is the “operations layer” of an agent:  • Task control, retries, async ops.  • Latency optimization and parallel execution.  • Scaling and monitoring with platforms like Helicone. 𝟳. 𝗠𝗼𝗻𝗶𝘁𝗼𝗿𝗶𝗻𝗴 & 𝗚𝗼𝘃𝗲𝗿𝗻𝗮𝗻𝗰𝗲 No enterprise system is complete without observability:  • Langfuse, Helicone for token tracking, error monitoring, and usage analytics.  • Permissions, filters, and compliance to meet enterprise-grade requirements. 𝟴. 𝗗𝗲𝗽𝗹𝗼𝘆𝗺𝗲𝗻𝘁 & 𝗜𝗻𝘁𝗲𝗿𝗳𝗮𝗰𝗲𝘀 Agents must meet users where they work:  • Interfaces: Chat UI, Slack, dashboards.  • Cloud-native deployment: Docker + Kubernetes for resilience and scalability. Takeaway: Scaling AI agents is not about picking the “best LLM.” It’s about assembling the right stack of frameworks, memory, governance, and deployment pipelines—each acting as a building block in a larger system. As enterprises adopt agentic AI, the winners will be those who build with scalability in mind from day one. Question for you: When you think about scaling AI agents in your org, which area feels like the hardest gap—Memory Systems, Governance, or Execution Engines?